pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Robust Online Portfolio Selection
Authors: Man Yiu Tsang, Tony Sit, Hoi Ying Wong
Date: 2022-06-02 00:00:00
Tasks: Decision Making, Sequential Decision Making

The online portfolio selection (OLPS) problem differs from classical portfolio model problems, as it involves making sequential investment decisions. Many OLPS strategies described in the literature capture market movement based on various beliefs and are shown to be profitable. In this paper, we propose a robust optimization (RO)-based strategy that takes transaction costs into account. Moreover, unlike existing studies that calibrate model parameters from benchmark data sets, we develop a novel adaptive scheme that decides the parameters sequentially. With a wide range of parameters as input, our scheme captures market uptrend and protects against market downtrend while controlling trading frequency to avoid excessive transaction costs. We numerically demonstrate the advantages of our adaptive scheme against several benchmarks under various settings. Our adaptive scheme may also be useful in general sequential decision-making problems. Finally, we compare the performance of our strategy with that of existing OLPS strategies using both benchmark and newly collected data sets. Our strategy outperforms these existing OLPS strategies in terms of cumulative returns and competitive Sharpe ratios across diversified data sets, demonstrating its adaptability-driven superiority.
Longitudinal abnormalities in white matter extracellular free water volume fraction and neuropsychological functioning in patients with traumatic brain injury
Authors: James J Gugger, Alexa E Walter, Drew Parker, Nishant Sinha, Justin Morrison, Jeffrey Ware, Andrea LC Schneider, Dmitriy Petrov, Danielle K Sandsmark, Ragini Verma, Ramon Diaz-Arrastia
Date: 2022-06-02 00:00:00

Traumatic brain injury is a global public health problem associated with chronic neurological complications and long-term disability. Biomarkers that map onto the underlying brain pathology driving these complications are urgently needed to identify individuals at risk for poor recovery and to inform design of clinical trials of neuroprotective therapies. Neuroinflammation and neurodegeneration are two endophenotypes associated with increases in brain extracellular water content after trauma. The objective of this study was to describe the relationship between a neuroimaging biomarker of extracellular free water content and the clinical features of patients with traumatic brain injury. We analyzed a cohort of 64 adult patients requiring hospitalization for non-penetrating traumatic brain injury of all severities as well as 32 healthy controls. Patients underwent brain MRI and clinical neuropsychological assessment in the subacute (2-weeks) and chronic (6-months) post-injury period, and controls underwent a single MRI. For each subject, we derived a summary score representing deviations in whole brain white matter (1) extracellular free water volume fraction (VF) and (2) free water-corrected fractional anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was significantly higher in TBI patients in the subacute and chronic post-injury period relative to controls. SAS for VF significantly correlated with neuropsychological functioning in the subacute, but not chronic post-injury period. These findings indicate abnormalities in whole brain white matter extracellular water fraction in patients with TBI and are an important step toward identifying and validating noninvasive biomarkers that map onto the pathology driving disability after TBI.
Drug Repurposing For SARS-COV-2 Using Molecular Docking
Authors: Imra Aqeel, Abdul Majid, Muhammad Ismail, Hina Bashir
Date: 2022-06-02 00:00:00
Tasks: Molecular Docking

Drug repurposing is an unconventional approach that is used to investigate new therapeutic aids of existing and shelved drugs. Recent advancement in technologies and the availability of the data of genomics, proteomics, transcriptomics, etc., and with the accessibility of large and reliable database resources, there are abundantly of opportunities to discover drugs by drug repurposing in an efficient manner. The recent pandemic of SARS-COV-2, that caused the death of 6,245,750 human beings to date, has tremendously increase the exceptional usage of bioinformatics tools in interpreting the molecular characterizations of viral infections. In this paper, we have employed various bioinformatics tools such as AutoDock-Vina, PyMol etc. We have found a leading drug candidate Cepharanthine that has shown better results and effectiveness than recently used antiviral drug candidates such as Favipiravir, IDX184, Remedesivir, Ribavirin and etc. This paper has analyzed Cepharanthine potential therapeutic importance as a drug of choice in managing COVID-19 cases. It is anticipated that proposed study would be beneficial for researchers and medical practitioners in handling SARS-CoV-2 and its variant related diseases.
How brokers can optimally plot against traders
Authors: Manuel Lafond
Date: 2016-04-08 00:00:00

Traders buy and sell financial instruments in hopes of making profit, and brokers are responsible for the transaction. There are several hypotheses and conspiracy theories arguing that in some situations, brokers want their traders to lose money. For instance, a broker may want to protect the positions of a privileged customer. Another example is that some brokers take positions opposite to their traders', in which case they make money whenever their traders lose money. These are reasons for which brokers might manipulate prices in order to maximize the losses of their traders. In this paper, our goal is to perform this shady task optimally -- or at least to check whether this can actually be done algorithmically. Assuming total control over the price of an asset (ignoring the usual aspects of finance such as market conditions, external influence or stochasticity), we show how in quadratic time, given a set of trades specified by a stop-loss and a take-profit price, a broker can find a maximum loss price movement. We also look at an online trade model where broker and trader exchange turns, each trying to make a profit. We show in which condition either side can make a profit, and that the best option for the trader is to never trade.
Fast L1-Minimization Algorithms For Robust Face Recognition
Authors: Allen Y. Yang, Zihan Zhou, Arvind Ganesh, S. Shankar Sastry, Yi Ma
Date: 2010-07-21 00:00:00
Tasks: Compressive Sensing, Face Recognition, Robust Face Recognition

L1-minimization refers to finding the minimum L1-norm solution to an underdetermined linear system b=Ax. Under certain conditions as described in compressive sensing theory, the minimum L1-norm solution is also the sparsest solution. In this paper, our study addresses the speed and scalability of its algorithms. In particular, we focus on the numerical implementation of a sparsity-based classification framework in robust face recognition, where sparse representation is sought to recover human identities from very high-dimensional facial images that may be corrupted by illumination, facial disguise, and pose variation. Although the underlying numerical problem is a linear program, traditional algorithms are known to suffer poor scalability for large-scale applications. We investigate a new solution based on a classical convex optimization framework, known as Augmented Lagrangian Methods (ALM). The new convex solvers provide a viable solution to real-world, time-critical applications such as face recognition. We conduct extensive experiments to validate and compare the performance of the ALM algorithms against several popular L1-minimization solvers, including interior-point method, Homotopy, FISTA, SESOP-PCD, approximate message passing (AMP) and TFOCS. To aid peer evaluation, the code for all the algorithms has been made publicly available.
Comparative study on different Deep Learning models for Skin Lesion Classification using transfer learning approach
Authors: Saswat Panda, Abhishek Sunil Tiwari, Manas Ranjan Prusty
Date: 2021-01-03 00:00:00
Proceeding: International Journal of Scientific and Research Publication 2021 1
Tasks: Lesion Classification, Skin Lesion Classification, Transfer Learning

Developing countries, specifically India, do not have sufficient hospitals and doctors to reach out to the population.
Forget about skin specialists, there are still thousands of villages without even a basic hospital. But, there is one thing that
reaches out to every person in this country which is the internet. This research paper focuses on training different pre-trained
models on our dataset and suggests the best one for skin lesion classification. This model has been used with a mobile compatible web app to detect skin cancer at the earliest stage possible. Hence, the objective of this research paper is to develop
an intelligent system to detect skin cancer at the earliest stage possible by a skin lesion classifier. The dataset includes different
categories of diseases like Actinic Keratosis, Vascular Lesions, Lentigo, Melanoma, and Dermatofibroma. The pre-trained
models used in this research are the different transfer learning methods like VGG19, Xception, Densenet, Inception, MobileNet,
NasNetMobile, and Resnet. Using transfer learning, Deep Neural Networks can be trained with presumably less amount of
data. Also, transfer learning has been consistently proven to reduce training time as well as boost model accuracy. Hence, this
paper has given the emphasis on using transfer learning models instead of building a model from scratch.
Towards Practical and Efficient Long Video Summary
Authors: Xiaopeng Ke, Boyu Chang, Hao Wu, Fengyuan Xu, Sheng Zhong
Date: 2022-04-27 00:00:00
Proceeding: ICASSP 2022 4
Tasks: Video Summarization

Recently, video summarization (VS) techniques are widely used to alleviate huge processing pressure brought by numerous long videos. However, it is hard to summarize long videos efficiently since processing hundreds of frames is still time-consuming. In this paper, we find that the Kernel Temporal Segmentation (KTS) method designed for detecting the shot boundaries in SOTA VS methods is time-consuming while handling long videos. To address this issue, we propose the Distribution-based KTS (D-KTS) by fully considering the characteristic of shot length distribution. Furthermore, we propose the Hash-based Adaptive Frame Selection (HAFS) to improve the system performance by fully taking advantage of the temporal locality of long videos. Our experiments present that the proposed D-KTS is 92.70% faster and takes up 90.08% less memory than the baseline KTS method on average.
PMData: A Sports Logging Dataset
Authors: Vajira Thambawita et al.
Date: 2022-07-08 00:00:00
Proceeding: MMSys’20 2022 7

In this paper, we present data: a dataset that combines traditional lifelogging data with sports-activity data. Our dataset enables the development of novel data analysis and machine-learning applications where, for instance, additional sports data is used to predict and analyze everyday developments, like a person’s weight and sleep patterns; and applications where traditional lifelog data is used in a sports context to predict athletes’ performance.PMDatacombines input from Fitbit Versa 2 smartwatch wristbands, thePMSys sports logging smartphone application, and Google forms. Logging data has been collected from 16 persons for five months. Our initial experiments show that novel analyses are possible, but there is still room for improvement.
BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML
Authors: Mehdi Bahrami, Wei-Peng Chen, Lei Liu, Mukul Prasad
Date: 2022-06-01 00:00:00
Proceeding: AutoML'2022 2022 6
Tasks: AutoML

Data pre-processing is one of the key steps in creating machine learning pipelines for tabular data. One of the common data pre-processing operations implemented in AutoML systems is to encode categorical features as numerical features. Typically, this is implemented using a simple alphabetical sort on the categorical values, using functions such as OrdinalEncoder, LabelEncoder in Scikit-Learn and H2O. However, often there exist semantic ordinal relationships among the categorical values, such as: quality level (i.e., [’very good’ > ’good’ > ’normal’> ’poor’]), or month (i.e., [’Jan’< ’Feb’ < ’Mar’]). Such semantic relationships are not exploited by previous AutoML approaches. In this paper, we introduce BERT-Sort, a novel approach to semantically encode ordinal categorical values via zero-shot Masked Language Models (MLM) and apply it to AutoML for tabular data. We created a new benchmark of 42 features from 10 public data sets for sorting categorical ordinal values for the first time, where BERT-Sort significantly improves semantic encoding of ordinal values in comparison to the existing approaches with 27% improvement. We perform a comprehensive evaluation of BERT-Sort on different public MLMs, such as RoBERTa, XLM and DistilBERT. We also
compare the performance of raw data sets against encoded data sets through BERT-Sort in different AutoML platforms including AutoGluon, FLAML, H2O, and MLJAR to evaluate the proposed approach in an end-to-end scenario.
Egocentric Video-Language Pretraining
Authors: Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, RongCheng Tu, Wenzhe Zhao, Weijie Kong, Chengfei Cai, Hongfa Wang, Dima Damen, Bernard Ghanem, Wei Liu, Mike Zheng Shou
Date: 2022-06-03 00:00:00
Tasks: Action Recognition, Contrastive Learning, Moment Queries, Multi-Instance Retrieval, Natural Language Queries, Object State Change Classification, Object State Change Classification on Ego4D, Question Answering, Retrieval, Temporal Localization, Text Retrieval, Video Summarization, Video-Text Retrieval

Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention. Best performing works rely on large-scale, 3rd-person video-text datasets, such as HowTo100M. In this work, we exploit the recently released Ego4D dataset to pioneer Egocentric VLP along three directions. (i) We create EgoClip, a 1st-person video-text pretraining dataset comprising 3.8M clip-text pairs well-chosen from Ego4D, covering a large variety of human daily activities. (ii) We propose a novel pretraining objective, dubbed EgoNCE, which adapts video-text contrastive learning to the egocentric domain by mining egocentric-aware positive and negative samples. (iii) We introduce EgoMCQ, a development benchmark that is close to EgoClip and hence can support effective validation and fast exploration of our design decisions in EgoClip and EgoNCE. Furthermore, we demonstrate strong performance on five egocentric downstream tasks across three datasets: video-text retrieval on EPIC-KITCHENS-100; action recognition on Charades-Ego; natural language query, moment query, and object state change classification on Ego4D challenge benchmarks. The dataset and code are available at https://github.com/showlab/EgoVLP.
Revisiting the "Video" in Video-Language Understanding
Authors: Shyamal Buch, Cristóbal Eyzaguirre, Adrien Gaidon, Jiajun Wu, Li Fei-Fei, Juan Carlos Niebles
Date: 2022-06-03 00:00:00
Proceeding: CVPR 2022 1
Tasks: Benchmarking, Question Answering, Retrieval, Text to Video Retrieval, Video Question Answering, Video Retrieval

What makes a video task uniquely suited for videos, beyond what can be understood from a single image? Building on recent progress in self-supervised image-language models, we revisit this question in the context of video and language tasks. We propose the atemporal probe (ATP), a new model for video-language analysis which provides a stronger bound on the baseline accuracy of multimodal models constrained by image-level understanding. By applying this model to standard discriminative video and language tasks, such as video question answering and text-to-video retrieval, we characterize the limitations and potential of current video-language benchmarks. We find that understanding of event temporality is often not necessary to achieve strong or state-of-the-art performance, even compared with recent large-scale video-language models and in contexts intended to benchmark deeper video-level understanding. We also demonstrate how ATP can improve both video-language dataset and model design. We describe a technique for leveraging ATP to better disentangle dataset subsets with a higher concentration of temporally challenging data, improving benchmarking efficacy for causal and temporal understanding. Further, we show that effectively integrating ATP into full video-level temporal models can improve efficiency and state-of-the-art accuracy.
YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection
Authors: Xiao Ruiqiang
Date: 2022-06-03 00:00:00
Tasks: Data Augmentation, Image Classification, Management

In response to the situation that the conventional bridge crack manual detection method has a large amount of human and material resources wasted, this study is aimed to propose a light-weighted, high-precision, deep learning-based bridge apparent crack recognition model that can be deployed in mobile devices' scenarios. In order to enhance the performance of YOLOv5, firstly, the data augmentation methods are supplemented, and then the YOLOv5 series algorithm is trained to select a suitable basic framework. The YOLOv5s is identified as the basic framework for the light-weighted crack detection model through experiments for comparison and validation.By replacing the traditional DarkNet backbone network of YOLOv5s with GhostNet backbone network, introducing Transformer multi-headed self-attention mechanism and bi-directional feature pyramid network (BiFPN) to replace the commonly used feature pyramid network, the improved model not only has 42% fewer parameters and faster inference response, but also significantly outperforms the original model in terms of accuracy and mAP (8.5% and 1.1% improvement, respectively). Luckily each improved part has a positive impact on the result. This paper provides a feasible idea to establish a digital operation management system in the field of highway and bridge in the future and to implement the whole life cycle structure health monitoring of civil infrastructure in China.
SNAKE: Shape-aware Neural 3D Keypoint Field
Authors: Chengliang Zhong, Peixing You, Xiaoxue Chen, Hao Zhao, Fuchun Sun, Guyue Zhou, Xiaodong Mu, Chuang Gan, Wenbing Huang
Date: 2022-06-03 00:00:00
Tasks: Keypoint Detection

Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection? Existing methods either seek salient features according to statistics of different orders or learn to predict keypoints that are invariant to transformation. Nevertheless, the idea of incorporating shape reconstruction into 3D keypoint detection is under-explored. We argue that this is restricted by former problem formulations. To this end, a novel unsupervised paradigm named SNAKE is proposed, which is short for shape-aware neural 3D keypoint field. Similar to recent coordinate-based radiance or distance field, our network takes 3D coordinates as inputs and predicts implicit shape indicators and keypoint saliency simultaneously, thus naturally entangling 3D keypoint detection and shape reconstruction. We achieve superior performance on various public benchmarks, including standalone object datasets ModelNet40, KeypointNet, SMPL meshes and scene-level datasets 3DMatch and Redwood. Intrinsic shape awareness brings several advantages as follows. (1) SNAKE generates 3D keypoints consistent with human semantic annotation, even without such supervision. (2) SNAKE outperforms counterparts in terms of repeatability, especially when the input point clouds are down-sampled. (3) the generated keypoints allow accurate geometric registration, notably in a zero-shot setting. Codes are available at https://github.com/zhongcl-thu/SNAKE
A Learning-Based Method for Automatic Operator Selection in the Fanoos XAI System
Authors: David Bayani
Date: 2022-06-03 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

We describe an extension of the Fanoos XAI system [Bayani et al 2022] which enables the system to learn the appropriate action to take in order to satisfy a user's request for description to be made more or less abstract. Specifically, descriptions of systems under analysis are stored in states, and in order to make a description more or less abstract, Fanoos selects an operator from a large library to apply to the state and generate a new description. Prior work on Fanoos predominately used hand-written methods for operator-selection; this current work allows Fanoos to leverage experience to learn the best operator to apply in a particular situation, balancing exploration and exploitation, leveraging expert insights when available, and utilizing similarity between the current state and past states. Additionally, in order to bootstrap the learning process (i.e., like in curriculum learning), we describe a simulated user which we implemented; this simulation allows Fanoos to gain general insights that enable reasonable courses of action, insights which later can be refined by experience with real users, as opposed to interacting with humans completely from scratch. Code implementing the methods described in the paper can be found at https://github/DBay-ani/Operator_Selection_Learning_Extensions_For_Fanoos.
Compositional Visual Generation with Composable Diffusion Models
Authors: Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, Joshua B. Tenenbaum
Date: 2022-06-03 00:00:00
Tasks: Sentence

Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible, they struggle to understand the composition of certain concepts, such as confusing the attributes of different objects or relations between objects. In this paper, we propose an alternative structured approach for compositional generation using diffusion models. An image is generated by composing a set of diffusion models, with each of them modeling a certain component of the image. To do this, we interpret diffusion models as energy-based models in which the data distributions defined by the energy functions may be explicitly combined. The proposed method can generate scenes at test time that are substantially more complex than those seen in training, composing sentence descriptions, object relations, human facial attributes, and even generalizing to new combinations that are rarely seen in the real world. We further illustrate how our approach may be used to compose pre-trained text-guided diffusion models and generate photorealistic images containing all the details described in the input descriptions, including the binding of certain object attributes that have been shown difficult for DALLE-2. These results point to the effectiveness of the proposed method in promoting structured generalization for visual generation. Project page: https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/