pwc-archive/papers-with-abstracts huggingface dataset:
Antibiotic-dependent instability of homeostatic plasticity for growth and environmental load
Authors: Shunnosuke Okada, Yudai Inabu, Hirokuni Miyamoto, Kenta Suzuki, Tamotsu Kato, Atsushi Kurotani, Yutaka Taguchi, Ryoichi Fujino, Yuji Shiotsuka, Tetsuji Etoh, Naoko Tsuji, Makiko Matsuura, Arisa Tsuboi, Akira Saito, Hiroshi Masuya, Jun Kikuchi, Hiroshi Ohno, Hideyuki Takahashi
Date: 2022-11-27 00:00:00
Tasks: Causal Inference

Reducing antibiotic usage in livestock animals has become an urgent issue worldwide to prevent antimicrobial resistance. Here, abuse of chlortetracycline (CTC), a versatile antibacterial agent, on the performance, blood components, fecal microbiota, and organic acid concentration in calves was investigated. Japanese Black calves were fed milk replacer containing CTC at 10 g/kg (CON) or 0 g/kg (EXP). Growth performance was not affected by CTC administration. However, CTC administration altered the correlation between fecal organic acids and bacterial genera. Machine learning methods such as association analysis, linear discriminant analysis, and energy landscape analysis revealed that CTC administration affected according to certain rules the population of various types of fecal bacteria. It is particularly interesting that the population of several methane-producing bacteria was high in the CON, and that of Lachnospiraceae, a butyrate-producing bacteria, was high in the EXP at 60 d of age. Furthermore, statistical causal inference based on machine learning data estimated that CTC treatment affects the entire intestinal environment, inhibiting butyrate production for growth and biological defense, which may be attributed to methanogens in feces. Thus, these observations highlight the multiple harmful impacts of antibiotics on intestinal health and the potential production of greenhouse gas in the calves.
Machine Learning Algorithms for Predicting in-Hospital Mortality in Patients with ST-Segment Elevation Myocardial Infar
Authors: Ding Tao, Chen Liu, Shihan Wan
Date: 2022-11-26 00:00:00
Tasks: regression

Acute myocardial infarction (AMI) is one of the most severe manifestation of coronary artery disease. ST-segment elevation myocardial infarction (STEMI) is the most serious type of AMI. We proposed to develop a machine learning algorithm based on the home page of electronic medical record (HPEMR) for predicting in-hospital mortality of patients with STEMI in the early stage. Methods: This observational study applied clinical information collected between 2013 and 2017 from 7 tertiary hospitals in Shenzhen, China. The patients' STEMI data were used to train 4 different machine learning algorithms to predict in-hospital mortality among the patients with STEMI, including Logistic Regression, Support Vector Machine, Gradient Boosting Decision Tree, and Artificial Neuron network. Results: A total of 5865 patients with STEMI were enrolled in our study. The model was developed by considering 3 types of variables, which included demographic data, diagnosis and comorbidities, and hospitalization information basing on HPEMR. The association of selected features using univariant logistic regression was reported. Specially, for the comorbidities, atrial fibrillation (OR: 11.0; 95% CI: 5.64 - 20.2), acute renal failure (OR: 9.75; 95% CI: 3.81 - 25.0), type 2 diabetic nephropathy (OR: 5.45; 95% CI: 1.57 - 19.0), acute heart failure (OR: 6.05; 95% CI: 1.99 - 14.9), and cardiac function grade IV (OR: 28.6; 95% CI: 20.6 - 39.6) were found to be associated with a high odds of death. Within the test dataset, our model showed a good discrimination ability as measured by area under the receiver operating characteristic curve (AUC; 0.879) (95% CI: 0.825 - 0.933).
Karyotype AI for Precision Oncology
Authors: Zahra Shamsi, Isaac Reid, Drew Bryant, Jacob Wilson, Xiaoyu Qu, Avinava Dubey, Konik Kothari, Mostafa Dehghani, Mariya Chavarha, Valerii Likhosherstov, Brian Williams, Michael Frumkin, Fred Appelbaum, Krzysztof Choromanski, Ali Bashir, Min Fang
Date: 2022-11-20 00:00:00
Tasks: Few-Shot Learning, Inductive Bias

We present a machine learning method capable of accurately detecting chromosome abnormalities that cause blood cancers directly from microscope images of the metaphase stage of cell division. The pipeline is built on a series of fine-tuned Vision Transformers. Current state of the art (and standard clinical practice) requires expensive, manual expert analysis, whereas our pipeline takes only 15 seconds per metaphase image. Using a novel pretraining-finetuning strategy to mitigate the challenge of data scarcity, we achieve a high precision-recall score of 94% AUC for the clinically significant del(5q) and t(9;22) anomalies. Our method also unlocks zero-shot detection of rare aberrations based on model latent embeddings. The ability to quickly, accurately, and scalably diagnose genetic abnormalities directly from metaphase images could transform karyotyping practice and improve patient outcomes. We will make code publicly available.
ETF construction on CRIX
Authors: Konstantin Häusler
Date: 2022-11-28 00:00:00

Investments in cryptocurrencies (CCs) remain risky due to high volatility. Exchange Traded Funds (ETFs) are a suitable tool to diversify risk and to benefit from the growth of the whole CC sector. We construct an ETF on the CRIX, the CRyptocurrency IndeX that maps the non-stationary CC dynamics closely by adapting its constituents weights dynamically. The scenario analysis considers the fee schedules of regulated CC exchanges, spreads obtained from high-frequency order book data, and models capital deposits to the ETF stochastically. The analysis yields valuable insights into the mechanisms, costs and risks of this new financial product: i) although the composition of the CRIX ETF changes frequently (from 5 to 30 constituents), it remains robust in its core, as the weights of Bitcoin (BTC) and Ethereum (ETH) are robust over time, ii) on average, a portion of 5.2% needed to be rebalanced at the rebalancing dates, iii) trading costs are low compared to traditional assets, iv) the liquidity of the CC sector has increased significantly during the analysis period, spreads occur especially for altcoins and increase by the size of the transactions. But since BTC and ETH are most affected by rebalancing, the cost of spreads remains limited.
Dynamic Equilibrium with Insider Information and General Uninformed Agent Utility
Authors: Jerome Detemple, Scott Robertson
Date: 2022-11-28 00:00:00

We study a continuous time economy where agents have asymmetric information. The informed agent (``$I$''), at time zero, receives a private signal about the risky assets' terminal payoff $\Psi(X_T)$, while the uninformed agent (``$U$'') has no private signal. $\Psi$ is an arbitrary payoff function, and $X$ follows a time-homogeneous diffusion. Crucially, we allow $U$ to have von Neumann-Morgenstern preferences with a general utility function on $(0,\infty)$ satisfying the standard conditions. This extends previous constructions of equilibria with asymmetric information used when all agents have exponential utilities and enables us to study the impact of $U$'s initial share endowment on equilibrium. To allow for $U$ to have general preferences, we introduce a new method to prove existence of a partial communication equilibrium (PCE), where at time $0$, $U$ receives a less-informative signal than $I$. In the single asset case, this signal is recoverable by viewing the equilibrium price process over an arbitrarily short period of time, and hence the PCE is a dynamic noisy rational expectations equilibrium. Lastly, when $U$ has power (constant relative risk aversion) utility, we identify the equilibrium price in the small and large risk aversion limits.
A model-free approach to continuous-time finance
Authors: Henry Chiu, Rama Cont
Date: 2022-11-28 00:00:00
Tasks: model

We present a non-probabilistic, pathwise approach to continuous-time finance based on causal functional calculus. We introduce a definition of self-financing, free from any integration concept and show that the value of a self-financing portfolio is a pathwise integral (every self-financing strategy is a gradient) and that generic domain of functional calculus is inherently arbitrage-free. We then consider the problem of hedging a path-dependent payoff across a generic set of scenarios. We apply the transition principle of Isaacs in differential games and obtain a verification theorem for the optimal solution, which is characterised by a fully non-linear path-dependent equation. For the Asian option, we obtain explicit solution.
Parameter Estimation of the Heston Volatility Model with Jumps in the Asset Prices
Authors: Jarosław Gruszka, Janusz Szwabiński
Date: 2022-11-27 00:00:00
Tasks: parameter estimation

Parametric estimation of stochastic differential equations (SDEs) has been a subject of intense studies already for several decades. The Heston model for instance is driven by two coupled SDEs and is often used in financial mathematics for the dynamics of the asset prices and their volatility. Calibrating it to real data would be very useful in many practical scenarios. It is very challenging however, since the volatility is not directly observable. In this paper, a complete estimation procedure of the Heston model without and with jumps in the asset prices is presented. Bayesian regression combined with the particle filtering method is used as the estimation framework. Within the framework, we propose a novel approach to handle jumps in order to neutralise their negative impact on the estimates of the key parameters of the model. An improvement of the sampling in the particle filtering method is discussed as well. Our analysis is supported by numerical simulations of the Heston model to investigate the performance of the estimators. And a practical follow-along recipe is given to allow for finding adequate estimates from any given data.
Efficient and Accurate Calibration to FX Market Skew with Fully Parameterized Local Volatility Model
Authors: Dongli Wu, Bufan Zhang, Xiao Lin
Date: 2022-11-22 00:00:00
Tasks: Management

When trading American and Asian options in the FX derivatives market, banks must calculate prices using a complex mathematical model. It is often observed that different models produce varying prices for the same exotic option, which violates the non-arbitrage requirement of derivative risk management. To address this issue, we have studied a fully parameterized local volatility model for pricing American/Asian options. This model, when implemented using a grid or Monte-Carlo numerical method, can be efficiently and accurately calibrated to FX market skew volatilities. As a result, the model can provide reliable prices for exotic options during daily trading activities.
Siamese based Neural Network for Offline Writer Identification on word level data
Authors: Vineet Kumar, Suresh Sundaram
Date: 2022-11-17 00:00:00
Tasks: Handwriting Recognition

Handwriting recognition is one of the desirable attributes of document comprehension and analysis. It is concerned with the documents writing style and characteristics that distinguish the authors. The diversity of text images, notably in images with varying handwriting, makes the process of learning good features difficult in cases where little data is available. In this paper, we propose a novel scheme to identify the author of a document based on the input word image. Our method is text independent and does not impose any constraint on the size of the input image under examination. To begin with, we detect crucial components in handwriting and extract regions surrounding them using Scale Invariant Feature Transform (SIFT). These patches are designed to capture individual writing features (including allographs, characters, or combinations of characters) that are likely to be unique for an individual writer. These features are then passed through a deep Convolutional Neural Network (CNN) in which the weights are learned by applying the concept of Similarity learning using Siamese network. Siamese network enhances the discrimination power of CNN by mapping similarity between different pairs of input image. Features learned at different scales of the extracted SIFT key-points are encoded using Sparse PCA, each components of the Sparse PCA is assigned a saliency score signifying its level of significance in discriminating different writers effectively. Finally, the weighted Sparse PCA corresponding to each SIFT key-points is combined to arrive at a final classification score for each writer. The proposed algorithm was evaluated on two publicly available databases (namely IAM and CVL) and is able to achieve promising result, when compared with other deep learning based algorithm.
LAPTNet: LiDAR-Aided Perspective Transform Network
Authors: Manuel Alejandro Diaz-Zapata, Özgür Erkent, Christian Laugier, Jilles Dibangoye, David Sierra González
Date: 2022-11-14 00:00:00
Tasks: Autonomous Vehicles

Semantic grids are a useful representation of the environment around a robot. They can be used in autonomous vehicles to concisely represent the scene around the car, capturing vital information for downstream tasks like navigation or collision assessment. Information from different sensors can be used to generate these grids. Some methods rely only on RGB images, whereas others choose to incorporate information from other sensors, such as radar or LiDAR. In this paper, we present an architecture that fuses LiDAR and camera information to generate semantic grids. By using the 3D information from a LiDAR point cloud, the LiDAR-Aided Perspective Transform Network (LAPTNet) is able to associate features in the camera plane to the bird's eye view without having to predict any depth information about the scene. Compared to state-of-theart camera-only methods, LAPTNet achieves an improvement of up to 8.8 points (or 38.13%) over state-of-art competing approaches for the classes proposed in the NuScenes dataset validation split.
Sign Language to Text Conversion in Real Time using Transfer Learning
Authors: Shubham Thakar, Samveg Shah, Bhavya Shah, Anant V. Nimkar
Date: 2022-11-13 00:00:00
Tasks: Deep Learning, Image Classification, Transfer Learning, Translation

The people in the world who are hearing impaired face many obstacles in communication and require an interpreter to comprehend what a person is saying. There has been constant scientific research and the existing models lack the ability to make accurate predictions. So we propose a deep learning model trained on ASL i.e. American Sign Language which will take actions in the form of ASL as input and translate it into text. To achieve the translation a Convolution Neural Network model and a transfer learning model based on the VGG16 architecture are used. There has been an improvement in accuracy from 94% of CNN to 98.7% of Transfer Learning, an improvement of 5%. An application with the deep learning model integrated has also been built.
Sentence-Level Sign Language Recognition Framework
Authors: Atra Akandeh
Date: 2022-11-13 00:00:00
Tasks: Sentence, Sign Language Recognition

We present two solutions to sentence-level SLR. Sentence-level SLR required mapping videos of sign language sentences to sequences of gloss labels. Connectionist Temporal Classification (CTC) has been used as the classifier level of both models. CTC is used to avoid pre-segmenting the sentences into individual words. The first model is an LRCN-based model, and the second model is a Multi-Cue Network. LRCN is a model in which a CNN as a feature extractor is applied to each frame before feeding them into an LSTM. In the first approach, no prior knowledge has been leveraged. Raw frames are fed into an 18-layer LRCN with a CTC on top. In the second approach, three main characteristics (hand shape, hand position, and hand movement information) associated with each sign have been extracted using Mediapipe. 2D landmarks of hand shape have been used to create the skeleton of the hands and then are fed to a CONV-LSTM model. Hand locations and hand positions as relative distance to head are fed to separate LSTMs. All three sources of information have been then integrated into a Multi-Cue network with a CTC classification layer. We evaluated the performance of proposed models on RWTH-PHOENIX-Weather. After performing an excessive search on model hyper-parameters such as the number of feature maps, input size, batch size, sequence length, LSTM memory cell, regularization, and dropout, we were able to achieve 35 Word Error Rate (WER).
PatchBlender: A Motion Prior for Video Transformers
Authors: Gabriele Prato, Yale Song, Janarthanan Rajendran, R Devon Hjelm, Neel Joshi, Sarath Chandar
Date: 2022-11-11 00:00:00

Transformers have become one of the dominant architectures in the field of computer vision. However, there are yet several challenges when applying such architectures to video data. Most notably, these models struggle to model the temporal patterns of video data effectively. Directly targeting this issue, we introduce PatchBlender, a learnable blending function that operates over patch embeddings across the temporal dimension of the latent space. We show that our method is successful at enabling vision transformers to encode the temporal component of video data. On Something-Something v2 and MOVi-A, we show that our method improves the baseline performance of video Transformers. PatchBlender has the advantage of being compatible with almost any Transformer architecture and since it is learnable, the model can adaptively turn on or off the prior. It is also extremely lightweight compute-wise, 0.005% the GFLOPs of a ViT-B.
Text-Aware Dual Routing Network for Visual Question Answering
Authors: Luoqian Jiang, Yifan He, Jian Chen
Date: 2022-11-17 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) is a challenging task to provide an accurate natural language answer given an image and a natural language question about the image. It involves multi-modal learning, i.e., computer vision (CV) and natural language processing (NLP), as well as flexible answer prediction for free-form and open-ended answers. Existing approaches often fail in cases that require reading and understanding text in images to answer questions. In practice, they cannot effectively handle the answer sequence derived from text tokens because the visual features are not text-oriented. To address the above issues, we propose a Text-Aware Dual Routing Network (TDR) which simultaneously handles the VQA cases with and without understanding text information in the input images. Specifically, we build a two-branch answer prediction network that contains a specific branch for each case and further develop a dual routing scheme to dynamically determine which branch should be chosen. In the branch that involves text understanding, we incorporate the Optical Character Recognition (OCR) features into the model to help understand the text in the images. Extensive experiments on the VQA v2.0 dataset demonstrate that our proposed TDR outperforms existing methods, especially on the ''number'' related VQA questions.
Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
Authors: Jiachen Lei, Shuang Ma, Zhongjie Ba, Sai Vemprala, Ashish Kapoor, Kui Ren
Date: 2022-11-18 00:00:00
Tasks: Object State Change Classification, Temporal Localization, Video Understanding

In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022. As team TheSSVL, we ranked 2nd place in both tasks. Our code will be made available.
Clarity: an improved gradient method for producing quality visual counterfactual explanations
Authors: Claire Theobald, Frédéric Pennerath, Brieuc Conan-Guez, Miguel Couceiro, Amedeo Napoli
Date: 2022-11-22 00:00:00
Tasks: counterfactual

Visual counterfactual explanations identify modifications to an image that would change the prediction of a classifier. We propose a set of techniques based on generative models (VAE) and a classifier ensemble directly trained in the latent space, which all together, improve the quality of the gradient required to compute visual counterfactuals. These improvements lead to a novel classification model, Clarity, which produces realistic counterfactual explanations over all images. We also present several experiments that give insights on why these techniques lead to better quality results than those in the literature. The explanations produced are competitive with the state-of-the-art and emphasize the importance of selecting a meaningful input space for training.
Leveraging per Image-Token Consistency for Vision-Language Pre-training
Authors: Yunhao Gou, Tom Ko, Hansi Yang, James Kwok, Yu Zhang, Mingxuan Wang
Date: 2022-11-20 00:00:00
Proceeding: CVPR 2023 1
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Sentence

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether they are consistent with the image (i.e., Image-Text Consistent Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks.
FAF: A novel multimodal emotion recognition approach integrating face, body and text
Authors: Zhongyu Fang, Aoyun He, Qihui Yu, Baopeng Gao, Weiping Ding, Tong Zhang, Lei Ma
Date: 2022-11-20 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition

Multimodal emotion analysis performed better in emotion recognition depending on more comprehensive emotional clues and multimodal emotion dataset. In this paper, we developed a large multimodal emotion dataset, named "HED" dataset, to facilitate the emotion recognition task, and accordingly propose a multimodal emotion recognition method. To promote recognition accuracy, "Feature After Feature" framework was used to explore crucial emotional information from the aligned face, body and text samples. We employ various benchmarks to evaluate the "HED" dataset and compare the performance with our method. The results show that the five classification accuracy of the proposed multimodal fusion method is about 83.75%, and the performance is improved by 1.83%, 9.38%, and 21.62% respectively compared with that of individual modalities. The complementarity between each channel is effectively used to improve the performance of emotion recognition. We had also established a multimodal online emotion prediction platform, aiming to provide free emotion prediction to more users.