pwc-archive/papers-with-abstracts huggingface dataset:
Differential action of TIGIT on islet and peripheral nerve autoimmunity in the NOD mouse
Authors: Chantal Bécourt, Sandrine Luce, Ute C Rogner, Christian Boitard
Date: 2023-11-23 00:00:00

We previously demonstrated that the abrogation of the ICOS pathway prevents type 1 diabetes development in the Non Obese Diabetic (NOD) mouse, but results in a CD4+ T-cell dependent autoimmune neuromyopathy in aged mice. Pancreatic islet infiltrates in conventional NOD mice and neuromuscular infiltrates in Icosl-/- NOD mice have in common that they exhibit a strong enrichment in CD4+TIGIT+ T-cells, whilst TIGIT expression in the peripheral CD4+ T-cells is limited to the CD4+FoxP3+ T-cell population.When deleting Tigit on the NOD background, diabetes incidence was found increased. Peripheral CD4+CD226+ effector T-cells exhibited an increased frequency of IL-17 producing CD4+CD226+RORgt+ T-cells versus a decreased frequency of IFN$\gamma$-producing CD4+CD226+Tbet+ T-cells. ICOS is expressed in both CD4+FoxP3+ and CD4+CD226+ splenic T-cell subsets. Icosl deletion leads to a decrease of CD4+FoxP3+ cells, with decrease of PD1 but increase of ICOS and CCRX3. Also in the Icosl-/- model, CD4+CD226+ T-cells are decreased by Tigit deletion, and showed an increase of CD4+CD226+RORgt+ T-cells and a decrease of CD4+CD226+Tbet+ T-cells.However, deletion of Tigit in aged Icosl-/- NOD mice population did not increase the incidence of the autoimmune neuromyopathy observed in Icosl-/- NOD mice. Interestingly, the upregulation of CD4+CD226+RORgt+ T-cells was partly rescued.We conclude from our study that both Icosl and Tigit deletions on the NOD background lead to a shift between the ratio of IFN$\gamma$ and IL-17-producing CD4+CD226+ effector cells. The ICOS-dependent neuromyopathy development remains dominant and is not further altered in the absence of TIGIT.
Transitive inference as probabilistic preference learning
Authors: Francesco Mannella, Giovanni Pezzulo
Date: 2023-11-23 00:00:00

Transitive Inference (TI) is a cognitive task that assesses an organism's ability to infer novel relations between items based on previously acquired knowledge. TI is known for exhibiting various behavioral and neural signatures, such as the Serial Position Effect (SPE), Symbolic Distance Effect (SDE), and the brain's capacity to maintain and merge separate ranking models. We propose a novel framework that casts TI as a probabilistic preference learning task, using one-parameter Mallows models. We present a series of simulations that highlight the effectiveness of our novel approach. We show that the Mallows ranking model natively reproduces SDE and SPE. Furthermore, extending the model using Bayesian selection showcases its capacity to generate and merge ranking hypotheses as pairs with connecting symbols are encountered. Finally, we employ neural networks to replicate Mallows models, demonstrating how this framework aligns with observed prefrontal neural activity during TI. Our innovative approach sheds new light on the nature of TI, emphasizing the potential of probabilistic preference learning for unraveling its underlying neural mechanisms.
On the Relevance and Appropriateness of Name Concentration Risk Adjustments for Portfolios of Multilateral Development Banks
Authors: Eva Lütkebohmert, Julian Sester, Hongyi Shen
Date: 2023-11-23 00:00:00

Sovereign loan portfolios of Multilateral Development Banks (MDBs) typically consist of only a small number of borrowers and hence are heavily exposed to single name concentration risk. Based on realistic MDB portfolios constructed from publicly available data, this paper quantifies the magnitude of the exposure to name concentration risk using exact Monte Carlo simulations. In comparing the exact adjustment for name concentration risk to its analytic approximation as currently applied by the major rating agency Standard & Poor's, we further investigate whether current capital adequacy frameworks for MDBs are overly conservative. Finally, we discuss the choice of appropriate model parameters and their impact on measures of name concentration risk.
Set-valued intrinsic measures of systemic risk
Authors: Jana Hlavinova, Birgit Rudloff, Alexander Smirnow
Date: 2023-11-24 00:00:00

In recent years, it has become apparent that an isolated microprudential approach to capital adequacy requirements of individual institutions is insufficient. It can increase the homogeneity of the financial system and ultimately the cost to society. For this reason, the focus of the financial and mathematical literature has shifted towards the macroprudential regulation of the financial network as a whole. In particular, systemic risk measures have been discussed as a risk measurement and mitigation tool. In this spirit, we adopt a general approach of multivariate, set-valued risk measures and combine it with the notion of intrinsic risk measures. In order to define the risk of a financial position, intrinsic risk measures utilise only internal capital, which is received when part of the currently held assets are sold, instead of relying on external capital. We translate this methodology into the systemic framework and show that systemic intrinsic risk measures have desirable properties such as the set-valued equivalents of monotonicity and quasi-convexity. Furthermore, for convex acceptance sets we derive a dual representation of the systemic intrinsic risk measure. We apply our methodology to a modified Eisenberg-Noe network of banks and discuss the appeal of this approach from a regulatory perspective, as it does not elevate the financial system with external capital. We show evidence that this approach allows to mitigate systemic risk by moving the network towards more stable assets.
Class Gradient Projection For Continual Learning
Authors: Cheng Chen, Ji Zhang, Jingkuan Song, Lianli Gao
Date: 2023-11-25 00:00:00
Tasks: Continual Learning, Contrastive Learning

Catastrophic forgetting is one of the most critical challenges in Continual Learning (CL). Recent approaches tackle this problem by projecting the gradient update orthogonal to the gradient subspace of existing tasks. While the results are remarkable, those approaches ignore the fact that these calculated gradients are not guaranteed to be orthogonal to the gradient subspace of each class due to the class deviation in tasks, e.g., distinguishing "Man" from "Sea" v.s. differentiating "Boy" from "Girl". Therefore, this strategy may still cause catastrophic forgetting for some classes. In this paper, we propose Class Gradient Projection (CGP), which calculates the gradient subspace from individual classes rather than tasks. Gradient update orthogonal to the gradient subspace of existing classes can be effectively utilized to minimize interference from other classes. To improve the generalization and efficiency, we further design a Base Refining (BR) algorithm to combine similar classes and refine class bases dynamically. Moreover, we leverage a contrastive learning method to improve the model's ability to handle unseen tasks. Extensive experiments on benchmark datasets demonstrate the effectiveness of our proposed approach. It improves the previous methods by 2.0% on the CIFAR-100 dataset.
Continual Referring Expression Comprehension via Dual Modular Memorization
Authors: Heng Tao Shen, Cheng Chen, Peng Wang, Lianli Gao, Meng Wang, Jingkuan Song
Date: 2023-11-25 00:00:00
Tasks: Memorization, Referring Expression, Referring Expression Comprehension

Referring Expression Comprehension (REC) aims to localize an image region of a given object described by a natural-language expression. While promising performance has been demonstrated, existing REC algorithms make a strong assumption that training data feeding into a model are given upfront, which degrades its practicality for real-world scenarios. In this paper, we propose Continual Referring Expression Comprehension (CREC), a new setting for REC, where a model is learning on a stream of incoming tasks. In order to continuously improve the model on sequential tasks without forgetting prior learned knowledge and without repeatedly re-training from a scratch, we propose an effective baseline method named Dual Modular Memorization (DMM), which alleviates the problem of catastrophic forgetting by two memorization modules: Implicit-Memory and Explicit-Memory. Specifically, the former module aims to constrain drastic changes to important parameters learned on old tasks when learning a new task; while the latter module maintains a buffer pool to dynamically select and store representative samples of each seen task for future rehearsal. We create three benchmarks for the new CREC setting, by respectively re-splitting three widely-used REC datasets RefCOCO, RefCOCO+ and RefCOCOg into sequential tasks. Extensive experiments on the constructed benchmarks demonstrate that our DMM method significantly outperforms other alternatives, based on two popular REC backbones. We make the source code and benchmarks publicly available to foster future progress in this field: https://github.com/zackschen/DMM.
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation
Authors: Xun Liang, Shichao Song, Simin Niu, Zhiyu Li, Feiyu Xiong, Bo Tang, Yezhaohui Wang, Dawei He, Peng Cheng, Zhonghao Wang, Haiying Deng
Date: 2023-11-26 00:00:00
Tasks: Benchmarking, Hallucination, Hallucination Evaluation, Text Generation

Large language models (LLMs) have emerged as pivotal contributors in contemporary natural language processing and are increasingly being applied across a diverse range of industries. However, these large-scale probabilistic statistical models cannot currently ensure the requisite quality in professional content generation. These models often produce hallucinated text, compromising their practical utility in professional contexts. To assess the authentic reliability of LLMs in text generation, numerous initiatives have developed benchmark evaluations for hallucination phenomena. Nevertheless, these benchmarks frequently utilize constrained generation techniques due to cost and temporal constraints. These techniques encompass the use of directed hallucination induction and strategies that deliberately alter authentic text to produce hallucinations. These approaches are not congruent with the unrestricted text generation demanded by real-world applications. Furthermore, a well-established Chinese-language dataset dedicated to the evaluation of hallucinations in text generation is presently lacking. Consequently, we have developed an Unconstrained Hallucination Generation Evaluation (UHGEval) benchmark, designed to compile outputs produced with minimal restrictions by LLMs. Concurrently, we have established a comprehensive benchmark evaluation framework to aid subsequent researchers in undertaking scalable and reproducible experiments. We have also executed extensive experiments, evaluating prominent Chinese language models and the GPT series models to derive professional performance insights regarding hallucination challenges.
Improving Rumor Detection by Class-based Adversarial Domain Adaptation
Authors: Jingqiu Li, Lanjun Wang, Jianlin He, Yongdong Zhang, AnAn Liu
Date: 2023-10-01 00:00:00
Proceeding: MM '23: Proceedings of the 31st ACM International Conference on Multimedia 2023 10
Tasks: Domain Adaptation, Event Detection

Since rumors widely spread on social networks can cause seri- ous negative impacts, a batch of studies have investigated how to detect rumors. Most of them rely on existing datasets and try to improve the detection performance on those known datasets, but their performance drops significantly when detecting newly emerging events. This is not in line with the original intention of the rumor detection task. To tackle this issue, we formulate the rumor detection problem as a domain adaption task and propose the Class-based Adversarial Domain Adaptive framework, CADA, which is a general model framework where any latest rumor detec- tion methods can be plugged-in. The improvement of new emerging rumor event detection is based on adversarial training. Specifically, CADA considers class-based discriminators to achieve fine-grained alignment of declarations to be detected of different classes. Ex- periments on three public datasets show that CADA can improve the detection performance of existing rumor detection models and achieve better results than state-of-the-art models. In terms of ac- curacy, the performance is improved by at least 3% compared with the original base model in the PHEME dataset, and as high as 10% in the Twitter datasets.
Fake News Detection Through Temporally Evolving User Interactions
Authors: Shuzhi Gong, Richard Sinnott, Jianzhong Qi, Cecile Paris
Date: 2023-05-28 00:00:00
Proceeding: PAKDD: Advances in Knowledge Discovery and Data Mining 2023 5
Tasks: Fake News Detection

Detecting fake news on social media is an increasingly important problem, because of the rapid dissemination and detrimental impact of fake news. Graph-based methods that encode news propagation paths into tree structures have been shown to be effective. Existing studies based on such methods represent the propagation of news through static graphs or coarse-grained graph snapshots. They do not capture the full dynamics of graph evolution and hence the temporal news propagation patterns. To address this issue and model dynamic news propagation at a finer-grained level, we propose a temporal graph-based model. We join this model with a neural Hawkes process model to exploit the distinctive self-exciting patterns of true news and fake news on social media. This creates a highly effective fake news detection model that we named SEAGEN. Experimental results on real datasets show that SEAGEN achieves an accuracy of fake news detection of over 93% with an advantage of over 2.5% compared to other state-of-the-art models.
CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering
Authors: Haidong Zhu, Tianyu Ding, Tianyi Chen, Ilya Zharkov, Ram Nevatia, Luming Liang
Date: 2023-11-27 00:00:00
Tasks: Few-Shot Learning, NeRF, Neural Rendering

Generalizability and few-shot learning are key challenges in Neural Radiance Fields (NeRF), often due to the lack of a holistic understanding in pixel-level rendering. We introduce CaesarNeRF, an end-to-end approach that leverages scene-level CAlibratEd SemAntic Representation along with pixel-level representations to advance few-shot, generalizable neural rendering, facilitating a holistic understanding without compromising high-quality details. CaesarNeRF explicitly models pose differences of reference views to combine scene-level semantic representations, providing a calibrated holistic understanding. This calibration process aligns various viewpoints with precise location and is further enhanced by sequential refinement to capture varying details. Extensive experiments on public datasets, including LLFF, Shiny, mip-NeRF 360, and MVImgNet, show that CaesarNeRF delivers state-of-the-art performance across varying numbers of reference views, proving effective even with a single reference image.
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification
Authors: Renkai Wu, Yinghao Liu, Pengchen Liang, Qing Chang
Date: 2023-11-27 00:00:00
Tasks: Image Segmentation, Lesion Classification, Lesion Segmentation, Medical Image Classification, Medical Image Segmentation, Semantic Segmentation, Skin Lesion Segmentation

Computer-aided diagnosis of skin diseases is an important tool. However, the interpretability of computer-aided diagnosis is currently poor. Dermatologists and patients cannot intuitively understand the learning and prediction process of neural networks, which will lead to a decrease in the credibility of computer-aided diagnosis. In addition, traditional methods need to be trained using negative samples in order to predict the presence or absence of a lesion, but medical data is often in short supply. In this paper, we propose a multiple high-order attention interaction model (MHA-UNet) for use in a highly explainable skin lesion segmentation task. MHA-UNet is able to obtain the presence or absence of a lesion by explainable reasoning without the need for training on negative samples. Specifically, we propose a high-order attention interaction mechanism that introduces squeeze attention to a higher level for feature attention. In addition, a multiple high-order attention interaction (MHAblock) module is proposed by combining the different features of different orders. For classifying the presence or absence of lesions, we conducted classification experiments on several publicly available datasets in the absence of negative samples, based on explainable reasoning about the interaction of 5 attention orders of MHAblock. The highest positive detection rate obtained from the experiments was 81.0% and the highest negative detection rate was 83.5%. For segmentation experiments, comparison experiments of the proposed method with 13 medical segmentation models and external validation experiments with 8 state-of-the-art models in three public datasets and our clinical dataset demonstrate the state-of-the-art performance of our model. The code is available from https://github.com/wurenkai/MHA-UNet.
On Bringing Robots Home
Authors: Nur Muhammad Mahi Shafiullah, Anant Rai, Haritheja Etukuru, Yiqian Liu, Ishan Misra, Soumith Chintala, Lerrel Pinto
Date: 2023-11-27 00:00:00

Throughout history, we have successfully integrated various machines into our homes. Dishwashers, laundry machines, stand mixers, and robot vacuums are a few recent examples. However, these machines excel at performing only a single task effectively. The concept of a "generalist machine" in homes - a domestic assistant that can adapt and learn from our needs, all while remaining cost-effective - has long been a goal in robotics that has been steadily pursued for decades. In this work, we initiate a large-scale effort towards this goal by introducing Dobb-E, an affordable yet versatile general-purpose system for learning robotic manipulation within household settings. Dobb-E can learn a new task with only five minutes of a user showing it how to do it, thanks to a demonstration collection tool ("The Stick") we built out of cheap parts and iPhones. We use the Stick to collect 13 hours of data in 22 homes of New York City, and train Home Pretrained Representations (HPR). Then, in a novel home environment, with five minutes of demonstrations and fifteen minutes of adapting the HPR model, we show that Dobb-E can reliably solve the task on the Stretch, a mobile robot readily available on the market. Across roughly 30 days of experimentation in homes of New York City and surrounding areas, we test our system in 10 homes, with a total of 109 tasks in different environments, and finally achieve a success rate of 81%. Beyond success percentages, our experiments reveal a plethora of unique challenges absent or ignored in lab robotics. These range from effects of strong shadows, to variable demonstration quality by non-expert users. With the hope of accelerating research on home robots, and eventually seeing robot butlers in every home, we open-source Dobb-E software stack and models, our data, and our hardware designs at https://dobb-e.com
Street TryOn: Learning In-the-Wild Virtual Try-On from Unpaired Person Images
Authors: Aiyu Cui, Jay Mahajan, Viraj Shah, Preeti Gomathinayagam, Chang Liu, Svetlana Lazebnik
Date: 2023-11-27 00:00:00
Tasks: Image Generation, Semantic Segmentation, Virtual Try-on, Virtual Try-on (Model2Street), Virtual Try-on (Shop2Street), Virtual Try-on (Street2Street)

Most virtual try-on research is motivated to serve the fashion business by generating images to demonstrate garments on studio models at a lower cost. However, virtual try-on should be a broader application that also allows customers to visualize garments on themselves using their own casual photos, known as in-the-wild try-on. Unfortunately, the existing methods, which achieve plausible results for studio try-on settings, perform poorly in the in-the-wild context. This is because these methods often require paired images (garment images paired with images of people wearing the same garment) for training. While such paired data is easy to collect from shopping websites for studio settings, it is difficult to obtain for in-the-wild scenes. In this work, we fill the gap by (1) introducing a StreetTryOn benchmark to support in-the-wild virtual try-on applications and (2) proposing a novel method to learn virtual try-on from a set of in-the-wild person images directly without requiring paired data. We tackle the unique challenges, including warping garments to more diverse human poses and rendering more complex backgrounds faithfully, by a novel DensePose warping correction method combined with diffusion-based conditional inpainting. Our experiments show competitive performance for standard studio try-on tasks and SOTA performance for street try-on and cross-domain try-on tasks.
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Authors: Munan Ning, Bin Zhu, Yujia Xie, Bin Lin, Jiaxi Cui, Lu Yuan, Dongdong Chen, Li Yuan
Date: 2023-11-27 00:00:00
Tasks: Decision Making, Question Answering

Video-based large language models (Video-LLMs) have been recently introduced, targeting both fundamental improvements in perception and comprehension, and a diverse range of user inquiries. In pursuit of the ultimate goal of achieving artificial general intelligence, a truly intelligent Video-LLM model should not only see and understand the surroundings, but also possess human-level commonsense, and make well-informed decisions for the users. To guide the development of such a model, the establishment of a robust and comprehensive evaluation system becomes crucial. To this end, this paper proposes \textit{Video-Bench}, a new comprehensive benchmark along with a toolkit specifically designed for evaluating Video-LLMs. The benchmark comprises 10 meticulously crafted tasks, evaluating the capabilities of Video-LLMs across three distinct levels: Video-exclusive Understanding, Prior Knowledge-based Question-Answering, and Comprehension and Decision-making. In addition, we introduce an automatic toolkit tailored to process model outputs for various tasks, facilitating the calculation of metrics and generating convenient final scores. We evaluate 8 representative Video-LLMs using \textit{Video-Bench}. The findings reveal that current Video-LLMs still fall considerably short of achieving human-like comprehension and analysis of real-world videos, offering valuable insights for future research directions. The benchmark and toolkit are available at: \url{https://github.com/PKU-YuanGroup/Video-Bench}.