BC BUSINESS
Saved by @Mollyycolllinss

Time for #PapersThatMakeYouGoHmmm! A weekly summary of new ML papers from arXiv that make me think one or more of:

1. That looks useful!
2. That's an interesting approach!
3. A business could be built around this!
4. How did they do that?!

 How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations

https://t.co/GTAaqHRgi1
Validating Label Consistency in NER Data Annotation

https://t.co/eeYBU4AUvT
A two-stage data association approach for 3D Multi-object Tracking

https://t.co/h3LijchIzC
Neural Networks, Artificial Intelligence and the Computational Brain

https://t.co/2jJHGlHYsn
Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation

https://t.co/QhahrfRN5T
Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

https://t.co/gM4vCIMMzW
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

https://t.co/xvyYotmqBG
Discussion of Ensemble Learning under the Era of Deep Learning

https://t.co/eqdMw8WNEU
Do we need to go Deep? Knowledge Tracing with Big Data

https://t.co/Z3EtibSYA3
mt5b3: A Framework for Building AutonomousTraders

https://t.co/w6sBscN3uo
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism

https://t.co/oO0r8IOshz
Classifying Scientific Publications with BERT -- Is Self-Attention a Feature Selection Method?

https://t.co/7pAUbTGfLb
Collision-Free Flocking with a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning

https://t.co/1R4qJ5M0Eq
Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data

https://t.co/S3fQcgbNcK
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers

https://t.co/8sPKuqAPnQ
DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling

https://t.co/UcgL7WDUGv
Noise Learning Based Denoising Autoencoder

https://t.co/hPSClsZTTp
Illuminating the Space of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks

https://t.co/7xawUMSYSW
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search

https://t.co/b6PQNPyDef
Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum

https://t.co/7qA7BomthH
Dissonance Between Human and Machine Understanding

https://t.co/nRBcIDIoSP
A System for Automated Open-Source Threat Intelligence Gathering and Management

https://t.co/zRIE873tMW
Classification of Pedagogical content using conventional machine learning and deep learning model

https://t.co/kFt1Vr11DS
GLocalX -- From Local to Global Explanations of Black Box AI Models

https://t.co/jNEAt3yDei
An Artificial Intelligence based approach to estimating time of arrival and bus occupancy for public transport systems in Africa

https://t.co/oQkFARvo0e
Edge-Featured Graph Attention Network

https://t.co/5jRr0ynqHA
Situation and Behavior Understanding by Trope Detection on Films

https://t.co/2tTVFlj7BM
Meta-Reinforcement Learning for Adaptive Motor Control in Changing Robot Dynamics and Environments

https://t.co/wsMBpdo3zG
Disentangled Recurrent Wasserstein Autoencoder

https://t.co/KNKdFN9RII
GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention

https://t.co/eGlkz92WGB
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

https://t.co/8AC8HngYl1
An attention model to analyse the risk of agitation and urinary tract infections in people with dementia

https://t.co/51FwnK9i5v
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations

https://t.co/4lp4UxSYMV
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions

https://t.co/abJTLSptss
Understanding in Artificial Intelligence

https://t.co/b5kufxxoL5
A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

https://t.co/6TRfUgvv1v
Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation

https://t.co/OYEPMDtf5I
An Empirical Comparison of Deep Learning Models for Knowledge Tracing on Large-Scale Dataset

https://t.co/VtsS1g7pVx
Leveraging AI to optimize website structure discovery during Penetration Testing

https://t.co/tZxbNs99Tu
Is it a great Autonomous FX Trading Strategy or you are just fooling yourself

https://t.co/ted6dt7jBd
Deep Reinforcement Learning for Active High Frequency Trading

https://t.co/C4iR7RNfs2
Studying Catastrophic Forgetting in Neural Ranking Models

https://t.co/GtzumvDLEj
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions

https://t.co/TxUEKByuBX
DeepPayload: Black-box Backdoor Attack on Deep Learning Models through Neural Payload Injection

https://t.co/aNjfrPm8Gd
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning

https://t.co/4lYbSyvDH3
CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation

https://t.co/8tMpbWAu1b
Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction

https://t.co/KzvOxzqexs
Deep Parametric Continuous Convolutional Neural Networks

https://t.co/m3jGJWSnXr
Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition

https://t.co/B3EG8k37SB
GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation

https://t.co/rjx4yjnNQj
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors

https://t.co/fAEPrJmbYB
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

https://t.co/wPPbWpOR36
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

https://t.co/wPPbWpOR36
GeoSim: Photorealistic Image Simulation with Geometry-Aware Composition

https://t.co/AcLGlax0fk
SceneGen: Learning to Generate Realistic Traffic Scenes

https://t.co/JNOxqvAeKB
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems

https://t.co/Tjh0adUiNv
Slot Machines: Discovering Winning Combinations of Random Weights in Neural Networks

https://t.co/XB0dKTws4J
NNStreamer: Efficient and Agile Development of On-Device AI Systems

https://t.co/s6SOklUTsp
AR-based Modern Healthcare: A Review

https://t.co/pcoZB3J3ka
Attention Based Video Summaries of Live Online Zoom Classes

https://t.co/M4ZStoy1AN
When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time

https://t.co/YTIAAWqAcd
On the Verification and Validation of AI Navigation Algorithms

https://t.co/gwBaqOeu4c
Local Navigation and Docking of an Autonomous Robot Mower using Reinforcement Learning and Computer Vision

https://t.co/YHLUQ5SpIa
LIME: Learning Inductive Bias for Primitives of Mathematical Reasoning

https://t.co/rsqGk5fQ8b
Player-AI Interaction: What Neural Network Games Reveal About AI as Play

https://t.co/DIzXf1wuW6
Probabilistic Inference for Learning from Untrusted Sources

https://t.co/PFyTH6jZyp
Teaming up with information agents

https://t.co/fpaMVZlH0k
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study

https://t.co/kStzf3RoTX
Black-box Adversarial Attacks in Autonomous Vehicle Technology

https://t.co/0B2xkavOWt
Motion-Based Handwriting Recognition

https://t.co/lT0ybiyiAl
Affordance-based Reinforcement Learning for Urban Driving

https://t.co/Do6J5eo7j6
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

https://t.co/St76T1uGLq
Responsible AI Challenges in End-to-end Machine Learning

https://t.co/u3drnpONrR
Mining Knowledge Graphs From Incident Reports

https://t.co/Sm1wZA2gYQ
Descriptive AI Ethics: Collecting and Understanding the Public Opinion

https://t.co/3u3By4VxIz
Hostility Detection and Covid-19 Fake News Detection in Social Media

https://t.co/GlrtTAalKE
Robusta: Robust AutoML for Feature Selection via Reinforcement Learning

https://t.co/ihKgihjDfV
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization

https://t.co/rZBlbHStLd
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks

https://t.co/oPTKZbiaHd
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media

https://t.co/aU5g6hXOaS
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

https://t.co/mHFCoQBzCm
ItNet: iterative neural networks with tiny graphs for accurate and efficient anytime prediction

https://t.co/38Ivf3iTys
Adversarial Machine Learning in Text Analysis and Generation

https://t.co/OAaQGy3VD9
Dive into Decision Trees and Forests: A Theoretical Demonstration

https://t.co/YEd3NpQqLc
Stress Testing of Meta-learning Approaches for Few-shot Learning

https://t.co/CP6QDkSUPF
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer

https://t.co/UW74MHAEwV
Analysis of Information Flow Through U-Nets

https://t.co/H7Oay6mTpj
Distilling Interpretable Models into Human-Readable Code

https://t.co/tNph0XpFDT
Invariance, encodings, and generalization: learning identity effects with neural networks

https://t.co/kCJt8bajEP
Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem

https://t.co/cHHRGBip9h
Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?

https://t.co/8Zx7fVfIIp
Explainable Patterns: Going from Findings to Insights to Support Data Analytics Democratization

https://t.co/kuvkN4ZMZu
MPASNET: Motion Prior-Aware Siamese Network for Unsupervised Deep Crowd Segmentation in Video Scenes

https://t.co/ffTvTzz5rZ
LEAF: A Learnable Frontend for Audio Classification

https://t.co/3WaQ8MIC8A
Customer Price Sensitivities in Competitive Automobile Insurance Markets

https://t.co/3nIj6XJNYQ
Pre-training without Natural Images

https://t.co/m1lBQFT9KJ
Arabic Speech Recognition by End-to-End, Modular Systems and Human

https://t.co/6rWnWJvJiR
Ensemble learning and iterative training (ELIT) machine learning: applications towards uncertainty quantification and automated experiment in atom-resolved microscopy

https://t.co/lumhRoaVhf
Influence Estimation for Generative Adversarial Networks

https://t.co/ypMwRmSJqv
Text Line Segmentation for Challenging Handwritten Document Images Using Fully Convolutional Network

https://t.co/R9SiU4pWEy
TensorBNN: Bayesian Inference for Neural Networks using Tensorflow

https://t.co/5wEXw5ECXL
Bayesian Neural Networks for Fast SUSY Predictions

https://t.co/eEjxi4ybgU
Probabilistic Solar Power Forecasting: Long Short-Term Memory Network vs Simpler Approaches

https://t.co/FdvIcYrTJZ
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments

https://t.co/XFZLPMcllL
Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey

https://t.co/WfdmTkw69N
Intelligent Icing Detection Model of Wind Turbine Blades Based on SCADA data

https://t.co/1OjpuDYWcY
Machine learning applications for COVID-19: A state-of-the-art review

https://t.co/9DSd3TfB4D
Implicit Bias of Linear RNNs

https://t.co/IfgXSmwaFb
Open-Domain Conversational Search Assistant with Transformers

https://t.co/8iMUvwb0To
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer

https://t.co/IQKlMjf5Hv
Variational Autoencoders with a Structural Similarity Loss in Time of Flight MRAs

https://t.co/zS4DRvfhI7
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details

https://t.co/EA9GZDwoQO
Cross-domain few-shot learning with unlabelled data

https://t.co/DW7JGPcNwS
Classification of COVID-19 X-ray Images Using a Combination of Deep and Handcrafted Features

https://t.co/JswvK6YlQJ
The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions

https://t.co/Z9cI9d1n2b
A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations

https://t.co/bWrX68Pxl1
Image Denoising using Attention-Residual Convolutional Neural Networks

https://t.co/5aG06Yf2RF
Interpretable Models for Granger Causality Using Self-explaining Neural Networks

https://t.co/DR34Ed1qGd
Continual Deterioration Prediction for Hospitalized COVID-19 Patients

https://t.co/OsBrVfw7kj
Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning

https://t.co/bKhILExhy7
PeerGAN: Generative Adversarial Networks with a Competing Peer Discriminator

https://t.co/944IW7qRsm
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge

https://t.co/jBfVPPcvlY
Optimizing Hyperparameters in CNNs using Bilevel Programming in Time Series Data

https://t.co/Y1hnvcMo2K
Deep Reinforcement Learning Optimizes Graphene Nanopores for Efficient Desalination

https://t.co/r3B4ST2XIX
Handling Non-ignorably Missing Features in Electronic Health Records Data Using Importance-Weighted Autoencoders

https://t.co/MeBl9H7kS5
Does Continual Learning = Catastrophic Forgetting?

https://t.co/jk7tasU0IR
A survey on shape-constraint deep learning for medical image segmentation

https://t.co/OuSpETBxYG
Predicting Pneumonia and Region Detection from X-Ray Images using Deep Neural Network

https://t.co/qpDYzwLw2b
Comparative Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans

https://t.co/Q2nOU79sNv
Deep Learning Models for Calculation of Cardiothoracic Ratio from Chest Radiographs for Assisted Diagnosis of Cardiomegaly

https://t.co/GOe2yLmOWF
Collaboration among Image and Object Level Features for Image Colourisation

https://t.co/bVqv6ZWQTV
Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG

https://t.co/EbrLI7pYe9
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods

https://t.co/DcOl3AbCds
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse Learning

https://t.co/5uPWFcYtzQ
Using Shape to Categorize: Low-Shot Learning with an Explicit Shape Bias

https://t.co/VTDmjNd9QD
Challenges in the application of a mortality prediction model for COVID-19 patients on an Indian cohort

https://t.co/tx5Sc89T4Y
A simple geometric proof for the benefit of depth in ReLU networks

https://t.co/TT25NN3Z3M
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks

https://t.co/CgZhQGXLhC
Deep Learning for Moving Blockage Prediction using Real Millimeter Wave Measurements

https://t.co/8MlUI1ezGy
Discrete Graph Structure Learning for Forecasting Multiple Time Series

https://t.co/MXCAmJNoaX
Heterogeneous Similarity Graph Neural Network on Electronic Health Records

https://t.co/6WhEGluvBZ
Learning from pandemics: using extraordinary events can improve disease now-casting models

https://t.co/7jWkDBEzv6
Physics-Informed Deep Learning for Traffic State Estimation

https://t.co/43BlFlrB6W
Diverse Complexity Measures for Dataset Curation in Self-driving

https://t.co/CZCCyoONoE
Phases of learning dynamics in artificial neural networks: with or without mislabeled data

https://t.co/5hgjP1yYgN
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks

https://t.co/aQbjE43vtA
Visual Analytics approach for finding spatiotemporal patterns from COVID19

https://t.co/pSrj6m6zj5
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

https://t.co/SMWKivSQuU
Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients

https://t.co/XcyMard8Nf
Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution

https://t.co/s5cJeOGptC
MultiBodySync: Multi-Body Segmentation and Motion Estimation via 3D Scan Synchronization

https://t.co/B9E2XzC4zC
Data-driven discovery of multiscale chemical reactions governed by the law of mass action

https://t.co/M2HkwCZBQm
Temporal Clustering of Disorder Events During the COVID-19 Pandemic

https://t.co/5M4wjqpubk
Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling

https://t.co/zYC4jpnXkH
Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data

https://t.co/0B1e6SVE2d
Exponential Kernels with Latency in Hawkes Processes: Applications in Finance

https://t.co/S44KgRfJjs
Deciding What to Learn: A Rate-Distortion Approach

https://t.co/s6TPmGNN5M
Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper

https://t.co/ATqL7pEWkP
The Geometry of Deep Generative Image Models and its Applications

https://t.co/EwSU9rEiaw
Comparisons of Graph Neural Networks on Cancer Classification Leveraging a Joint of Phenotypic and Genetic Features

https://t.co/jcJIQk6gwJ
A Neophyte With AutoML: Evaluating the Promises of Automatic Machine Learning Tools

https://t.co/diHKuYRFdO
Empirical Evaluation of Supervision Signals for Style Transfer Models

https://t.co/0lxQ3WXzaN
Needmining: Designing Digital Support to Elicit Needs from Social Media

https://t.co/xRhSg5WL9I
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function

https://t.co/252Oegv871
Video Summarization Using Deep Neural Networks: A Survey

https://t.co/LffqAz9gVb
Nowcasting Gentrification Using Airbnb Data

https://t.co/tySM5cSpy9
How Shift Equivariance Impacts Metric Learning for Instance Segmentation

https://t.co/OPdhtdDmC2
@threadreaderapp unroll

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Ramin
Ramin
@MalwareRE
As part of our commitment to keeping our customers/community protected & informed, we are releasing a blog that shines light on transition between Stage 1 and 2 of #Solorigate/#SUNBURST campaign, custom Cobalt Strike loaders, post-exploit. artifacts, IOCs: https://t.co/b0ReHMa63u


Here are some highlights:
The missing link between the Solorigate backdoor and the custom #CobaltStrike loaders observed during the #Solorigate is an Image File Execution Options (IFEO) Debugger registry value created for the legitimate process dllhost.exe (ATT&CK ID: T1546.012).

Once the registry value is created, the attackers wait for the occasional execution of dllhost.exe, which might happen naturally on a system. This execution triggers a process launch of wscript.exe configured to run the VBScript file dropped by the SolarWinds backdoor (Stage 1).


The VBScript in turn runs rundll32.exe, activating the Cobalt Strike loader DLL using a clean parent/child process tree completely disconnected from the SolarWinds process. Finally, the VBScript removes the previously created IFEO value to clean up any traces of execution.

On the custom Cobalt Strike Loaders: we identified several second-stage malware, including TEARDROP, Raindrop, and other custom loaders for the Cobalt Strike beacon. During the lateral movement phase, the custom loader DLLs are dropped mostly in existing Windows sub-directories.
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Terence Kawaja
Terence Kawaja
@tkawaja
SPAC IS THE NEW BLACK: SPACs (Special Purpose Acquisition Companies) are all the rage these days. In this thread, I want to say a few things about SPACs that I learned in my research of the market. 1/15


What was once an esoteric (and somewhat tawdry) financing vehicle has now come into the main in terms of quantity ($83 billion in IPOs in 2020) and quality (A-player names pursuing SPACs from name brand PE and VC funds to leading investors and advisors and even celebrities.) 2/15

Early and serial SPAC sponsors like Harry Sloan and Jeff Sagansky (Flying Eagle), Michael Klein (Churchill) and Chamath Palihapitiya (Hedosophia) have helped propel the SPAC phenomenon to new heights, not to mention tech gurus, former politicians and even famous athletes. 3/15


LUMA has created a SPAC LUMAscape that maps over 250 SPACs by stage across 8 verticals. This is the evergreen post that we will endeavor to keep updated (and welcome your feedback): https://t.co/aboAAemu53 4/15

A SPAC is a blank check company where investors back a sponsor group to find a suitable target to merge with (the de-SPACing) which is often accompanied by a PIPE (private investment into public equity) to further finance the target company. 5/15
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Madima
Madima
@MaanoMadima
Below are some of the most interesting business stories I came across in 2020.

P. S The list is not exhaustive.

1) PSG unbundled it's 28.1% ownership of Capitec.

2) MTN sold a minority stake in Jumia Technologies for R2.3-billion.

3) Dischem has completed the acquisition of Medicare Health for R282million

4) TFG bought 381 Jet stores from Edcon for R480m.

5) Capitec entered into a partnership with SA Home Loans to offer home loans.

6) Food Lover’s Market has started closing outlets in Zambia.

7) Sasol sold 50% of the "base chemicals division" at Lake Charles which equates to ~25% of total Lake Charles project. Sasol will still retain around ~75% of the total Lake Charles Chemical Project.

8) Sasol agreed to sell its 50% stake in Gemini for R6.2bn.

9) Tongaat Hulett sold its starch and glucose business to Barloworld for R5.26bn. The group has used R4.64bn of this to pay down debt.

10) The Competition Commission approved Nimble Fund 1’s intention to acquire Edcon Book Debts.

11) Greg Maloka has decided to resign from Kaya.

12) Ethos Private Equity has won a contract with Ninety One to take on an advisory contract for Ninety One's Africa Private Equity funds.

Ninety One will maintain control and oversight of the portfolio, while Ethos will manage the assets

13) Bidvest Wits sold to TTM
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Kyle Prinsloo
Kyle Prinsloo
@study_web_dev
The freelancing blueprint 🧵👇

Disclaimer:

Freelancing is NOT for everybody, but it is for everyone 😎

Just give freelancing an honest attempt for a year.

If you enjoy it - cool.

If you don't - cool.

At least you tried.

Freelancing starts with a SKILL.

1. Learn how to make a website (Web Design).
2. Learn how to get sales for a business (Digital Marketing).
3. Learn Web Design and Digital Marketing.

I'd recommend option 3.

You're offering a once-off and monthly service.

Win-win.

Where do you learn this skill?

YouTube, Google and Udemy.

Don't overcomplicate it.

How do you make money from this skill?

Marketing and Branding.

This will help you get clients.
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Anurag Singh
Anurag Singh
@anuragsingh_as
#PolicybazaarIPO Long thread: There is monetary printing by RBI, there is lending by banks & finally there is the Indian IPO. “Foreign owned” company with ~1 Bn investment is selling “Indian” unicorn to “savvy” Indians for $ 6.2 Bn. Want to know how this printing works? Let’s go-

The basics: Rs 6017 Cr being raised by new shares (Rs 3750) & re-selling (Rs 2267). Face value Rs 2 offered at Rs ~1000 per share is one of a kind. Who decides the price? Examine the valuation ladder below.
With ~$ 150 Mn invested till 2017, we see $ 525 Mn rushes in after 2018


So, a company valued $1 Bn in 2018 & $ 1.5 Bn in 2019 by key investors like Tencent, suddenly “needs money” in March 2021. Falcon Edge “invests” all of $ 75 Mn in March at valuation of $ 2.65 Bn.
Enter IPO bankers a couple of months later & they value the company at $ 6.2 Bn

Why does PolicyBazaar need $ 75 Mn just 6 months before IPO & give away 2.5 times in returns? And it doesn’t even use that money as the B/S reflects appx Rs 1000 Cr in cash lying around.
What kind of a favor is that? Why do they need US investment bank for this just before IPO?

Notice 50% rise in shareholding of promotors here & a 1: 500 bonus share issue. After all, how do you print money & still keep share price at “reasonable” Rs 1000?
Without bonus issue in June 2021, each share would have costed Rs 50,000. Heard of circular valuation?
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Nikita Poojary
Nikita Poojary
@niki_poojary
Time for a Thread🧵On yesterday’s “F&O Pe Charcha - Diary Of An Option Seller” with @Mitesh_Engr by @Paytm Money

Why Gain/loss ratio is important that the batting average
• More than the Win Loss ratio check the total Gain /loss ratio
• If your avg loss is 1% and avg gain is +5% then even if you are losing in 7 positions and winning in 3 out of 10 still you will be net profitable

Treat Trading as a business:
• Stick to a single strategy. Eg: A restaurateur usually wouldn’t be changing his recipe on a daily basis
• Mitesh Sir only works on BO or BD strategy, as he is a versatile trader

• Any trader should atleast experience 3 scenarios i.e. bull, bear & consolidation
• Target 4% per month i.e. 48% p.a.
• Option buying requires skill, everyday there wont be huge momentum, so buying options won’t yield good results, but high chances of falling into a bad habit

Stock Selection:
• In F&O segment there are ~150 stocks
• Not everyday all stocks will move
• Only 5-7 stocks move upside or downside
• Focus only these momentum stocks
• Check which one is moving
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s. e. smith
s. e. smith
@sesmith
Oh are we talking about Sheryl Sandberg again? Well, your reminder that disabled feminists, LGBQT feminists, and feminists of color criticised her ‘lean in’ deal from day one. Don’t let anyone retcon that out of existence.

Like, a pretty narrow slice of feminists actually endorse and support corporate feminism, they’re just white, nondisabled, straight, cis, and noisy so their voices have a bigger reach.

So if your impression is that ‘feminism’ gave her a ‘free pass’ maybe you should question your overall impression of feminism rather than continuing to centre a handful of people as ‘thought leaders’ or whatever.

Critics within feminism have been doing this work for a long time and have been getting shat on it for about as long and I'm tired of seeing them erased. But what do I know — I am, after all, not a feminist.

But I WILL say, from my not-a-feminist perspective, that people who aren’t feminists who are criticising feminism should maybe understand the movement more deeply beyond a handful of high profile celebrity feminists.
TECH , SOCIETY
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Anshul Pandey
Anshul Pandey
@Anshulspiritual
IMPORTANCE AND CHARACTERISTICS OF GARUDA PURAN.

On the request of Garud, Bhagwan Vishnu, the rider of Garud, described this Puran based in Taksharya Kalp. It has ninteen thousand shlokas.
This book begins with a question and goes on to describe the Shrishti.


Then it describes the Surya puja vidhi, Diksha, Shradha, Nav vyuh puja, Vishnu sahastra naam kirtan and dhyan, Yog, Mrityunjay puja,Mala mantra, Shivarcha, Gopal puja, Trailikya mohan Shridhar puja, Vishnuarcha, Devpuja, Sandhya, Pancham tattva, Durga and Chakra Archana,

Maheshwar puja, Pavitra ropan puja, Murti dhyan, Vaastu maan, Prasaad, Sarvdev pratishtha, Ashtaang Yog, Daan dharm, Prayaschit, Narak description, Chakravyuh, Jyotish, Samudra shastra, Swar gyan, Tirth, Importance of Gaya, Different Manvantars, Pittra's, Varna dharma,

Dravya shudhhi, Samarpan, Vinayak puja, Grah yagya, Ashram, Shouch, Pret shudhi, Niti Shashtra, Vrat katha, Som and Surya vansh, Hari vansh, Bharat akhyan, Ayurveda, Medicine in Ayurveda, Rog nashak kavach, Garud Kavach, Traipur mantra, Prashna, difficulties in vyakaran,

Chhand shastra, Discipline, Snan Vidhi Tarpan, Bali Vaishvadev, Pavarna Karma, Sapindan, Dharmasar, Repentence of sins, Prati sankraman, Karmafal, Yug dharm, Vishnu bhakti, Method of Namaskaar to Hari, Narsingh strotras, Gyanamrit, Vishnuarchan strotras, Principles of Sankhya,
RELIGION
Meet Shah
Meet Shah
@ms89_meet
List of companies with more than 50% Market Share:

1) Maruti Suzuki (passenger cars)
2) APL Apollo (structural & pre galvanized tubes)
3) CDSL (investors accounts)
4) Interglobe Aviation (air traffic passengers)
5) GMM Pfaudler (glass lined equipment)

6) Asian Paints (decorative paints)
7) Colgate (oral care)
8) Symphony (coolers)
9) PGHH (female care & vaporub)
10) La Opala Rg (opalware)
11) HLE Glasscoat (filtration & drying equipment)
12) Delta Corp (online poker games)
13) Bajaj Auto (3W segment)
14) Vinati Organics (IBB)

15) OCCL (insoluble sulphur)
16) LMW (textile machinery)
17) Bajaj Consumer (almond hair oil)
18) Indiamart Intermesh (online B2B Classified space)
19) Vst Tillers (power tillers)
20) Sanghvi Movers (overall domestic crane hiring market)
21) Emami (antiseptic & male grooming)

List of companies with more than 60% Market Share:

1) Concor (domestic container cargo transport)
2) Exide (lead batteries)
3) Naukri (Indian job market space)
4) Praj (ethanol plant installing)
5) ACE (mobile & tower cranes)
6) Pidilite (adhesives)
7) Jamna Auto (leaf spring)

8) CAMS (RTA within mutual fund industry)
9) Time Technoplast (polymer based industrial packaging)
MONOPOLIES , STOCKS , EQUITIES
Tej Dosa \U0001f602\U0001f9e0
Tej Dosa 😂🧠...
@ComedicBizman
(A THREAD)

18 Things I Would Do At 18:

I was a dumb 18-year-old. I thought I had the world figured out. I didn't.

I stayed up till the wee hours. Drank every liquor invented.

Partied non-stop. Woke up after noon.

Surfed the web aimlessly.

Read BS self-help.

Repeat.

If I could do it all over again, here's 18 things I would do...

PHASE ONE: GET MY MIND RIGHT

ACTION #1: Break the chains - I wish I made the decision earlier. I didn't. I was scared. Anxious. Nervous. I knew once I made the decision, there was no going back.

Don't be like me.

Make the decision at 18. The decision to pave your own path in life. To go against the herd. To choose your own adventure. To commit to whatever sets your heart on fire.

ACTION #2: Cut off every negative person in your life.

Scroll through the contacts in your phone...

Notice how the names make you feel.

Delete any and all names that fill you with doubt and negativity.

ACTION #3: Condition your mind to embrace the producer mindset instead of the consumer mindset.

Consume 20% of the time. Produce 80% of the time.

Case closed.

ACTION #4: Set an intention to make $10k a month and write it out every MORNING and NIGHT as if you already achieved it (in the past tense).

Visualize yourself already having the money as you write.

Cheesy?

Yes.

Does it work?

Yes.

PHASE TWO: EDUCATION
LIFE