Victim, who?

The #MeToo movement has brought to the forefront more than a million stories from all over the world.  Here’s me jumping on the bandwagon a bit late with my story – Me and my mom were waiting for a cab after a late night movie in my home city when a random dude on a cycle grabbed me and drove away. My immediate reaction was to pick up a stone and throw it at him and then run behind him like a crazy person to catch him at which point my mom decided to unfreeze and ran behind me to stop me. The last bit was comical to be honest but on a serious note, I’m kind of proud of how we handled it.  I wasn’t embarrassed. My mom didn’t think it was because I was dressed in a skirt and people didn’t think it was because we were out late. At least based on my news feed, that’s not how this usually works.

If I rob a bank today and say that it’s because they were open late and advertised their money, would you blame the bank? Victim blaming is so illogical that I won’t even get into it. What I’ve been thinking about more recently is what the random dude even achieved by grabbing me. Empirical evidence demonstrates that “sexist, patriarchal and sexually hostile attitudes” are main causes of rape culture.

If you’re an Indian, chances are that you received no formal sex education and ‘consent’ wasn’t even in your vocabulary till you graduated high school. We grew up watching Salman Khan stalking his heroines on screen and assaulting his girl friends off screen. I can safely say that 80% of Telugu movies I’ve seen as a child were problematic to say the least : you can’t beat the hero? Let’s rape the sister.  You’ve just been raped? Let’s beg the rapist to marry you or kill yourself. Clearly, rape is barely about sex at all. It’s also not irrational or uncontrollable. It’s about power and degradation. This terrible promotion of violent masculinity and female purity unfortunately runs deep in our society even today.

I’ve long considered myself a feminist. I joke that Sheryl Sandberg’s ghost is a constant voice in my head telling me to lean in all the time. But it’s hard to not acknowledge my own privilege and realize that feminism tends to represent the more liberal educated women and it’s probably meaningless unless we also actively advocate for women whose realities do not look or feel like our own.

Do you remember Mukesh Singh’s supreme court lawyer Manohar Lal Sharma saying he will burn his daughter if she brought dishonor?  The fact that he could say such atrocious things even as the whole nation protested for the “India’s Daughter” baffled us all so we put our thumbs to good use on the mighty Twitter that resulted in a petition against him. But how do you even respond to little kids saying similar things in this video?  This leads me to believe that a death penalty will not change much. Changing this mind set that sex some how glorifies a man and brings shame to a woman is more vital. So may be start with disrupting the stereotype of power imbalance?

  • Don’t victimize the girl – It shouldn’t be cute and acceptable when a girl hits a guy and atrocious the other way round. The guy isn’t taking advantage of the girl if it’s consensual. No to different curfew times and no to painting the girl as in need of protection!
  •  Re-examine masculinity – Why won’t the macho man who refuses to talk about feelings, takes without asking and thinks violence is cool go extinct already? Its not masculinity, it’s just bad manners and/or toxic.

Related Articles
[1] Interviewing 100 convicted rapists
[2]  Facebook’s sexual harassment policies
[3] The Story Behind BITS Pilani’s Girls’ Hostel Curfew Removal
[4] India’s Daughter Documentary

Coded Map Reduce

As a brief background on map reduce: it is a distributed computing model that enables processing of large data sets on distributed commodity servers. Within a map reduce, data is divided into several map tasks that map the data into intermediate key value pairs. These key value pairs are shuffled and sent to reducers where they are reduced to give the final output. Even though this is very efficient, it is shown that 33% of the entire processing time is taken up by shuffling. To combat this, many techniques such as combiners, cache memories have been developed. Coded map reduce is another attempt at reducing the time taken to shuffle data and reduce the communication load.

To demonstrate the need for and improvements in Coded Map Reduce, the paper considers a simple problem of word counting. The job is to count the number of times A, B, C, D words that occur in Chapters 1-12 using K=6 servers. First, for map tasks assignment, a master controller assigns each file to pK servers for mapping where p is a fraction of K. In a naive map reduce, pK = 1 and assignments could be done as shown below.


On the other hand, if we use uncoded map reduce by increasing redundancy, i.e mapping each file in more than one servers, the paper shows that the communication load can be decreased. Consider a naive mapper that assigns the files as shown in the below picture.

Capture.JPGAfter map assignment, all the servers start to map the files assigned to them and generate key value pairs similar to (Wi, vin)[n] denoting (word, count)[file]. In the original map reduce, all servers continue to map till all N sub-files have been mapped at one server. However, a key design parameter r modifies this functioning. Now, the mappers continue till rK of the servers have mapped a sub file after which the rest are aborted. If A is the subset of indices of the servers a sub-file is assigned to, intuitively this means that the new $latex An  \subset A $. This r defines the redundancy of map tasks and the delay at map task is proportional to r. Following the map tasks, we begin with reduce tasks. The goal of this step is to evaluate the final counts. Let us assume that one reducer is responsible for each key. Hence we need Q keys to evaluate Q words at K servers. We also represent the reducer distribution D as the set of keys being evaluated at a particular server. The division of these keys should follow that no two reducers should evaluate the same key. So if a particular reducer is evaluating the key Wq, it needs the corresponding values from all N files. The reducer already has v_in keys but requires $latex vjn$ where $latex i \neq j $. To accomplish this, we need a data shuffling scheme. Incorporating a simple scheme, at every time slot, one of the K servers constructs a message that is a function of all the keys and sends it to all other servers. The communication load is therefore the average number of intercommunication time slots required for that scheme. Given this setting, for rK = pK = 1, we have $latex L_mapreduce =36 $. For coded mapreduce using rk=pk=2, $latex L_uncoded = 24$ for a generic scheme without optimizing. This is because in conventional map reduce with no redundancy, each server has (N/K = 3) files and requires keys from the other 9 files but redundant map reduce has (rN/k =6) files and requires keys only from the other 6 files. This is further reduced using coded map reduce with redundancy. Consider the following assignment scheme that is used in coded map reduce:

Coded Map Reduce.jpg

In this coded map reduce, instead of using naive assignment of files to map tasks, they are assigned such that each file is assigned to two servers and any two servers have two chapters in common. Once (word, count)[file] pairs are obtained, coded map reduce does any additional step creating coded key value pairs. For example, server adds (B, 25)[3] and (C, 15)[1] to create (BC, 40)[3,1]. In general, these coded key value pairs of the format (Wj, counti+countj)[ni,nj]. The reduce tasks are similar to before but performs a decoding operation first to compute count_i first. Upon calculating, we can see that this needs a load of 12 (by each server accessing the shared link 3 times). This is a 66% reduction in communication load from the conventional map reduce and a 50% less than that of an uncoded version.

The first question that comes to mind now is how the decrease in communication load effects the increased processing time due to redundancy. The paper therefore derives the expected map processing time and shows the trade off between communication load and processing time with repetitive mappings. The results are summarized in the pictures below:



[1] “Coded MapReduce”, Songze Li, Mohammad Ali Maddah-Ali, and A. Salman Avestimehr, arXiv:1512.01625


Backpropagation For Dummies

I’ve coded the backpropagation algorithm for the second time last week and am amazed at how easy it is to make mistakes and how accustomed I am to using libraries. I’m positive at least 95% of Machine Learning students would have spent a night debugging this algorithm so I’m going to try and make it a little bit easier to derive and understand it.

Intuitively, you’re trying to propagate loss back to every layer so that it can be minimized. So to train a simple neural network, you will send one input, compute your loss based on the given output, back propagate your loss in order to reduce it in the next iteration and repeat till your model converges. We can divide this into 3 questions: How to go from input to output? How to define loss given predicted output and true output? And finally, how to backpropagate the loss?

How to go from input to output? Forward Propagation

Let us consider a simple feedforward neural network with one hidden layer of sigmoid activation and the output layer with softmax activation as shown below.  As we can see, the input feature dimension M = 3 and output feature dimension N = 2. For each layer in the network, a_{i-1} are the inputs and h_i are the outputs.

NeuralNetwork (3)Before we can get from input to output, we need to initialize our weights and biases. The main function of biases is only to shift the input. For example, for a simple sigmoid function, the crossover from y=0 to y=1 happens at x=0. If we want this crossover to happen at x=1/2, we could use a bias: to shift the output. Hence biases can simply be initialized to zero. However, weights cannot be. This is because it would result in 0 outputs at all neurons. We cannot also initialize them with the same weights and you should think about why not. A good and simple initialization of weights can be done by sampling uniformly in [-b, b] where b is computed using the following formula:

b = \frac{\sqrt{6}}{\sqrt{H_k + H_{k-1}}}               (1)

where H_k is the size of h_k

Now the output can be computed by simply propagating the input from one layer to another. For going from one layer’s output to the next layer’s input h_i -> a_i, the features are scaled and shifted using W and b respectively using equation 2.

a_i = (W^i)^T * h_i + B^i                                             (2)

h_{i+1} = g(a_i)                                                           (3)

For going from pre-activations a_i to activations h_i, we transform it using the nonlinear activation function in equation 3 where g is either sigmoid or softmax in our example. In the given model, Y , the output is obtained by computing one hot vector from h_3 which is simply placing 1 for the feature with maximum probability and 0s for the rest. In order to judge how good or bad a model is, we first need to identify our loss function.

Loss Function

Two of the most commonly used loss functions are mean squared loss and cross entropy loss.
Mean Squared Loss :

L = \sum_{n=1}^{n=N} (y_n - p_n) ^ 2

Cross Entropy Loss:

L = - \sum_{n=1}^{n=N} y_n  log p_n

For classification networks, we usually prefer to use cross entropy loss and for regression networks, we prefer the mean squared error loss. So for this network, since it is a classification network, we will use the cross entropy loss.

Back Propagation

Back Propagation is used to calculate the contribution of each parameter on the error and correct them so as to reduce error in the next iteration. In our neural network, we want to learn values of W, B such that the error reduces. In order to do this, we compute the gradient of the loss function and propagate it in the following way:

\frac{\partial L_n}{\partial h3_{n}} =- \frac{\partial y_n  log  h3_n }{\partial h3_n} = - \frac{\partial y_n }{\partial h3_{n}}

\frac{\partial h3_n}{\partial a2_{n'}} = \frac{\partial }{\partial a2_{n'}} * \frac{\partial e^{n}}{\partial \sum_{n''} e^{n''}}

When n’ = n,

\frac{\partial h3_n}{\partial a2_{n}} =\frac{\partial e^{n}}{\partial \sum_{n''} e^{n''}} - \frac{\partial e^{2n}}{\partial (\sum_{n''} e^{n''})^2}

\frac{\partial h3_n}{\partial a2_{n}} = h3_n ( 1 - h3_n)

When n’ \neq n,

\frac{\partial h3_n}{\partial a2_{n'}} =\frac{\partial e^{n} e^{n'}}{\partial (\sum_{n''} e^{n''}) ^ 2} =  -h3_{n'} h3_n

Combining both,

\frac{\partial L_n}{\partial a2} = -\frac{y_n}{h3_n} (-\sum_{n' \neq n} h3_n h3_{n'} + h3_n  -  h3_n ^ 2) =

\frac{\partial L_n}{\partial a2}  =  h3_n -y_n 


\frac{\partial L}{\partial a2}  = h3 - y [2 x 1]

Since we know that a2 = W2 ^T h_2 + b2, computing W and b gradients from here should be relatively straight forward as shown below:

\frac{\partial a2}{\partial b2} = 1

\frac{\partial a2}{\partial W2} = h_2 ^ T 

Hence we get gradients as :

\frac{\partial L}{\partial b2} =h3 - y  [2 x 1]

\frac{\partial L}{\partial W2} =(h3 - y) X h_2 ^ T [2 x 3]

For computing gradients for the next layer, we first have to propagate it through activations and preactivations like we did before.

\frac{\partial a2}{\partial h2} = W_2 

\frac{\partial h2}{\partial a1} = \frac{\partial}{\partial a1} \frac{1}{1 + e^{-a1}} 

\frac{\partial h2}{\partial a1} = \frac {e^{-a1}}{(1 + e^{-a1})^2}  = h2(1-h2)

Now propograting back to W, b like before, using chain rule, we get:

\frac{\partial L}{\partial b1} =\frac{\partial L}{\partial a2} \frac{\partial a2}{\partial h2} \frac{\partial h2}{\partial a1} \frac{\partial a1}{\partial b1} = W2^T x (y(1-h3)) h2(1-h2)  [3 x 1]

\frac{\partial L}{\partial W1} = \frac{\partial L}{\partial a2} \frac{\partial a2}{\partial h2} \frac{\partial h2}{\partial a1} \frac{\partial a1}{\partial W1}

\frac{\partial L}{\partial W1} = W2^T x (h3 - y) h2(1-h2) x h_1^T  [3 x 3]

Notice that since $latex \frac{\partial ai}{\partial bi} $ is always one, $latex \frac{\partial ai}{\partial Wi} $ can be written as $latex \frac{\partial Li}{\partial bi} * hi ^ T$.

Using these equations, your back propagation algorithm should be <10 lines. 🙂

Pointless Note: Honestly, I’ve really always wanted to write a XYZ for dummies article and thought it was about time.

Guilty Non-Vegetarian

I’m just about 20% done with Sapiens: A Brief History of humankind and find myself quoting the book in 90% of my conversations. It is interesting, provocative and so relevant to just about everything today and I highly recommend you get a copy too. The central theme of the book, as the title suggests, is about evolution. How we as a species changed to survive and pretty much aced the chart of evolutionary successes. The comparisons between evolutionary success and individual suffering are perhaps the most relevant to this article.

He says the basic aim of evolution is to survive and multiply. As humans triumphed, from a strictly evolutionary perspective, so did the domesticated animals and agricultural crops: they multiplied by millions all thanks to us but unlike the crops, the animals didn’t have it all good.  A couple of years back, my brother bought a puppy, Hugo. As it turns out, it is a norm in America to neuter dogs/cut off their tails. Honestly, the first image that popped into my head upon hearing that is this scene from Season 3, Game of Thrones where Varys reveals the Sorcerer who mutilated him in a box, tied and gagged. I guess it is hard to see how wrong something is when it becomes common place. But read this: (Paraphrased from Sapiens)

“To ensure that pigs can’t run away, farmers in northern New Guinea slice off a chunk of each pig’s nose or gouge out pigs’ eyes so they cannot find food or even find their way around which makes them completely dependent. The dairy industry has its own ways of forcing animals to do its will. Cows, goats, and sheep produce milk only after giving birth as long as the youngsters are sucking. One common method throughout history was to simply slaughter the calves and kids after birth, milk the mother for all she was worth and get her pregnant again. In an industrial meat farm, a calf is separated from its mother after birth, locked inside a tiny cage not much bigger than itself where it spends about 4 months after which it is slaughtered. It is not allowed to play with other calves or even walk: all so that its muscles will not grow strong. Soft muscles mean a soft and juicy steak. ” YIKES

I’m positive dinosaurs are celebrating right now for being extinct. I have these bouts of immense guilt for being a non-vegetarian every now and then. I remember this time in high school, I turned vegetarian for about 2 years upon watching a truly disturbing video. Believe me, it is hard! Really really really hard.  However, I honestly believe that once we get through racism, sexism, corruption and all the other things that affect humans directly, animal cruelty will become the center of everyone’s attention. Until then, I guess I’m like this mob-wife, happy and content with the money that it entails but cut-off from the reality of all the cruelty!


Orange monster’s election as the President of United States, unexpected and unnecessary, has left me dumbfounded. How could this happen? I’ve obsessively watched all election coverage over the past 18 months. I’ve read random books about the Clintons and even attempted to read a few of Trump’s. I’ve watched the world unify against him : Beyonce, Humans of New York, news papers and television shows, Silicon Valley, Wall Street and even the Kardashians. How was that not enough?

Admittedly, I have very little political knowledge. Let’s actually go ahead and say it’s nada. The only other political campaigns that I have even remotely followed are as follows: 2008 Obama’s campaign for president, Arvind Kejriwal’s campaign for chief minister of Delhi and Narendra Modi’s campaign for Prime Minister of India. All three gathered insane attention from social media : mostly positive. I remember people gathering in huge numbers for candle light marches in support of Arvind Kejriwal all through out the country. I remember waking up to thousands of Modi Sarkaar memes and messages all over the internet. I remember watching Obama’s Yes We Can speech in English class suddenly hoping I would get to vote for him. In my opinion what got them elected are their positive campaigns on social media, excellent oratorial skills and hopeful messages.

On the other hand, we have a racist, sexist, xenophobic 70 year old pervert who uses words like bigly and rallies against everything sane. A year back, I would have compared him to Rahul Gandhi who single handedly destroyed the Congress Party. I couldn’t go on Youtube without finding a thousand new videos talking about what’s wrong with Trump. My Facebook posts were filled with hate for him. I mean, you’ve got to be a special kind of wrong to break Brandon Stanton of Humans of New York from character. All this led me to believe that Clinton would win by a landslide. How could she not? Secretary of State, two time senator of New York, former first lady of the United States vs the nonsensical Donald Duck. Yet, here we are, hoping to wake up from this nightmare of a result.

How did it even get here? 50% of Americans thought Donald Trump was a better candidate. 50%. Last night, being the computers student that I am, I wondered if I was living in an internet bubble that I created for myself where I was exposed only to pro-Hillary news due to my obvious hatred for Trump. I checked, believe me, while the recommended videos are highly skewed, media in general is truly pro Hillary so it’s not just me who thinks he is unfit so how did this happen? I hear so many people say that it was a protest vote. What does that even mean? Would you protest against rise in medical fees by letting a drunk driver operate on you? What does that even mean?! I hear the others talk about how fucked up the bipartisanship system in US is, well, yes it is. I hear a few others say that the Wiki Leaks and FBI director’s revelation led to Clinton’s defeat. The rest think USA is not ready for a female president. I know the far-right populists are rising in the polls all over the world but I still assumed he wouldn’t be getting as many votes as he did. I mean, have you heard him speak?

It is very easy to think that all those 50% of the people are uneducated white supremacists and it would definitely be a gross generalization. I’m positive most of them are normal people with kids, jobs and may be even some common sense. So why? How are they okay with watching him glorify sexual assault or even be as ill prepared as he is for everything? Ugh I realize this post has more questions than answers but I’m still unable to process it. I can only conclude by saying that I refuse to be dispassionate. The only good thing that has come out of this election for me is how inspired I am about so many issues today and how aware I am of the bubble that I live in. I sincerely hope there’s a girl somewhere watching this mother load of shit unload on to this world, deciding to break the damn glass ceiling once and for all. I hope it will happen sooner rather than later!

TV Shows That Shaped Me

There is nothing I love more than watching TV shows. Name it, and I have probably already seen it. But my life is finally catching up and I simply don’t have any time. So here I am, making a list of TV shows that changed my life to keep myself from starting another TV show. Promise me though, to not judge because my list is not very hip and is definitely not a hardcore fanatic list. It is silly but it is what it is!

Sex and The City :  Aw I can hear you silently stereotyping me and putting me in a box of naivety. Say what you will but this show was well beyond its years and so impressionable in my opinion. First female-centric show, bold and iconoclastic; revolving around four inseparable friends in New York city! For me, the brilliance of the show had nothing to do with their clothes, shoes or even sex. I simply adored the four smart and funny women, especially their friendship. They were real and unlike any women I have ever seen on TV before. Not naive or cunning, supportive of each other through thick and thin and a breath of fresh air from the typical Indian female characters that I was accustomed to watching. They were also independent and had no interest in marrying someone for the sake of marrying. I know shine theory is a popular term today but this show thought it through a few decades back. I mean, have you seen that scene where they sing “I am woman”? Easily makes my day every time I catch it on TV.

F.R.I.E.N.D.S : The show revolves around six friends living in New York city as they live their lives. But whom am I kidding, who needs an introduction to this show? It changed my life by simply being so hilarious and sweet. By now, I feel that they should start paying me for all the promotions I do for them. There aren’t many shows that I can watch twice unless it’s the only thing on TV when I’m home hiding from the world. But Friends is an exception. I weep every time I watch the last episode and make it a point to watch an episode after every Khaleed Hosseini novel. I can go on forever about how much I love this show but I’m going to try and stop before I bore you all. But let me tell you that I will be personally offended if you haven’t seen it yet. So if you haven’t, DO IT NOW! The world can wait.

Castle : Castle is one of the first shows I have ever watched as a kid, probably my first American TV show. It revolves around Kate Beckett, an  NYPD officer and Richard Castle, a famous novelist solving murder mysteries. I’m seeing a pattern here but I love TV shows with strong (AND like-able) female characters! I’m so glad there are more and more of them that I love today but that was hardly the case when I was in high school. I can sum up Indian Television back then (actually relevant even today) for you in one line. Evil mother in law or jealous ex girl friend making the life of a hero and a naive as hell heroine’s life a mess. There, I just saved 10 years of your life. So when characters like Kate Beckett came up, it was hard not to adore them. I also love the Castle-Beckett duo, they’re simply too cute; the amazing supporting cast and the positive family dynamic.

Shark Tank : All my respectable reading and TV habits are a result of my elder brother without who I probably would have never read a non-fiction book or watched this show. This show is about a panel of investors who invest their money in budding entrepreneurs. After watching about 4 seasons, they become a sort of voice in your head which is great. More than anything else, it has taught me that execution is almost always more important than the idea itself. As a part of the generation that wants to ‘make it big’ with one good idea for an app, it is a good reality check.

Ellen DeGeneres Show: Since I already mentioned that I waste my precious time watching celebrity interviews, you should know who started this madness: Ellen. I stumbled upon her channel on Youtube and have been hooked since. She’s a huge ball of positivity: I haven’t heard her say a mean or unnecessary thing yet. She is never judgmental and never makes jokes that are offensive to anyone. She hasn’t even made a joke on Donald Trump. Is that even possible for television show hosts? In spite of that, she manages to be funny which is shocking and commendable. All this adoration without even being a witness to a time when she changed the conversation about the LGBT community, poof! Not only do I watch her videos everyday morning during breakfast, she also introduced me to a whole list of other television hosts like Trevor Noah, Jimmy Fallon, John Oliver, Jimmy Kimmel and Stephen Colbert. Thank you for getting me out of bed everyday, you all. Thank you also for keeping me up to date with everything that happens in America and being partly responsible for my obsession with this election.

Other amazing TV shows that you must watch before you get too busy in life include : Modern Family, Grey’s Anatomy (S1 – S5), Narcos, Silicon Valley, Game of Thrones and Sherlock. So there you go, happy weekend everyone!

Installing Ubuntu 14.04 on Lenovo Yoga 710

After spending an entire Sunday trying to install Ubuntu Gnome 14.04 on my Lenovo Yoga 710 (i5 7400, 8GB RAM and 256 SSD with NVidia 940MX) I have finally arrived at the solution. Hope this helps some body else put their Sunday to better use.

Step 1. Install ISO File

Install  Ubuntu Gnome or Ubuntu LTS depending on which you prefer.

Step 2.  Create a live bootable usb using rufus.

You can also use Windows USB/DVD tool to make a live bootable usb from your windows machine but that didn’t really work for me. The easiest way in my opinion is to use Rufus USB installer.

  • Download Rufus from here and select rufus.exe.
  • Select the .iso file and your usb stick to make a live bootable usb stick.

Follow this link if you have any trouble with rufus

Step 3.

Select Restart Now from advanced start up options on your windows 10.


Choose use a device from advanced startup options and then select your usb disk.

Step 4: Boot into Ubuntu

Here comes the deviation from the standard Ubuntu installation. Setting nomodeset, changing bios boot mode to legacy, diableing secure boot and fast boot, installing different or more stable versions are some of the things didn’t work for me. What seemed to be causing the system crash for me was the ACPI. Since disabling it completely is not advisable as it is a standard for power management, we can set acpi to noirq.


At this point, you should ideally see the grub window. Take your selection to install ubuntu and type the character e to edit boot entries. Go to the line that starts with linux, this is the line that tells grub which kernel to boot and the parameters to boot with. Add acpi=noirq to the end of the line containing linux. For example :

linux /vmlinuz root=UUID=$diskuuid loop=/ubuntu/disks/root.disk preseed/file=/ubuntu/install/preseed.cfg wubi-diskimage ro quiet splash acpi=noirq

Step 5: Follow usual installation steps to install Ubuntu

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Step 6 :

In order to make the changes we made to booting permanent, open the terminal and edit the grub as follows.

sudo vim /etc/default/grub

Modify the following line to include the changes to acpi.

GRUB_CMDLINE_LINUX_DEFAULT=”acpi=noirq quiet splash”

and then reboot to apply the changes.

sudo update-grub
sudo reboot

Well, so there’s that! My setup seems to be stable for now but I’m still trying to get my touchscreen, nvidia drivers and fingerprint scanner (which I have grown quite fond of!!) to work. I will update this post if and when i find fixes/alternatives for the same. Do let me know if you can get it to work without compromising acpi.

Incase the above steps didn’t work you, may be you should check the following posts out. They didn’t work for me but you never know!

1. How to install ubuntu alongside windows 10

2. Install ubuntu on a computer with Nvidia Geforce

3. Set nomodeset to stop your video drivers from loading

Screw Beauty Standards

I’ve always emphasized the role of films and celebrities in shaping norms in the society and it is unfortunate that so many celebrities don’t realize the power and influence that they possess over all of us. Do you remember Kate Moss telling us that nothing tastes as good as skinny feels? Then there’s Parineeti Chopra who flaunted her skinny body after a “makeover” and told us all that she lost excuses and got results. She was by no standards fat after the release of her first film but she had to shed more weight to fit into Bollywood measurements. I get it, you’ve got to do what you’ve got to do to stay in the game and I doubt it’s fun to watch the tabloids repeatedly write about your weight or your unibrow and not about the parineetu-3_1449725539excellent work that you’re doing. What I completely disapprove of, though, is portraying this makeover as if she got healthy because no, she just got skinny. Today, as a result of this mindless starvation and ridiculous beauty standards, all I see on TV are extremely skinny girls, photoshopped to insanity, telling us all that that’s how an average girl looks like.

On the other hand, there are confident celebrities who are genuine, smart and so very candid that I’ve grown to love and admire. I love Serena Williams for putting up the untouched version of her People magazine cover. I love Jennifer Lawrence for refusing to lose weight for her role in Hunger Games. She said, “I’m never going to starve myself for a part … I don’t want little girls to be like, ‘Oh, I want to look like Katniss, so I’m going to skip dinner.’” Yet another reason to love her is her regular insistence that Hollywood be held accountable for the kinds of messages it conveys about size, beauty, and femininity. Now there’s a celebrity worthy of her fame.

In a recent Buzzfeed article, Sonam Kapoor said, ‘Eventually, I didn’t even need the tabloids to point out my flaws – I could look at myself on camera monitors and predict what would be criticised.” It is so easy to hate yourself in this era of digital photography and social media it seems like, but if Sonam Kapoor can’t feel good about how she looks, I doubt average individuals like us even stand a chance. I mean, she’s GORGEOUS! How did we even get this deep inside the rabbit hole?

While I am so inspired by this article that she posted, I also know that there will be a plethora of comments blaming her for double standards, asking her to stop posing for covers. Telling her she is a part of the problem. But is she, really? I mean, let’s consider this. As a girl in the 21st century, we constantly put ourselves through immense pain to remove body hair. Can you really find even one girl who enjoys it? I’m sure I don’t, doesn’t mean I won’t. We’ve reached a point in society where it’s expected from all of us, it’s the norm. We are constantly pressurized to fulfil and maintain a set of beauty standards that is not just time taking and annoying but also so damn painful. Maybe we need more insiders telling us how things really are to break this bubble once and for all. Did you know Victoria Beckham eats only spinach and salt at restaurants? Did you know that Jennifer Garner couldn’t breathe in her best-dressed dress and had a panic attack? Did you know that 40% of the models today are suffering from eating disorders?

I simply can’t get over how ridiculous all of this is. I sometimes wonder who decided that eyebrows shaped a particular way are prettier than the other. I hope whoever that is, is burning in hell right now for centuries of torture all the women have to go through, thanks to that single revelation. Now, don’t even get me started on heels. We recently had a career fair at my college where girls dressed up to perfection in their blazers, pant suits, and way-too-high heels. Believe me, I know what a confidence booster heels are but after about 15 minutes, I’m usually ready to pass out on the floor. Can you imagine impressing recruiters or even just having enough patience to listen to them while you’re suffering like that? Maybe girls get used to this at some point because as it turns out, it is an unofficial dress code for executive women today. One manager actually told all of us at a professional women seminar that the trick is to put on heels right before you go into a meeting and have a pair of sandals at your desk for other times. Well, no wonder women don’t take up executive roles (Damn you heels. :P)

Don’t get me wrong,  I’m not against looking pretty or dressing up. I would love to one-day master the art of putting on makeup too (I don’t think that’s happening and not for the lack of trying, believe me!) I don’t wish to be obese or unhealthily skinny and I definitely don’t want to grow a moustache or a unibrow. If looking your best allows you to be more confident and present yourself in a way that would emphasize you, why not. But I hope I don’t grow into this person who feels guilty for eating a chocolate or puts on 6-inch heels to work. I hope I don’t ever find the need to starve myself or live on pineapples. I hope I can retain my common sense and I hope that my confidence is not linked to my waist line or somebody else’s perception of what I should look/eat/be like. I hope I remember that what’s really important is who you are…and chocolate. Screw the beauty standards!

Cool articles/videos/campaigns that I enjoyed FYI :
–  I didn’t wake up like this – Sonam Kapoor
–  Jennifer Garner’s dress causes panic attack
–  Dove Real Beauty Campaign
–  Impractical Standards of Beauty


United the @$#*ing States

Culture shock is inevitable when you’re moving to a new place. You like some things, you hate some things. Mostly hate, if you’re a slob (read Indian) like me in the United States. The weird thing, though, is that I didn’t expect to be shocked, even though I knew I was moving to a different continent because I grew up watching American movies and TV shows. Since the United States more or less dictates entertainment to the entire world, I thought there was nothing here that could surprise me. Oh boy, was I wrong!

It’s not the major things that caught me off guard. It’s the little things that blew my mind. While talking to my brother about the weather in Pittsburgh, he very sweetly told me to start talking in Fahrenheit terms. Let me take a minute here to address my issues with this. Firstly, why does the USA follow the Imperial system when the entire planet works with the metric system? I guess it doesn’t matter if it’s Fahrenheit or Celsius, they must have flipped a coin and picked one. But what the hell are pounds and miles? SI units are simple and make life easy. 1 kilometer, abbreviated as 1km is 1000 meters. 1 liter, abbreviated as 1l is 1000 milliliters(ml). 1 pound abbreviated as lb for god knows why is what? 1 mile is what again? In my humble opinion, imperial system is so very silly and  I can’t understand why the Americans didn’t switch to the metric system already.

I also can’t wrap my head around how money works in this country. Here are a couple of examples to prove my point:

  1. 1 can of coke = $1 but 12 cans of coke = $3 ?!
    Things are cheaper if you buy bigger quantities in India as well but I often made sense of that by assuming they saved on the packaging and since that doesn’t apply here, I simply don’t get it.
  2. T-Mobile gives me $60 worth of free rides every month (+occasional free food) for a $20 per month subscription. Don’t ask me why.
  3. On my first day to work, I put in a $10 note as I boarded the bus and waited for the change to come out while the driver awkwardly stared at me. Turns out, the damn machine won’t spit out any change. So the next time, I carefully picked out $2.25 change and walked to a vending machine only to realize that it doesn’t take any change. I can’t think of a good reason why they work in polar opposite ways, if not to traumatize the new Indian kid (Okay I’m being a little overdramatic here)
  4. I’ve known this before coming here but why doesn’t the price tag include tax? I never know how much I spent untill I get to the cashier and although it’s making my mental math quite good, I don’t like it one bit.

All said and done, what Indian students have the most trouble with when they come to the USA is the sheer amount of walking that they have to do every day and the lack of good food, and I am no exception. I am as shocked as you are while typing this but I genuinely, truly and completely miss our arrogant and entitled auto-wallahs. I wore one of those trackers to work one day and turns out, I walk about 4 miles every single day. Can you believe that? 4 miles! (For all you people from normal countries, that’s 6.4 km hehe) I can only hope that I will survive the winter here. If I don’t starve by then, that is.

Like every average Indian student, I can cook three things. Maggi noodles, instant oats and scrambled eggs. Turns out, you can’t live on those three forever and since you don’t really enjoy outside food anyway, you are doomed to starvation if you don’t sincerely attempt to cook. As an incoming student, we’ve talked about what laptops to buy, which courses to take, what languages to revise and what not in order to transition into university life. But believe me, the only thing you probably have to work on before boarding your flight is your cooking skills. Once you have done that, you are all set to go. Board your flight, land in Pittsburgh, find me and cook me a meal (pretty please?)

Am I Your Real Friend or Party Friend?

My first month in Bangalore was probably the most depressing 30 days of my 21-year existence. For the first time in my life, I had no group of friends, no familiar faces in the city that I can meet in the evenings. When you’re in college, you don’t need to work hard at making friends, they just kind of happen but when you’re in a new city working at a start up it’s not that simple. The only people I got to interact with every day were my two colleagues (possibly the coolest bosses I will ever have but nevertheless). Wasting time on Youtube got frustrating soon enough and all I wanted was to go back to college. During this period, I met my first-year roommate, we both didn’t spend every minute in college together, in fact, we weren’t even in the same group but she is one of the only ones to whom I volunteer my okay-I-messed-up-big-time stories. She doesn’t undermine my silly problems and I value her opinions. We decided to go on an impromptu trek to Tadaikamanol as a break from hell, spent Valentine’s Day in a camp somewhere in Karnataka eating junk food and gossiping about everything on planet Earth. And just like that, I was back to sanity! Isn’t it sweet how one vacation with a friend can get you from I’m-going-to-kill-myself to I’m-going-to-get-past-this-shit in one day?

Looking back, I wonder why we didn’t hang out as much as we should have. I loved her since day 1 but we didn’t have all the quintessential Manipal experiences together. In spite of that, she knows me more than 90% of the people I hung out with on a daily basis. Maybe most relationships are built out of shared interests, hobbies and for having fun together and not necessarily out of mutual admiration, love, and respect. The truth of the matter is that the friend who you smoked up with every day before class but didn’t know or care about your backlogs was never your real friend. He was your party friend. I don’t know if this is sexist but this clear distinction between real friends and party friends is easier to notice within guy groups. My male friends spend hours together every day since many years now but have never invited each other home or know anything about each others’ families. I doubt if they cried through break ups or discussed rejections and failures while keeping up with their unfortunate macho facade. (I have long maintained that there’s nothing like a girl-girl friendship and I will stand by that forever :P)

Even though it’s easier to notice within guy groups, we can all categorize our friends into those two broad groups. On one side, we have our party friends whom we love hanging out with. We tend to go to parties/dinners together, do stupid things we normally won’t have the courage to do otherwise. We seldom talk about each other or know anything of substance about each other. Even though we probably have the most fun with them and let loose, we probably won’t confide in them when we’re deep in trouble or ask them when we need some honest advice. They don’t necessarily want to know what you think or feel as long as you keep them company. On the other hand, we have our real friends who are closest to our heart. We don’t have to talk to them every day but we can rely on them when we need to and can call them crying when our jeans don’t fit. We can talk about the ugly boring stuff. We don’t necessarily have to do cool and fun things together but we definitely do listen to each other.

But hey, of course, you can and should make party friends, they bring the mental balance to your life that you definitely need. But at a time when we are all so practical, struggling to find out who we are and constantly trying to prove ourselves, it is important to know the difference between a real friend and a party friend. Don’t go around expecting your party friend to understand you or stand up for you and definitely don’t take your real friends for granted. It is important to prioritize, grow up, let go and live easy.