Miracle-dz/UniFAR-FTUs huggingface dataset:
Provably Fast and Space-Efficient Parallel Biconnectivity (Abstract)

We propose the first parallel biconnectivity algorithm (FAST-BCC) that has optimal work, polylogarithmic span, and is space-efficient. Our algorithm creates a skeleton graph based on any spanning tree of the input graph. Then we use the connectivity information of the skeleton to compute the biconnectivity of the original input. We carefully analyze the correctness of our algorithm. We implemented FAST-BCC and compared it with existing implementations, including GBBS, Slota and Madduri's algorithm, and the sequential Hopcroft-Tarjan algorithm. We tested them on a 96-core machine on 27 graphs with varying edge distributions. FAST-BCC is faster than all existing baselines on each graph.
The Excitation and Propagation of Elastic Waves (2nd edn): Cambridge Monographs on Mechanics and Applied Mathematics

J A Hudson 1984 Cambridge: Cambridge University Press viii + 224 pp price £9.95 (paperback) ISBN 0 521 31867 X Applied physics links apparently disparate areas of practical importance through the understanding of fundamental principles. This text is a good example, since practitioners interested in the mathematical framework which it presents range from seismologists and geophysical prospectors to engineers who use artificial means to generate elastic waves in solids in the search for cracks in the axles of vehicles or defects in silicon integrated circuits.
Efficient Construction of Directed Hopsets and Parallel Single-source Shortest Paths (Abstract)

The single-source shortest-path problem is as follows: given a graph with nonnegative edge weights and a designated source vertex s, return the distances from~s to each other vertex such. This paper presents a randomized parallel single-source shortest paths (SSSP) algorithm for directed graphs with non-negative integer edge weights that solves the exact SSSP problem in O (m) work and n^1/2+o(1) span, with high probability. All previous exact SSSP algorithms with nearly linear work have linear span, even for undirected unweighted graphs. To solve exact SSSP problem, we first show a deterministic reduction from exact SSSP to directed hopsets using the iterative gradual rounding technique. An (β, ε)-hopset is a set of weighted edges, also known as shortcuts, that when added to the graph, admit β-hop paths with weights no more than (1 + ε) times the true shortest path distances. We show that (β, ε)-hopsets can be used to solve the exact SSSP problem in O (m) work and O (β) span. Furthermore, we present the first nearly linear-work algorithm for constructing hopsets on directed graphs. Our sequential algorithm runs in O (m) time and constructs a hopset with O (n) edges and β = n^1/2+o(1) . We also provide a parallel version of the algorithm with O (m) work and n^1/2+o(1) span. The directed hopsets can be used to solve approximate SSSP problems efficiently, where the objective is to return estimates of the distances from the source vertex to every other vertex such that the estimate falls between the true distance and (1+ε) times the distance. Specifically, for constant ε and graphs with polynomially-bounded real edge weights, there is an algorithm solving approximate SSSP problem with O (m) work and n^1/2+o(1) span.
TIB AV-Analytics: A Web-based Platform for Scholarly Video Analysis and Film Studies

Video analysis platforms that integrate automatic solutions for multimedia and information retrieval enable various applications in many disciplines including film and media studies, communication science, and education. However, current platforms for video analysis either focus on manual annotations or include only a few tools for automatic content analysis. In this paper, we present a novel web-based video analysis platform called TIB AV-Analytics (TIB-AV-A). Unlike previous platforms, TIB-AV-A integrates state-of-the-art approaches in the fields of computer vision, audio analysis, and natural language processing for many relevant video analysis tasks. To facilitate future extensions and to ensure interoperability with existing tools, the video analysis approaches are implemented in a plugin structure with appropriate interfaces and import-export functions. TIB-AV-A leverages modern web technologies to provide users with a responsive and interactive web interface that enables manual annotation and provides access to powerful deep learning tools without a requirement for specific hardware dependencies. Source code and demo are publicly available at: https://service.tib. eu/tibava.
Adaptive Sliding Mode Force Tracking Approach for Unstructured Environment

This manuscript presents a novel force tracking scheme that focuses on solving the problem of compliant interaction during contact between robot and unstructured environment. Constant interaction force error compensation lacks adaptive capability for robot, which cannot effectively compensate for unstructured environment. Contrarily, the proposed adaptive variable impedance control method based on sliding mode structure (AVIS) can steadily track desired force during interaction process. Sliding mode control based on approximation law is used to adaptively modify damping and stiffness parameters of impedance control online, which continuously compensates for nonlinear dynamic system with high uncertainty. Stability and convergence of AVIS are proven. Experiments on diverse force interaction tasks show that stable and robust force tracking performance can be achieved by AVIS.
Bias in Reinforcement Learning: A Review in Healthcare Applications

Reinforcement learning (RL) can assist in medical decision making using patient data collected in electronic health record (EHR) systems. RL, a type of machine learning, can use these data to develop treatment policies. However, RL models are typically trained using imperfect retrospective EHR data. Therefore, if care is not taken in training, RL policies can propagate existing bias in healthcare. Literature that considers and addresses the issues of bias and fairness in sequential decision making are reviewed. The major themes to mitigate bias that emerge relate to (1) data management; (2) algorithmic design; and (3) clinical understanding of the resulting policies.
Context-Aware Modeling via Simulated Exposure Page for CTR Prediction

Click-through rate (CTR) prediction plays a crucial role in industrial recommendation and advertising systems, which generate and expose multiple items for each user request. Although the user's click action on an item will be affected by the other exposed items (called contextual items), current CTR prediction methods do not exploit this context because CTR prediction is performed before the contextual items are generated. This paper introduces a solution Contextual Items Simulation and Modeling (CISM) to tackle this limitation. Specifically, we propose a near-line Context Simulation Center to simulate exposure page without affecting online service latency, and an online Context Modeling Transformer to learn user-wise context from the simulated results w.r. t. the candidate item. In addition, knowledge distillation is introduced to further improve CTR prediction. Extensive experiments on both public and industrial datasets demonstrate the effectiveness of CISM. Currently, CISM has been deployed in the online display advertising system of Meituan Waimai, serving the main traffic.
HeteroCS: A Heterogeneous Community Search System With Semantic Explanation

Community search, which looks for query-dependent communities in a graph, is an important task in graph analysis. Existing community search studies address the problem by finding a densely-connected subgraph containing the query. However, many real-world networks are heterogeneous with rich semantics. Queries in heterogeneous networks generally involve in multiple communities with different semantic connections, while returning a single community with mixed semantics has limited applications. In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. We propose to automatically discover the query semantics to enable the search of different semantic communities and develop a comprehensive community evaluation model to support the ranking of results. We build HeteroCS, a heterogeneous community search system with semantic explanation, upon our semantic community model, and deploy it on two real-world graphs. We present a demonstration case to illustrate the novelty and effectiveness of the system.
Computational Versus Perceived Popularity Miscalibration in Recommender Systems

Popularity bias in recommendation lists refers to over-representation of popular content and is a challenge for many recommendation algorithms. Previous research has suggested several offline metrics to quantify popularity bias, which commonly relate the popularity of items in users' recommendation lists to the popularity of items in their interaction history. Discrepancies between these two factors are referred to as popularity miscalibration. While popularity metrics provide a straightforward and well-defined means to measure popularity bias, it is unknown whether they actually reflect users' perception of popularity bias. To address this research gap, we conduct a crowd-sourced user study on Prolific, involving 56 participants, to (1) investigate whether the level of perceived popularity miscalibration differs between common recommendation algorithms, (2) assess the correlation between perceived popularity miscalibration and its corresponding quantification according to a common offline metric. We conduct our study in a well-defined and important domain, namely music recommendation using the standardized LFM-2b dataset, and quantify popularity miscalibration of five recommendation algorithms by utilizing Jensen-Shannon distance (JSD). Challenging the findings of previous studies, we observe that users generally do perceive significant differences in terms of popularity bias between algorithms if this bias is framed as popularity miscalibration. In addition, JSD correlates moderately with users' perception of popularity, but not with their perception of unpopularity.
Encouraging Critical Thinking Support System: Question Generation and Lecture Slide Recommendations

In recent years, online learning has gained popularity in Japan, increasing opportunities for remote study using lecture slides. However, a common issue in Japanese education is that students tend to not ask questions in class. To address this issue, we propose an online learning support system informed by research on "critical thinking" that facilitates the process of students' questioning and thinking. In this study, we aimed to clarify the relationship between the questions generated by students and the class slides. We did this by analyzing the hierarchical relationship of the keywords that appear in the slides. We also propose a method of recommending appropriate lecture slides for the questions that we hope will encourage students to ask meaningful questions and engage in productive discussions.
SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval

Traditionally, sparse retrieval systems relied on lexical representations to retrieve documents, such as BM25, dominated information retrieval tasks. With the onset of pre-trained transformer models such as BERT, neural sparse retrieval has led to a new paradigm within retrieval. Despite the success, there has been limited software supporting different sparse retrievers running in a unified, common environment. This hinders practitioners from fairly comparing different sparse models and obtaining realistic evaluation results. Another missing piece is, that a majority of prior work evaluates sparse retrieval models on in-domain retrieval, i.e. on a single dataset: MS MARCO. However, a key requirement in practical retrieval systems requires models that can generalize well to unseen out-of-domain, i.e. zero-shot retrieval tasks. In this work, we provide SPRINT, a unified python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval. The toolkit currently includes five built-in models: uniCOIL, DeepImpact, SPARTA, TILDEv2 and SPLADEv2. Users can also easily add customized models by defining their term weighting method. Using our toolkit, we establish strong and reproducible zero-shot sparse retrieval baselines across the well-acknowledged benchmark, BEIR. Our results demonstrate that SPLADEv2 achieves the best average score of 0.470 nDCG@10 on BEIR amongst all neural sparse retrievers. In this work, we further uncover the reasons behind its performance gain. We show that SPLADEv2 produces sparse representations with a majority of tokens outside of the original query and document which is often crucial for its performance gains, i.e. a limitation among its other sparse counterparts. We provide our SPRINT toolkit, models, and data used in our experiments publicly here: https://github.com/thakur-nandan/sprint.
Developing a Prototype to Scale up Digital Support for Online Assessment Design

Educators rarely have access to just-in-time feedback and guiding heuristics when designing or updating assessments in higher education. This study describes the initial development process for an automated support system for designing high-quality online assessments. We identify key elements to embed in this digital artifact to offer just-in-time support for educators to design and evaluate their online assessments. We follow a design science approach in six stages, because it simultaneously generates knowledge about the method used to develop the artifact and the design of the artifact itself. Specifically, we focus on the early stages of problem identification, solution objectives, and initial conceptual design. After reviewing multiple assessment models and frameworks, we discuss a recent framework for evaluating and designing high-quality online assessments, consisting of ten design and contextual elements. This framework underpins the proposed solution which is a digital artifact that encourages consideration of alternate forms of assessment while retaining the flexibility to operate within individual educators' design practices and contexts. We expect the proposed system to help educators and instructional designers to better understand the strengths and weaknesses of their assessments, consider alternate forms of assessment, and incorporate the system into their assessment design process.
Acoustic Prediction of LAGOON Landing Gear: Cavity Noise and Coherent Structures

Detached-eddy simulations of the LAGOON landing gear configuration are performed using different unstructured meshes. One mesh is generated based on available experimental and numerical data, and based on this first mesh, a second one is carefully designed to improve the solutions in regions of strong unsteadiness. Results are presented in terms of steady and unsteady hydrodynamic data as well as noise predictions, which are performed using the Ffowcs-Williams and Hawkings formulation. Numerical results show good agreement with experimental data available in the literature. A wavelet transform analysis demonstrates that the sound generation in the cavity is an intermittent process that leads to strongly varying amplitudes of the surface pressure and the far-field noise radiation. A proper orthogonal decomposition is employed to identify coherent energetic structures responsible for the cavity tones. Finally, it is shown how the local flow topology affects the impingement of these coherent structures on th.
Evaluating Task-oriented Dialogue Systems with Users

Evaluation is one of the major concerns when developing information retrieval systems. Especially in the field of conversational AI, this topic has been heavily studied in the setting of both non-task and task-oriented conversational agents (dialogue systems). [1] Recently, several automatic metrics e.g., BLEU and ROUGE, proposed for the evaluation of dialogue systems, have shown poor correlation with human judgment and are thus ineffective for the evaluation of dialogue systems. As a consequence, a significant amount of research relies on human evaluation to estimate the effectiveness of dialogue systems[1, 4}. An emerging approach for evaluating task-oriented dialogue systems (TDS) is to estimate a user's overall satisfaction with the system from explicit and implicit user interaction signals [2, 3]. Though useful and effective, overall user satisfaction does not necessarily give insights into what aspects or dimensions a TDS is performing well on. Understanding why a user is satisfied or dissatisfied helps the TDS recover from an error and optimize towards an individual aspect to avoid total dissatisfaction during an interaction session. Understanding a user's satisfaction with TDS is crucial, mainly for two reasons. First, it allows system designers to understand different user perceptions regarding satisfaction, which in turn leads to better user personalization. Secondly, it can be used to avoid total dialogue failure by the system by deploying adaptive conversational approaches, such as failure recovery or switching topics. And, thus, fine-grained evaluation of TDS gives the system an opportunity to learn an individual user's interaction preferences leading to a fulfilled user goal. Therefore in this research, we take the first initiative toward understanding user satisfaction with TDS. We mainly focus on the fine-grained evaluation of conversational systems in a task-oriented setting.
MDKG: Graph-Based Medical Knowledge-Guided Dialogue Generation

Medical dialogue systems (MDS) have shown promising abilities to diagnose through a conversation with a patient like a human doctor would. However, current systems are mostly based on sequence modeling, which does not account for medical knowledge. This makes the systems more prone to misdiagnosis in case of diseases with limited information. To overcome this issue, we present MDKG, an end-to-end dialogue system for medical dialogue generation (MDG) specifically designed to adapt to new diseases by quickly learning and evolving a meta-knowledge graph that allows it to reason about disease-symptom correlations. Our approach relies on a medical knowledge graph to extract disease-symptom relationships and uses a dynamic graph-based meta-learning framework to learn how to evolve the given knowledge graph to reason about disease-symptom correlations. Our approach incorporates medical knowledge and hence reduces the need for a large number of dialogues. Evaluations show that our system outperforms existing approaches when tested on benchmark datasets.
Learning Query-aware Embedding Index for Improving E-commerce Dense Retrieval

The embedding index has become an essential part of the dense retrieval (DR) system, which enables a fast search for billion of items in online E-commerce applications. To accelerate the retrieval process in industrial scenarios, most of the previous studies only utilize item embeddings. However, the product quantization process without query embeddings will lead to inconsistency between queries and items. A straightforward solution is to put query embedding into the product quantization process. But we found that the distance of the positive query and item embedding pairs is too large, which means the query and item embeddings learned by the two-tower are not fully aligned. This problem would lead to performance decay when directly putting query embeddings into the product quantization. In this paper, we propose a novel query-aware embedding Index framework, which aligns the query and item embedding space to reduce the distance between positive pairs, thereby mixing the query and item embeddings to learn better cluster centers for product quantization. Specifically, we first propose s symmetric loss to train a better two-tower to achieve space alignment. Subsequently, we propose a mixed quantization strategy to put the query embeddings into the product quantization process for bridging the gap between queries and compressed item embeddings. Extensive experiments show that our framework significantly outperforms previous models on a real-world dataset, which demonstrates the superiority and effectiveness of the framework.
Dynamics and synchronization of a novel 4D-hyperjerk autonomous chaotic system with a Van der Pol nonlinearity

Abstract In the literature, hyperjerk systems raised up meaningful interest due to their simple and elegant structure as well as their complex dynamical features. In this work, we propose a novel 4D autonomous hyperjerk system which the particularity resides on the type of its nonlinearity namely the Van der Pol nonlinearity. The dynamics of this hyperjerk system is assessed thanks to the well-known nonlinear dynamic tools such as time series, bifurcation diagrams, Lyapunov exponent spectrum, two-parameter phase diagram, and phase portraits. As important result, it is established that the system presents a particular phenomenon of hysteretic dynamics that leads to the coexistence of attractors. Next, through the calculation of the Hamiltonian energy, we show that this latter depends on all the variables of the novel hyperjerk system. Furthermore, basing on an adaptive backstepping method whose target is a function of the states of the error system, a new controller is designed to carry out from t = 30, complete chaotic synchronization of the identical novel hyperjerk chaotic systems. Likewise, PSpice (9.2 full version) based simulations are presented in detail to confirm the feasibility of the theoretical model. One of the key points of this work is the designing in PSpice environment of this new adaptive backstepping controller to validate both theoretical and numerical synchronization results. Finally, our experimental measurements in the laboratory are in good agreement with the numerical and analog results.
Truncated protein isoforms generate diversity of protein localization and function in yeast

Genome-wide measurements of ribosome occupancy on mRNA transcripts have enabled global empirical identification of translated regions. These approaches have revealed an unexpected diversity of protein products, but high-confidence identification of new coding regions that entirely overlap annotated coding regions – including those that encode truncated protein isoforms – has remained challenging. Here, we develop a sensitive and robust algorithm focused on identifying N-terminally truncated proteins genome-wide, identifying 388 truncated protein isoforms, a more than 30-fold increase in the number known in budding yeast. We perform extensive experimental validation of these truncated proteins and define two general classes. The first set lack large portions of the annotated protein sequence and tend to be produced from a truncated transcript. We show two such cases, Yap5truncation and Pus1truncation, to have condition-specific regulation and functions that appear distinct from their respective annotated isoforms. The second set of N-terminally truncated proteins lack only a small region of the annotated protein and are less likely to be regulated by an alternative transcript isoform. Many localize to different subcellular compartments than their annotated counterpart, representing a common strategy for achieving dual localization of otherwise functionally identical proteins.