Miracle-dz/UniFAR-FTUs huggingface dataset:
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize

In this paper, we propose a new, simplified high probability analysis of AdaGrad for smooth, non-convex problems. More specifically, we focus on a particular accelerated gradient (AGD) template (Lan, 2020), through which we recover the original AdaGrad and its variant with averaging, and prove a convergence rate of $\mathcal O (1/ \sqrt{T})$ with high probability without the knowledge of smoothness and variance. We use a particular version of Freedman's concentration bound for martingale difference sequences (Kakade&Tewari, 2008) which enables us to achieve the best-known dependence of $\log (1 / \delta )$ on the probability margin $\delta$. We present our analysis in a modular way and obtain a complementary $\mathcal O (1 / T)$ convergence rate in the deterministic setting. To the best of our knowledge, this is the first high probability result for AdaGrad with a truly adaptive scheme, i.e., completely oblivious to the knowledge of smoothness and uniform variance bound, which simultaneously has best-known dependence of $\log( 1/ \delta)$. We further prove noise adaptation property of AdaGrad under additional noise assumptions.
drsphelps at SemEval-2022 Task 2: Learning idiom representations using BERTRAM

This paper describes our system for SemEval-2022 Task 2 Multilingual Idiomaticity Detection and Sentence Embedding sub-task B. We modify a standard BERT sentence transformer by adding embeddings for each idiom, which are created using BERTRAM and a small number of contexts. We show that this technique increases the quality of idiom representations and leads to better performance on the task. We also perform analysis on our final results and show that the quality of the produced idiom embeddings is highly sensitive to the quality of the input contexts.
Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images

— The new alternative is to use deep learning to inpaint any image by utilizing image classification and computer vision techniques. In general, image inpainting is a task of recreating or reconstructing any broken image which could be a photograph or oil/acrylic painting. With the advancement in the field of Artificial Intelligence, this topic has become popular among AI enthusiasts. With our approach, we propose an initial end-to-end pipeline for inpainting images using a complete Machine Learning approach instead of a conventional application-based approach. We first use the YOLO model to automatically identify and localize the object we wish to remove from the image. Using the result obtained from the model we can generate a mask for the same. After this, we provide the masked image and original image to the GAN model which uses the Contextual Attention method to fill in the region. It consists of two generator networks and two discriminator networks and is also called a coarse-to-fine network structure. The two generators use fully convolutional networks while the global discriminator gets hold of the entire image as input while the local discriminator gets the grip of the filled region as input. The contextual Attention mechanism is proposed to effectively borrow the neighbor information from distant spatial locations for reconstructing the missing pixels. The third part of our implementation uses SRGAN to resolve the inpainted image back to its original size. Our work is inspired by the paper Free-Form Image Inpainting with Gated Convolution and Generative Image Inpainting with Contextual Attention.
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory

Knowledge-grounded conversation (KGC) shows great potential in building an engaging and knowledgeable chatbot, and knowledge selection is a key ingredient in it. However, previous methods for knowledge selection only concentrate on the relevance between knowledge and dialogue context, ignoring the fact that age, hobby, education and life experience of an interlocutor have a major effect on his or her personal preference over external knowledge. Without taking the personalization issue into account, it is difficult for existing dialogue systems to select the proper knowledge and generate persona-consistent responses.In this work, we introduce personal memory into knowledge selection in KGC to address the personalization issue. We propose a variational method to model the underlying relationship between one’s personal memory and his or her selection of knowledge, and devise a learning scheme in which the forward mapping from personal memory to knowledge and its inverse mapping is included in a closed loop so that they could teach each other. Experiment results show that our methods outperform existing KGC methods significantly on both automatic evaluation and human evaluation.
SEAL: A Large-scale Video Dataset of Multi-grained Spatio-temporally Action Localization

In spite of many dataset eﬀorts for human action recognition, current computer vision algorithms are still limited to coarse-grained spatial and temporal annotations among human daily life. In this paper, we introduce a novel large-scale video dataset dubbed SEAL for multi-grained Spatio-tEmporal Action Localization. SEAL consists of two kinds of annotations, SEAL Tubes and SEAL Clips . We observe that atomic actions can be combined into many complex activities. SEAL Tubes provide both atomic action and complex activity annotations in tubelet level, producing 49.6k atomic actions spanning 172 action categories and 17.7k complex activities spanning 200 activity categories. SEAL Clips localizes atomic actions in space during two-second clips, producing 510.4k action labels with multiple labels per person. Exten-sive experimental results show that SEAL signiﬁcantly helps to advance video understanding. performance of feature learned on SEAL Clips and the great eﬀect of the atomic feature on complex activity localization task in tubelet level. Also, we demon-strated that denser spatial annotations can improve the performance of spatiotemporal classiﬁcation. SEAL Tubes provide a benchmark for tubelet-level action detection. We propose a baseline method and hope the new challenges posed by SEAL will spur researches on spatio-temporal action localization.
Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning

Text matching is a fundamental technique in both information retrieval and natural language processing. Text matching tasks share the same paradigm that determines the relationship between two given texts. The relationships vary from task to task, e.g. relevance in document retrieval, semantic alignment in paraphrase identification and answerable judgment in question answering. However, the essential signals for text matching remain in a finite scope, i.e. exact matching, semantic matching, and inference matching. Ideally, a good text matching model can learn to capture and aggregate these signals for different matching tasks to achieve competitive performance, while recent state-of-the-art text matching models, e.g. Pre-trained Language Models (PLMs), are hard to generalize. It is because the end-to-end supervised learning on task-specific dataset makes model overemphasize the data sample bias and task-specific signals instead of the essential matching signals, which ruins the generalization of model to different tasks. To overcome this problem, we adopt a specialization-generalization training strategy and refer to it as Match-Prompt. In specialization stage, descriptions of different matching tasks are mapped to only a few prompt tokens. In generalization stage, text matching model explores the essential matching signals by being trained on diverse multiple matching tasks. High diverse matching tasks avoid model fitting the data sample bias on a specific task, so that model can focus on learning the essential matching signals. Meanwhile, the prompt tokens obtained in the first step are added to the corresponding tasks to help the model distinguish different task-specific matching signals, as well as to form the basis prompt tokens for a new matching task. In this paper, we consider five common text matching tasks including document retrieval, open-domain question answering, retrieval-based dialogue, paraphrase identification, and natural language inference. Experimental results on eighteen public datasets show that Match-Prompt can improve multi-task generalization capability of PLMs in text matching and yield better in-domain multi-task, out-of-domain multi-task and new task adaptation performance than multi-task and task-specific models trained by previous fine-tuning paradigm.
HRTF measurement for accurate sound localization cues

A new database of head-related transfer functions (HRTFs) for accurate sound source localization is presented through precise measurement and post-processing in terms of improved frequency bandwidth and causality of head-related impulse responses (HRIRs) for accurate spectral cue (SC) and interaural time difference (ITD), respectively. The improvement effects of the proposed methods on binaural sound localization cues were investigated. To achieve sufficient frequency bandwidth with a single source, a one-way sealed speaker module was designed to obtain wide band frequency response based on electro-acoustics, whereas most existing HRTF databases rely on a two-way vented loudspeaker that has multiple sources. The origin transfer function at the head center was obtained by the proposed measurement scheme using a 0˚ on-axis microphone to ensure accurate spectral cue pattern of HRTFs, whereas in the previous measurements with a 9 0˚ off-axis microphone, the magnitude response of the origin transfer function fluctuated and decreased with increasing frequency, causing erroneous SCs of HRTFs. To prevent discontinuity of ITD due to non-causality of ipsilateral HRTFs, obtained HRIRs were circularly shifted by time delay considering the head radius of the measurement subject. Finally, various sound localization cues such as ITD, interaural level difference (ILD), SC, and horizontal plane directivity (HPD) were derived from the presented HRTFs, and improvements on binaural sound localization cues were examined. As a result, accurate SC patterns of HRTFs were confirmed through the proposed measurement scheme using the 0˚ on -axis microphone, and continuous ITD patterns were obtained due to the non-causality compensation. Source codes and presented HRTF database are available to relevant research groups at GitHub (https://github.com/han-saram/HRTF-HATS-KAIST).
Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations.
Nové trendy v marketingovej komunikácii

Cilem teto bakalařske prace je analýza a nasledne zhodnoceni nových trendů v marketingove komunikaci, ktere nam pomahaji přesněji zacilit na zakaznika a zjistit, jak jsou vnimany skupinou mladých lidi s rozdilnými zajmy a zalibami. Konkretně se tato prace zaměřuje na digitalni marketing, buzzmarketing a guerilový marketing, ktere se budou porovnavat s tradicnimi způsoby marketingove komunikace. Pod pojmem tradicni jsou mysleny media jako napřiklad tisk, reklama v televizi a radiu, ktere již v teto době nepřinaseji takový velký užitek a pomalu ztraceji na sve sile. Pro empirickou cast je zvolen kvantitativni výzkum formou dotaznikoveho průzkumu. Výzkumným souborem jsou hlavně studenti prvniho a druheho rocniku bakalařskeho studia. V zavěru teto prace jsou uvedeny výsledky dotaznikoveho průzkumu.
Cloud-based Virtual Reality Integrated Automatic Presentation Script for Understanding Urban Design Concepts in the Consensus Process A Case Study of One Foundation's Disaster Prevention Park in China

: In recent years, designers have used various types of tools, such as public Participation GIS (PP GIS) and Virtual Reality Modelling Language (VRML), to improve urban design concept understanding in the consensus process. However, these tools were frequently criticized as being too complex for the majority of potential users. Moreover, due to the limitations of data compression, hardware performance, network bandwidth and costs of current virtual reality platforms, the users need to gather in the same place and at a certain scheduled time in different phases, or download the virtual environment and avatar models from the server through the Internet, then host on the local computer. Cloud-based virtual reality technology is acknowledged as a powerful tool, which provides a communication platform for users. In this research, we contribute an example of a cloud-based virtual reality (Cloud-based VR) integrated automatic presentation script (APS) for providing a broader way for users to participate and understand urban design concepts in the consensus process. We took One Foundation’s Disaster Prevention Park in China as a case study, and built a Cloud-based VR platform to propose design concepts, and create an APS for auxiliary guidance of users toward understanding the concepts of urban design and deliberate design alternatives in a design report meeting through the Internet. The application results show that the Cloud-based VR (integrated APS) platform proposed in this paper can not only simplify the discussion of design concepts without the limitation of space and time, but also can improve the efficiency of discussion around design alternatives to reach a consensus without any extra expense.
Receptor Binding Conformation Regulate Leukocyte Ig-Like HLA Class I Allelic Sequence and

,1 Leukocyte Ig-like receptors (LILRs) are a family of innate immune receptors predominantly expressed by myeloid cells that can alter the Ag presentation properties of macrophages and dendritic cells. Several LILRs bind HLA class I. Altered LILR recognition due to HLA allelic variation could be a contributing factor in disease. We comprehensively assessed LILR binding to >90 HLA class I alleles. The inhibitory receptors LILRB1 and LILRB2 varied in their level of binding to different HLA alleles, correlating in some cases with speciﬁc amino acid motifs. LILRB2 displayed the weakest binding to HLA-B*2705, an allele genetically associated with several autoimmune conditions and delayed progression of HIV infection. We also assessed the effect of HLA class I conformation on LILR binding. LILRB1 exclusively bound folded b 2 -microglobulin–associated class I, whereas LILRB2 bound both folded and free H chain forms. In contrast, the activating receptor LILRA1 and the soluble LILRA3 protein displayed a preference for binding to HLA-C free H chain. To our knowledge, this is the ﬁrst study to identify the ligand of LILRA3. These ﬁndings support the hypothesis that LILR-mediated detection of unfolded versus folded MHC modulates immune responses during infection or inﬂammation. The Journal of Immunology , 2011, 186: 000–000. uman leukocyte Ag class I proteins direct the functions of both adaptive and innate immunity through their recognition by the TCR and leukocyte receptor complex-encoded receptors, which include members of the killer Ig-like receptor (KIR) and leukocyte Ig-like receptor (LILR) families. LILRs expressed on professional
Augmenting BiDAF with Components from R-NET for Question and Answering on SQUAD 2.0

The Stanford Question Answering Dataset (SQuAD2.0) [1] is a reading comprehension dataset of question-passage pairs, where the answer to the question is some segment of the passage. In this paper, we use the Bidirectional Attention Flow (BiDAF) model [2] for question answering. We augment the BiDAF model with both gated attention and self-matching attention, as well as character embeddings as outlined in R-NET [3]. We experimented with different variations of character embeddings as well as hyperparameters for finetuning. We found the most optimal model was with gated-self-attention, 2 CNN character embeddings with kernal sizes 3 and 5, and with batch size of 72 and dropout of 0.1 throughout the model. This model achieved F1: 65.869 and EM: 62.367 on the test set.
Photometric Based Loop Closure Correction and Pose Graph Optimization With Non-linear Factor Recovery

—The purpose of this research work is to investigate some information matrix into PGO (pose graph optimization). Considering the fundamental challenges associated with this problem, such as the null-space nature of SLAM system and the ambiguity of the scale drift during monocular visual odometry. NFR(Nonlinear Factor Recovery) was introduced to extract an information matrix from a rank deﬁcient marginalized hessian matrix such that a set of non-linear factors was reconstructed that make an optimal approximation of the information on the pose graph. Then, we proposed a photometric based Sim(3) relative pose estimation. In experiments on a public benchmark, we demonstrate a competitive performance of our method over the LDSO original approach.
Efficient NLP

The amount of computation put into training NLP models has grown tremendously in recent years. This trend raises the bar for participation in NLP research, excluding large parts of the community from experimenting with state-of-the-art models. It also creates environmental concerns since this computation uses increasing amounts of energy. This document is the report of a working group appointed by the ACL Executive Committee to promote ways that the ACL community can reduce the computational costs of NLP and thereby mitigate some of these concerns. The recommendations in this report are guided in part by the results of a survey we conducted with the ACL community 1 as well as a presentation of the main ideas during the ACL 2021 business meeting. 2 Below we provide a summary of our recommendations, followed by a background section, a detailed description of our recommendations, and a list of additional ideas proposed by members of the community.
How miRNAs Regulate Schwann Cells during Peripheral Nerve Regeneration—A Systemic Review

A growing body of studies indicate that small noncoding RNAs, especially microRNAs (miRNA), play a crucial role in response to peripheral nerve injuries. During Wallerian degeneration and regeneration processes, they orchestrate several pathways, in particular the MAPK, AKT, and EGR2 (KROX20) pathways. Certain miRNAs show specific expression profiles upon a nerve lesion correlating with the subsequent nerve regeneration stages such as dedifferentiation and with migration of Schwann cells, uptake of debris, neurite outgrowth and finally remyelination of regenerated axons. This review highlights (a) the specific expression profiles of miRNAs upon a nerve lesion and (b) how miRNAs regulate nerve regeneration by acting on distinct pathways and linked proteins. Shedding light on the role of miRNAs associated with peripheral nerve regeneration will help researchers to better understand the molecular mechanisms and deliver targets for precision medicine.
Can Pre-trained Language Models Understand Definitions?

Despite the great performance improvement on various tasks with pre-trained language models (PTLMs) in recent years, it remains unclear how much the models understand human languages. Motivated by the psycholinguistic fact that humans show great capability in concluding and describing concepts, we propose to examine the PTLMs’ ability to understand the definitions with a novel generalized word-sense matching task. Through proposing a novel task, word sense matching (WSM), we require PTLM-based models to find the alignment between embedding space of word-in-context and descriptive sentences capture the word senses. With extensive experiments, we further show that PTLMs can understand definitions through finding the semantic relevance between the contexts and definitions and fail when the negative samples have similar contexts or the definitions are abstract
IntrospectQA: Building Self-reflecting, Consistent Question Answering Models

Large pretrained language models (PTLMs) embed vast amounts of data and language patterns in their parameters, achieving state of the art performance on a variety of NLP tasks. Yet, these models often suffer from a fundamental weakness: they struggle to maintain logical consistency. We aim to improve the logical consistency of large pretrained language models (PTLMs) in the question answering (QA) setting as follows: (1) augment a pretrained QA model with an external memory for storing past model predictions and (2) integrate supervisory signals from a large pretrained NLI model to encourage consistency between past and future QA model predictions. Existing approaches for enforcing logical consistency rely on constraint solving algorithms like MAXSAT, which operate on hand-engineered constraint graphs specifying implications between facts: by utilizing a powerful NLI model to enforce consistency, our approach, IntrospectQA, removes the burden of constructing extensive constraint graphs and provides for generalization beyond a finite set of predefined logical clauses and entities. Our preliminary experiments indicate that IntrospectQA can boost performance over a baseline QA model over a wide variety of topics without any pretraining, finetuning, or external constraint graphs, suggesting that leveraging a pretrained NLI model is a potential avenue for improving the logical consistency and accuracy of a QA model.
Question Answering Augmentation System: Conditional Synonym Replacement

In this project, our team implemented Data Augmentation tools, such that we can distinguish and target different parts of speech and bolster the data with synonym replacements. We also explore whether augmenting just the context or augmenting the context, question, and answer provides better EM and F1 results. We hope to speed up data augmentation and experiment with the number of epochs for testing and the targeted words to augment