pwc-archive/papers-with-abstracts huggingface dataset:
Portfolio Optimization with Allocation Constraints and Stochastic Factor Market Dynamics
Authors: Marcos Escobar-Anel, Michel Kschonnek, Rudi Zagst
Date: 2023-03-17 00:00:00
Tasks: Portfolio Optimization

We study the expected utility portfolio optimization problem in an incomplete financial market where the risky asset dynamics depend on stochastic factors and the portfolio allocation is constrained to lie within a given convex set. We employ fundamental duality results from real constrained optimization to formally derive a dual representation of the associated HJB PDE. Using this representation, we provide a condition on the market dynamics and the allocation constraints, which ensures that the solution to the HJB PDE is exponentially affine and separable. This condition is used to derive an explicit expression for the optimal allocation-constrained portfolio up to a deterministic minimizer and the solution to a system of Riccati ODEs in a market with CIR volatility and in a market with multi-factor OU short rate.
Venture Capital Portfolio Construction and the Main Factors Impacting the Optimal Strategy
Authors: Francesco Farina, Mike Arpaia, Harpal Khing, Jonas Vetterle
Date: 2023-03-20 00:00:00

The optimal portfolio size for a venture capital (VC) fund is a topic often debated, but there is no consensus on the best strategy. This is because it is a function of many factors. It is not easy to find a general formula that can be applied to all situations, and it largely depends on the goal of the fund. In this report, we will go through the different factors step by step, studying how they affect fund returns and the optimal portfolio size, starting with some basic assumptions and then increasing the complexity of the model.
High-Frequency Volatility Estimation with Fast Multiple Change Points Detection
Authors: Greeshma Balabhadra, El Mehdi Ainasse, Pawel Polak
Date: 2023-03-19 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We propose a method for constructing sparse high-frequency volatility estimators that are robust against change points in the spot volatility process. The estimators we propose are $\ell_1$-regularized versions of existing volatility estimators. We focus on power variation estimators as they represent a fundamental class of volatility estimators. We establish consistency of these estimators for the true unobserved volatility and the change points locations, showing that minimax rates can be achieved for particular volatility estimators. The new estimators utilize the computationally efficient least angle regression algorithm for estimation purposes, followed by a reduced dynamic programming step to refine the final number of change points. In terms of numerical performance, these estimators are not only computationally fast but also accurately identify breakpoints near the end of the sample, both features highly desirable in today's electronic trading environment. In terms of out-of-sample volatility prediction, our new estimators provide more realistic and smoother volatility forecasts, outperforming a broad range of classical and recent volatility estimators across various frequencies and forecasting horizons.
Predictive Optimized Model on Money Markets Instruments With Capital Market and Bank Rates Ratio
Authors: Bilal Hungund, Shilpa Rastogi
Date: 2023-03-18 00:00:00
Tasks: Time Series

The money market and the capital market of the Indian financial markets have a symbiotic relationship in the development of the Indian economy. The nature and the characteristics of the markets differ to a large extent as the money market ensures liquidity in the system through the monetary policy by the regulators; capital markets propel and act as the engine driver for the economy in the long term. Therefore, the final throughput of the economy is the aggregation of the output of both the markets. Does that imply that the development of both markets is parallel in nature or is any one superior to the other or are they competitors? To understand the influence of one over the other the research was undertaken through a correlation matrix and time series model. A predictive model was further constructed for predicting the volume of money market instrument on the basis of fourteen days historical.
Optimal liquidation with temporary and permanent price impact, an application to cryptocurrencies
Authors: Hugo E. Ramirez, Julián Fernando Sanchez
Date: 2023-03-17 00:00:00

This paper studies the optimal liquidation of stocks in the presence of temporary and permanent price impacts, and we focus in the case of cryptocurrencies. We start by presenting analytical solutions to the problem with linear temporary impact, and linear and quadratic permanent impact. Then, using data from the order book of the BNB cryptocurrency, we estimate the functional form of the temporary and permanent price impact in three different scenarios: underestimation, overestimation and average estimation, finding different functional forms for each scenario. Using finite differences and optimal policy iteration, we solve the problem numerically and observe interesting changes in the optimal liquidation policy when applying calibrated linear and power forms for the temporary and permanent price impacts. Then, with these optimal policies, we identify optimal liquidation trajectories and simulate the liquidation of initial inventories to compare the performance among the optimal strategies under different parametrizations and against a naive strategy. Finally, we characterize the optimal policies based on the functional form of the inventory and find that policies generating the highest revenue are those starting with a low trading rate and increasing it as time passes.
Entropy-dissipation Informed Neural Network for McKean-Vlasov Type PDEs
Date: 2023-02-11 00:00:00
Proceeding: NeurIPS 2023 11

We extend the concept of self-consistency for the Fokker-Planck equation (FPE) to the more general McKean-Vlasov equation (MVE). While FPE describes the macroscopic behavior of particles under drift and diffusion, MVE accounts for the additional inter-particle interactions, which are often highly singular in physical systems. Two important examples considered in this paper are the MVE with Coulomb interactions and the vorticity formulation of the 2D Navier-Stokes equation. We show that a generalized self-consistency potential controls the KL-divergence between a hypothesis solution to the ground truth, through entropy dissipation. Built on this result, we propose to solve the MVEs by minimizing this potential function, while utilizing the neural networks for function approximation. We validate the empirical performance of our approach by comparing with state-of-the-art NN-based PDE solvers on several example problems.
Types of Approaches, Applications and Challenges in the Development of Sentiment Analysis Systems
Authors: Kazem Taghandiki, Elnaz Rezaei Ehsan
Date: 2023-03-09 00:00:00
Tasks: Sentiment Analysis

Today, the web has become a mandatory platform to express users' opinions, emotions and feelings about various events. Every person using his smartphone can give his opinion about the purchase of a product, the occurrence of an accident, the occurrence of a new disease, etc. in blogs and social networks such as (Twitter, WhatsApp, Telegram and Instagram) register. Therefore, millions of comments are recorded daily and it creates a huge volume of unstructured text data that can extract useful knowledge from this type of data by using natural language processing methods. Sentiment analysis is one of the important applications of natural language processing and machine learning, which allows us to analyze the sentiments of comments and other textual information recorded by web users. Therefore, the analysis of sentiments, approaches and challenges in this field will be explained in the following.
3D Face Reconstruction for Forensic Recognition -- A Survey
Authors: Simone Maurizio La Cava, Giulia Orrù, Tomáš Goldmann, Martin Drahansky, Gian Luca Marcialis
Date: 2023-02-03 00:00:00
Tasks: 3D Face Reconstruction, Face Reconstruction, Relation, Survey

3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role in bringing evidence to a lawsuit. Shedding some light on this matter is the goal of the present survey, where we start by clarifying the relation between forensic applications and biometrics. To our knowledge, no previous work adopted this relation to make the point on the state of the art. Therefore, we analyzed the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discussed the current obstacles that separate 3D face reconstruction from an active role in forensic applications.
Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)
Authors: Ming Li, Xinming Huang, Ziming Zhang
Date: 2023-02-05 00:00:00
Tasks: Representation Learning, Self-Supervised Learning, Vehicle Re-Identification

To learn distinguishable patterns, most of recent works in vehicle re-identification (ReID) struggled to redevelop official benchmarks to provide various supervisions, which requires prohibitive human labors. In this paper, we seek to achieve the similar goal but do not involve more human efforts. To this end, we introduce a novel framework, which successfully encodes both geometric local features and global representations to distinguish vehicle instances, optimized only by the supervision from official ID labels. Specifically, given our insight that objects in ReID share similar geometric characteristics, we propose to borrow self-supervised representation learning to facilitate geometric features discovery. To condense these features, we introduce an interpretable attention module, with the core of local maxima aggregation instead of fully automatic learning, whose mechanism is completely understandable and whose response map is physically reasonable. To the best of our knowledge, we are the first that perform self-supervised learning to discover geometric features. We conduct comprehensive experiments on three most popular datasets for vehicle ReID, i.e., VeRi-776, CityFlow-ReID, and VehicleID. We report our state-of-the-art (SOTA) performances and promising visualization results. We also show the excellent scalability of our approach on other ReID related tasks, i.e., person ReID and multi-target multi-camera (MTMC) vehicle tracking. The code is available at https://github.com/ ming1993li/Self-supervised-Geometric.
A Pervasive Framework for Human Detection and Tracking
Authors: Fesatidis Georgios, Bratsos Dimitrios, Kostas Kolomvatsos
Date: 2023-02-17 00:00:00
Tasks: Edge-computing, Human Detection

The advent of the Edge Computing (EC) leads to a huge ecosystem where numerous nodes can interact with data collection devices located close to end users. Human detection and tracking can be realized at edge nodes that perform the surveillance of an area under consideration through the assistance of a set of sensors (e.g., cameras). Our target is to incorporate the discussed functionalities to embedded devices present at the edge keeping their size limited while increasing their processing capabilities. In this paper, we propose two models for human detection accompanied by algorithms for tracing the corresponding trajectories. We provide the description of the proposed models and extend them to meet the challenges of the problem. Our evaluation aims at identifying models' accuracy while presenting their requirements to have them executed in embedded devices.
Towards CGAN-based Satellite Image Synthesis with Partial Pixel-Wise Annotation
Authors: Hadi Mansourifar, Steven J. Simske
Date: 2023-02-01 00:00:00
Tasks: Image Generation

Conditional Generative Adversarial Nets (CGANs) need a significantly huge dataset with a detailed pixel-wise annotation to generate high-quality images. Unfortunately, any amount of missing pixel annotations may significantly impact the result not only locally, but also in annotated areas. To the best of our knowledge, such a challenge has never been investigated in the broader field of GANs. In this paper, we take the first step in this direction to study the problem of CGAN-based satellite image synthesis given partially annotated images. We first define the problem of image synthesis using partially annotated data, and we discuss a scenario in which we face such a challenge. We then propose an effective solution called detail augmentation to address this problem. To do so, we tested two different approaches to augment details to compensate for missing pixel-wise annotations. In the first approach, we augmented the original images with their Canny edges to using the CGAN to compensate for the missing annotations. The second approach, however, attempted to assign a color to all pixels with missing annotation. Eventually, a different CGAN was trained to translate the new feature images into a final output.
Hematoxylin and eosin stained oral squamous cell carcinoma histological images dataset
Authors: Dalí F. D. dos Santos, Paulo R. de Faria, Adriano M. Loyola, Sérgio V. Cardoso, Bruno A. N. Travençolo, Marcelo Z. do Nascimento
Date: 2023-01-13 00:00:00
Tasks: Decision Making, Diagnostic, Image Segmentation, Segmentation, Semantic Segmentation

Computer-aided diagnosis (CAD) can be used as an important tool to aid and enhance pathologists' diagnostic decision-making. Deep learning techniques, such as convolutional neural networks (CNN) and fully convolutional networks (FCN), have been successfully applied in medical and biological research. Unfortunately, histological image segmentation is often constrained by the availability of labeled training data once labeling histological images for segmentation purposes is a highly-skilled, complex, and time-consuming task. This paper presents the hematoxylin and eosin (H&E) stained oral cavity-derived cancer (OCDC) dataset, a labeled dataset containing H&E-stained histological images of oral squamous cell carcinoma (OSCC) cases. The tumor regions in our dataset are labeled manually by a specialist and validated by a pathologist. The OCDC dataset presents 1,020 histological images of size 640x640 pixels containing tumor regions fully annotated for segmentation purposes. All the histological images are digitized at 20x magnification.
VideoSum: A Python Library for Surgical Video Summarization
Authors: Luis C. Garcia-Peraza-Herrera, Sebastien Ourselin, Tom Vercauteren
Date: 2023-02-15 00:00:00
Tasks: Video Summarization

The performance of deep learning (DL) algorithms is heavily influenced by the quantity and the quality of the annotated data. However, in Surgical Data Science, access to it is limited. It is thus unsurprising that substantial research efforts are made to develop methods aiming at mitigating the scarcity of annotated SDS data. In parallel, an increasing number of Computer Assisted Interventions (CAI) datasets are being released, although the scale of these remain limited. On these premises, data curation is becoming a key element of many SDS research endeavors. Surgical video datasets are demanding to curate and would benefit from dedicated support tools. In this work, we propose to summarize surgical videos into storyboards or collages of representative frames to ease visualization, annotation, and processing. Video summarization is well-established for natural images. However, state-of-the-art methods typically rely on models trained on human-made annotations, few methods have been evaluated on surgical videos, and the availability of software packages for the task is limited. We present videosum, an easy-to-use and open-source Python library to generate storyboards from surgical videos that contains a variety of unsupervised methods.
Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization
Authors: Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, Carsten Steger
Date: 2022-02-22 00:00:00
Proceeding: IJCV 2022 2
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

The unsupervised detection and localization of anomalies in natural images is an intriguing and challenging problem. Anomalies manifest themselves in very different ways and an ideal benchmark dataset for this task should contain representative examples for all of them. We find that existing datasets are biased towards local structural anomalies such as scratches, dents, or contaminations. In particular, they lack anomalies in the form of violations of logical constraints, e.g., permissible objects occurring in invalid locations. We contribute a new dataset based on industrial inspection scenarios that evenly covers both types of anomalies. We provide pixel-precise ground truth data for each anomalous region and define a generalized evaluation metric that addresses localization ambiguities that can arise for logical anomalies. Furthermore, we propose a novel algorithm that improves over the state of the art in the joint detection of structural and logical anomalies. It consists of a local and a global network branch. The first one inspects confined regions independent of their spatial locations in the input image and is primarily responsible for the detection of entirely new local structures. The second one learns a globally consistent representation of the training data through a bottleneck that enables the detection of violations of long-range dependencies, a key characteristic of many logical anomalies. We perform extensive evaluations on our new dataset to corroborate our claims.
Ranking via Sinkhorn Propagation
Authors: Ryan Prescott Adams, Richard S. Zemel
Date: 2011-06-09 00:00:00
Tasks: Information Retrieval, Retrieval

It is of increasing importance to develop learning methods for ranking. In contrast to many learning objectives, however, the ranking problem presents difficulties due to the fact that the space of permutations is not smooth. In this paper, we examine the class of rank-linear objective functions, which includes popular metrics such as precision and discounted cumulative gain. In particular, we observe that expectations of these gains are completely characterized by the marginals of the corresponding distribution over permutation matrices. Thus, the expectations of rank-linear objectives can always be described through locations in the Birkhoff polytope, i.e., doubly-stochastic matrices (DSMs). We propose a technique for learning DSM-based ranking functions using an iterative projection operator known as Sinkhorn normalization. Gradients of this operator can be computed via backpropagation, resulting in an algorithm we call Sinkhorn propagation, or SinkProp. This approach can be combined with a wide range of gradient-based approaches to rank learning. We demonstrate the utility of SinkProp on several information retrieval data sets.
Extracting Outcomes from Appellate Decisions in US State Courts
Authors: Alina Petrova, John Armour, Thomas Lukasiewicz
Date: 2020-12-09 00:00:00
Proceeding: JURIX 2020 12
Tasks: Attribute Extraction, legal outcome extraction, Text Classification

Predicting the outcome of a legal process has recently gained considerable research attention. Numerous attempts have been made to predict the exact outcome, judgment, charge, and fines of a case given the textual description of its facts and metadata. However, most of the effort has been focused on Chinese and European law, for which there exist annotated datasets. In this paper, we introduce CASELAW4 — a new dataset of 350k common law judicial decisions from the U.S. Caselaw Access Project, of which 250k have been automatically annotated with binary outcome labels of AFFIRM or REVERSE by our hybrid learning system. To our knowledge, it is the first attempt to perform outcome extraction (a) on such a large volume of English-language judicial opinions, (b) on the Caselaw Access Project data, and (c) on US State Courts of Appeal cases, and it paves the way to large-scale outcome prediction and advanced legal analytics using U.S. Case Law. We set up baseline results for the outcome extraction task on the new dataset, achieving an F-measure of 82.32%.
3D Human Mesh Estimation from Virtual Markers
Authors: Xiaoxuan Ma, Jiajun Su, Chunyu Wang, Wentao Zhu, Yizhou Wang
Date: 2023-03-21 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Denoising, Pose Estimation

Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion capture systems solve the problem by placing dense physical markers on the body surface, which allows to extract realistic meshes from their non-rigid motions. However, they cannot be applied to wild images without markers. In this work, we present an intermediate representation, named virtual markers, which learns 64 landmark keypoints on the body surface based on the large-scale mocap data in a generative style, mimicking the effects of physical markers. The virtual markers can be accurately detected from wild images and can reconstruct the intact meshes with realistic shapes by simple interpolation. Our approach outperforms the state-of-the-art methods on three datasets. In particular, it surpasses the existing methods by a notable margin on the SURREAL dataset, which has diverse body shapes. Code is available at https://github.com/ShirleyMaxx/VirtualMarker
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation
Authors: Seokju Cho, Heeseong Shin, Sunghwan Hong, Anurag Arnab, Paul Hongsuck Seo, Seungryong Kim
Date: 2023-03-21 00:00:00
Proceeding: CVPR 2024 1
Tasks: Image Segmentation, Open Vocabulary Semantic Segmentation, Open-Vocabulary Semantic Segmentation, Segmentation, Semantic Segmentation, text similarity

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work, we introduce a novel cost-based approach to adapt vision-language foundation models, notably CLIP, for the intricate task of semantic segmentation. Through aggregating the cosine similarity score, i.e., the cost volume between image and text embeddings, our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders, addressing the challenges faced by existing methods in handling unseen classes. Building upon this, we explore methods to effectively aggregate the cost volume considering its multi-modal nature of being established between image and text embeddings. Furthermore, we examine various methods for efficiently fine-tuning CLIP.
Non-Asymptotic Pointwise and Worst-Case Bounds for Classical Spectrum Estimators
Authors: Andrew Lamperski
Date: 2023-03-21 00:00:00
Tasks: Time Series

Spectrum estimation is a fundamental methodology in the analysis of time-series data, with applications including medicine, speech analysis, and control design. The asymptotic theory of spectrum estimation is well-understood, but the theory is limited when the number of samples is fixed and finite. This paper gives non-asymptotic error bounds for a broad class of spectral estimators, both pointwise (at specific frequencies) and in the worst case over all frequencies. The general method is used to derive error bounds for the classical Blackman-Tukey, Bartlett, and Welch estimators. In particular, these are first non-asymptotic error bounds for Bartlett and Welch estimators.