pwc-archive/papers-with-abstracts huggingface dataset:
Robust convex biclustering with a tuning-free method
Authors: Yifan Chen, Chunyin Lei, Chuanquan Li, Haiqiang Ma, Ningyuan Hu
Date: 2022-12-06 00:00:00

Biclustering is widely used in different kinds of fields including gene information analysis, text mining, and recommendation system by effectively discovering the local correlation between samples and features. However, many biclustering algorithms will collapse when facing heavy-tailed data. In this paper, we propose a robust version of convex biclustering algorithm with Huber loss. Yet, the newly introduced robustification parameter brings an extra burden to selecting the optimal parameters. Therefore, we propose a tuning-free method for automatically selecting the optimal robustification parameter with high efficiency. The simulation study demonstrates the more fabulous performance of our proposed method than traditional biclustering methods when encountering heavy-tailed noise. A real-life biomedical application is also presented. The R package RcvxBiclustr is available at https://github.com/YifanChen3/RcvxBiclustr.
Uniform-in-time propagation of chaos for mean field Langevin dynamics
Authors: Fan Chen, Zhenjie Ren, SongBo Wang
Date: 2022-12-06 00:00:00

We study the mean field Langevin dynamics and the associated particle system. By assuming the functional convexity of the energy, we obtain the $L^p$-convergence of the marginal distributions towards the unique invariant measure for the mean field dynamics. Furthermore, we prove the uniform-in-time propagation of chaos in both the $L^2$-Wasserstein metric and relative entropy.
Identification of Unobservables in Observations
Authors: Yingyao Hu
Date: 2022-12-05 00:00:00

In empirical studies, the data usually don't include all the variables of interest in an economic model. This paper shows the identification of unobserved variables in observations at the population level. When the observables are distinct in each observation, there exists a function mapping from the observables to the unobservables. Such a function guarantees the uniqueness of the latent value in each observation. The key lies in the identification of the joint distribution of observables and unobservables from the distribution of observables. The joint distribution of observables and unobservables then reveal the latent value in each observation. Three examples of this result are discussed.
A multi-language toolkit for the semi-automated checking of research outputs
Authors: Richard J. Preen, Maha Albashir, Simon Davy, Jim Smith
Date: 2022-12-06 00:00:00

This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. SACRO is a framework that applies best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers conduct their analyses. SACRO is designed to assist human checkers rather than seeking to replace them as with current automated rules-based approaches. The toolkit is composed of a lightweight Python package that sits over well-known analysis tools that produce outputs such as tables, plots, and statistical models. This package adds functionality to (i) automatically identify potentially disclosive outputs against a range of commonly used disclosure tests; (ii) apply optional disclosure mitigation strategies as requested; (iii) report reasons for applying SDC; and (iv) produce simple summary documents trusted research environment staff can use to streamline their workflow and maintain auditable records. This creates an explicit change in the dynamics so that SDC is something done with researchers rather than to them, and enables more efficient communication with checkers. A graphical user interface supports human checkers by displaying the requested output and results of the checks in an immediately accessible format, highlighting identified issues, potential mitigation options, and tracking decisions made. The major analytical programming languages used by researchers (Python, R, and Stata) are supported by providing front-end packages that interface with the core Python back-end. Source code, packages, and documentation are available under MIT license at https://github.com/AI-SDC/ACRO
Dataset vs Reality: Understanding Model Performance from the Perspective of Information Need
Authors: Mengying Yu, Aixin Sun
Date: 2022-12-06 00:00:00
Tasks: Image Captioning, Information Retrieval, Question Answering, Retrieval

Deep learning technologies have brought us many models that outperform human beings on a few benchmarks. An interesting question is: can these models well solve real-world problems with similar settings (e.g., identical input/output) to the benchmark datasets? We argue that a model is trained to answer the same information need for which the training dataset is created. Although some datasets may share high structural similarities, e.g., question-answer pairs for the question answering (QA) task and image-caption pairs for the image captioning (IC) task, they may represent different research tasks aiming for answering different information needs. To support our argument, we use the QA task and IC task as two case studies and compare their widely used benchmark datasets. From the perspective of information need in the context of information retrieval, we show the differences in the dataset creation processes, and the differences in morphosyntactic properties between datasets. The differences in these datasets can be attributed to the different information needs of the specific research tasks. We encourage all researchers to consider the information need the perspective of a research task before utilizing a dataset to train a model. Likewise, while creating a dataset, researchers may also incorporate the information need perspective as a factor to determine the degree to which the dataset accurately reflects the research task they intend to tackle.
Fast Online Hashing with Multi-Label Projection
Authors: Wenzhe Jia, Yuan Cao, Junwei Liu, Jie Gui
Date: 2022-12-03 00:00:00
Tasks: Retrieval

Hashing has been widely researched to solve the large-scale approximate nearest neighbor search problem owing to its time and storage superiority. In recent years, a number of online hashing methods have emerged, which can update the hash functions to adapt to the new stream data and realize dynamic retrieval. However, existing online hashing methods are required to update the whole database with the latest hash functions when a query arrives, which leads to low retrieval efficiency with the continuous increase of the stream data. On the other hand, these methods ignore the supervision relationship among the examples, especially in the multi-label case. In this paper, we propose a novel Fast Online Hashing (FOH) method which only updates the binary codes of a small part of the database. To be specific, we first build a query pool in which the nearest neighbors of each central point are recorded. When a new query arrives, only the binary codes of the corresponding potential neighbors are updated. In addition, we create a similarity matrix which takes the multi-label supervision information into account and bring in the multi-label projection loss to further preserve the similarity among the multi-label data. The experimental results on two common benchmarks show that the proposed FOH can achieve dramatic superiority on query time up to 6.28 seconds less than state-of-the-art baselines with competitive retrieval accuracy.
Using reinforcement learning to find an optimal set of features
Authors: Seyed Mehdi Hazrati Fard, Ali Hamzeh, Sattar Hashemi
Date: 2013-12-01 00:00:00
Proceeding: Elsevier 2013 12
Tasks: feature selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Identifying the most characterizing features of observed data is critical for minimizing the classification error. Feature selection is the process of identifying a small subset of highly predictive features out of a large set of candidate features. In the literature, many feature selection methods approach the task as a search problem, where each state in the search space is a possible feature subset. In this study, we consider feature selection problem as a reinforcement learning problem in general and use a well-known method, temporal difference, to traverse the state space and select the best subset of features. Specifically, first, we consider the state space as a Markov decision process, and then we introduce an optimal graph search to overcome the complexity of the problem of concern. Since this approach needs a state evaluation paradigm as an aid to traverse the promising regions in the state space, the presence of a low-cost evaluation function is necessary. This method initially explores the lattice of feature sets, and then exploits the obtained experiments. Finally, two methods, based on filters and wrappers, are proposed for the ultimate selection of features. Our empirical evaluation shows that this strategy performs well in comparison with other commonly used feature selection strategies, while maintaining compatibility with all datasets in hand.
Bounded arbitrage and nearly rational behavior
Authors: Leandro Nascimento
Date: 2022-12-06 00:00:00
Tasks: Decision Making

We establish the equivalence between a principle of almost absence of arbitrage opportunities and nearly rational decision-making. The implications of such principle are considered in the context of the aggregation of probabilistic opinions and of stochastic choice functions. In the former a bounded arbitrage principle and its equivalent form as an approximately Pareto condition are shown to bound the difference between the collective probabilistic assessment of a set of states and a linear aggregation rule on the individual assessments. In the latter we show that our general principle of limited arbitrage opportunities translates into a weakening of the McFadden-Richter axiom of stochastic rationality, and gives an upper bound for the minimum distance of a stochastic choice function to another in the class of random utility maximization models.
A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks
Authors: Muhammad Yeasir Arafat, Sangman Moh
Date: 2021-06-16 00:00:00
Proceeding: IEEE Internet of Things Journal 2021 6
Tasks: Q-Learning

Flying ad hoc networks (FANETs) have emanated over the last few years for numerous civil and military applications. Owing to underlying attributes, such as a dynamic topology, node mobility in 3-D space, and the limited energy of unmanned aerial vehicles (UAVs), a routing protocol for FANETs is challenging to design. Exiting topology-based routing is unsuitable for highly dynamic FANETs. Location-based routing protocols can be preferred for FANETs owing to their scalability, but are based on one-hop neighbor information and do not contemplate the reachability of further appropriate nodes for forwarding. Owing to the rapid mobility of UAVs, the topology frequently changes; thus, some route entries in the routing table can become invalid and the next-hop nodes may be unavailable before a timeout. That is, the routing decision based on one-hop neighbors cannot assure a successful delivery. In this study, we propose a novel Q -learning-based topology-aware routing (QTAR) protocol for FANETs to provide reliable combinations between the source and destination. The proposed QTAR improves the routing decision by considering two-hop neighbor nodes, extending the local view of the network topology. With the Q -learning technique, QTAR adaptively adjusts the routing decision according to the network condition. Our simulation results reveal that QTAR outstrips the existing routing protocols in respect of various performance metrics under distinct scenarios.
A Simple and Robust Correlation Filtering Method for Text-based Person Search
Authors: Wei Suo, Mengyang Sun, Kai Niu, Yiqi Gao, Peng Wang, Yanning Zhang, Qi Wu
Date: 2022-11-04 00:00:00
Proceeding: ECCV 2022 2022 11
Tasks: Denoising, Person Search, Text based Person Retrieval, Text-based Person Retrieval, Text based Person Search

Text-based person search aims to associate pedestrian images with natural language descriptions. In this task, extracting differentiated representations and aligning them among identities and descriptions is an essential yet challenging problem. Most of the previous methods depend on additional language parsers or vision techniques to select the relevant regions or words from noise inputs. But there exists heavy computation cost and inevitable error accumulation. Meanwhile, simply using horizontal segmentation images to obtain local-level features would harm the reliability of models as well. In this paper, we present a novel end-to-end Simple and Robust Correlation Filtering (SRCF) method which can effectively extract key clues and adaptively align the discriminative features. Different from previous works, our framework focuses on computing the similarity between templates and inputs. In particular, we design two different types of filtering modules (i.e., denoising filters and dictionary filters) to extract crucial features and establish multi-modal mappings. Extensive experiments have shown that our method improves the robustness of the model and achieves better performance on the two text-based person search datasets. Source code is available at https://github.com/Suo-Wei/SRCF.
ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation
Authors: Ziqin Zhou, BoWen Zhang, Yinjie Lei, Lingqiao Liu, Yifan Liu
Date: 2022-12-07 00:00:00
Proceeding: CVPR 2023 1
Tasks: Semantic Segmentation, zero-shot-classification, Zero-Shot Learning, Zero-Shot Semantic Segmentation

Recently, CLIP has been applied to pixel-level zero-shot learning tasks via a two-stage scheme. The general idea is to first generate class-agnostic region proposals and then feed the cropped proposal regions to CLIP to utilize its image-level zero-shot classification capability. While effective, such a scheme requires two image encoders, one for proposal generation and one for CLIP, leading to a complicated pipeline and high computational cost. In this work, we pursue a simpler-and-efficient one-stage solution that directly extends CLIP's zero-shot prediction capability from image to pixel level. Our investigation starts with a straightforward extension as our baseline that generates semantic masks by comparing the similarity between text and patch embeddings extracted from CLIP. However, such a paradigm could heavily overfit the seen classes and fail to generalize to unseen classes. To handle this issue, we propose three simple-but-effective designs and figure out that they can significantly retain the inherent zero-shot capacity of CLIP and improve pixel-level generalization ability. Incorporating those modifications leads to an efficient zero-shot semantic segmentation system called ZegCLIP. Through extensive experiments on three public benchmarks, ZegCLIP demonstrates superior performance, outperforming the state-of-the-art methods by a large margin under both "inductive" and "transductive" zero-shot settings. In addition, compared with the two-stage method, our one-stage ZegCLIP achieves a speedup of about 5 times faster during inference. We release the code at https://github.com/ZiqinZhou66/ZegCLIP.git.
COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data
Authors: Jonas C. Ditz, Bernhard Reuter, Nico Pfeifer
Date: 2022-12-02 00:00:00

Motivation: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that are optimized for large datasets usually operate as black boxes. In high stakes scenarios, like healthcare, using a black-box model poses safety and security issues. Without an explanation about molecular factors and phenotypes that affected the prediction, healthcare providers are left with no choice but to blindly trust the models. We propose a new type of artificial neural network, named Convolutional Omics Kernel Network (COmic). By combining convolutional kernel networks with pathway-induced kernels, our method enables robust and interpretable end-to-end learning on omics datasets ranging in size from a few hundred to several hundreds of thousands of samples. Furthermore, COmic can be easily adapted to utilize multi-omics data. Results: We evaluated the performance capabilities of COmic on six different breast cancer cohorts. Additionally, we trained COmic models on multi-omics data using the METABRIC cohort. Our models performed either better or similar to competitors on both tasks. We show how the use of pathway-induced Laplacian kernels opens the black-box nature of neural networks and results in intrinsically interpretable models that eliminate the need for post-hoc explanation models.
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
Authors: Fangqi Zhu, Jun Gao, Changlong Yu, Wei Wang, Chen Xu, Xin Mu, Min Yang, Ruifeng Xu
Date: 2022-12-07 00:00:00
Tasks: Language Modeling, Language Modelling, Prediction

Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted to improve event correlation reasoning by using pretrained language models and incorporating external knowledge~(e.g., discourse relations). Though promising results have been achieved, some challenges still remain. First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well. Second, modeling correlations between events with discourse relations is limited because it can only capture explicit correlations between events with discourse markers, and cannot capture many implicit correlations. To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. Specifically, we first introduce a novel event-level blank infilling strategy as the learning objective to inject event-level knowledge into the pretrained language model, and then design a likelihood-based contrastive loss for fine-tuning the generative model. Instead of using an additional prediction layer, we perform prediction by using sequence likelihoods generated by the generative model. Our approach models correlations between events in a soft way without any external knowledge. The likelihood-based prediction eliminates the need to use additional networks to make predictions and is somewhat interpretable since it scores each word in the event. Experimental results on the multi-choice narrative cloze~(MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines. Our code will be available at https://github.com/zhufq00/mcnc.
Point Cloud Registration of non-rigid objects in sparse 3D Scans with applications in Mixed Reality
Authors: Manorama Jha
Date: 2022-12-07 00:00:00
Tasks: Mixed Reality, Object, Point Cloud Registration

Point Cloud Registration is the problem of aligning the corresponding points of two 3D point clouds referring to the same object. The challenges include dealing with noise and partial match of real-world 3D scans. For non-rigid objects, there is an additional challenge of accounting for deformations in the object shape that happen to the object in between the two 3D scans. In this project, we study the problem of non-rigid point cloud registration for use cases in the Augmented/Mixed Reality domain. We focus our attention on a special class of non-rigid deformations that happen in rigid objects with parts that move relative to one another about joints, for example, robots with hands and machines with hinges. We propose an efficient and robust point-cloud registration workflow for such objects and evaluate it on real-world data collected using Microsoft Hololens 2, a leading Mixed Reality device.
Discovering Latent Knowledge in Language Models Without Supervision
Authors: Collin Burns, Haotian Ye, Dan Klein, Jacob Steinhardt
Date: 2022-12-07 00:00:00
Tasks: Imitation Learning, Language Modelling, Negation, Question Answering

Existing techniques for training language models can be misaligned with the truth: if we train models with imitation learning, they may reproduce errors that humans make; if we train them to generate text that humans rate highly, they may output errors that human evaluators can't detect. We propose circumventing this issue by directly finding latent knowledge inside the internal activations of a language model in a purely unsupervised way. Specifically, we introduce a method for accurately answering yes-no questions given only unlabeled model activations. It works by finding a direction in activation space that satisfies logical consistency properties, such as that a statement and its negation have opposite truth values. We show that despite using no supervision and no model outputs, our method can recover diverse knowledge represented in large language models: across 6 models and 10 question-answering datasets, it outperforms zero-shot accuracy by 4\% on average. We also find that it cuts prompt sensitivity in half and continues to maintain high accuracy even when models are prompted to generate incorrect answers. Our results provide an initial step toward discovering what language models know, distinct from what they say, even when we don't have access to explicit ground truth labels.
Toward Multi-Service Edge-Intelligence Paradigm: Temporal-Adaptive Prediction for Time-Critical Control over Wireless
Authors: Adnan Aijaz, Nan Jiang, Aftab Khan
Date: 2022-12-07 00:00:00
Tasks: Edge-computing

Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as packet losses, synchronization errors, and varying delays have a detrimental effect on performance of real-time control, often with safety implications. This paper introduces multi-service edge-intelligence as a new paradigm for realizing time-critical control over wireless. It presents the concept of multi-service edge-intelligence which revolves around tight integration of wireless access, edge-computing and machine learning techniques, in order to provide stability guarantees under wireless imperfections. The paper articulates some of the key system design aspects of multi-service edge-intelligence. It also presents a temporal-adaptive prediction technique to cope with dynamically changing wireless environments. It provides performance results in a robotic teleoperation scenario. Finally, it discusses some open research and design challenges for multi-service edge-intelligence.