Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Scalability in Recommender Systems through Lottery Ticket Hypothesis and Knowledge Distillation-based Neural Network Pruning

This study introduces an innovative approach aimed at the efficient pruning of neural networks, with a particular focus on their deployment on edge devices. Our method involves the integration of the Lottery Ticket Hypothesis (LTH) with the Knowledge Distillation (KD) framework, resulting in the formulation of three distinct pruning models. These models have been developed to address scalability issue in recommender systems, whereby the complexities of deep learning models have hindered their practical deployment. With judicious application of the pruning techniques, we effectively curtail the power consumption and model dimensions without compromising on accuracy. Empirical evaluation has been performed using two real world datasets from diverse domains against two baselines. Gratifyingly, our approaches yielded a GPU computation-power reduction of up to 66.67%. Notably, our study contributes to the field of recommendation system by pioneering the application of LTH and KD.
A2Q+: Improving Accumulator-Aware Weight Quantization

Quantization techniques commonly reduce the inference costs of neural networks by restricting the precision of weights and activations. Recent studies show that also reducing the precision of the accumulator can further improve hardware efficiency at the risk of numerical overflow, which introduces arithmetic errors that can degrade model accuracy. To avoid numerical overflow while maintaining accuracy, recent work proposed accumulator-aware quantization (A2Q), a quantization-aware training method that constrains model weights during training to safely use a target accumulator bit width during inference. Although this shows promise, we demonstrate that A2Q relies on an overly restrictive constraint and a sub-optimal weight initialization strategy that each introduce superfluous quantization error. To address these shortcomings, we introduce: (1) an improved bound that alleviates accumulator constraints without compromising overflow avoidance; and (2) a new strategy for initializing quantized weights from pre-trained floating-point checkpoints. We combine these contributions with weight normalization to introduce A2Q+. We support our analysis with experiments that show A2Q+ significantly improves the trade-off between accumulator bit width and model accuracy and characterize new trade-offs that arise as a consequence of accumulator constraints.
Adversarially Robust Signed Graph Contrastive Learning from Balance Augmentation

Signed graphs consist of edges and signs, which can be separated into structural information and balance-related information, respectively. Existing signed graph neural networks (SGNNs) typically rely on balance-related information to generate embeddings. Nevertheless, the emergence of recent adversarial attacks has had a detrimental impact on the balance-related information. Similar to how structure learning can restore unsigned graphs, balance learning can be applied to signed graphs by improving the balance degree of the poisoned graph. However, this approach encounters the challenge"Irreversibility of Balance-related Information"- while the balance degree improves, the restored edges may not be the ones originally affected by attacks, resulting in poor defense effectiveness. To address this challenge, we propose a robust SGNN framework called Balance Augmented-Signed Graph Contrastive Learning (BA-SGCL), which combines Graph Contrastive Learning principles with balance augmentation techniques. Experimental results demonstrate that BA-SGCL not only enhances robustness against existing adversarial attacks but also achieves superior performance on link sign prediction task across various datasets.
On the locally self-similar blowup for the generalized SQG equation

We analyze finite-time blowup scenarios of locally self-similar type for the inviscid generalized surface quasi-geostrophic equation (gSQG) in $\mathbb{R}^2$. Under an $L^r$ growth assumption on the self-similar profile and its gradient, we identify appropriate ranges of the self-similar parameter where the profile is either identically zero, and hence blowup cannot occur, or its $L^p$ asymptotic behavior can be characterized, for suitable $r, p$. Furthermore, we also generalize and improve on an analogous result proved in [Xue; Journal of Differential Equations, 2016] concerning the SQG equation and recover the results proved in [Cannone, Xue; Proceedings of the American Mathematical Society, 2015] concerning the globally self-similar solutions of the gSQG equation.
Path Choice Matters for Clear Attribution in Path Methods

Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust. Path methods are commonly employed to generate rigorous attributions that satisfy three axioms. However, the meaning of attributions remains ambiguous due to distinct path choices. To address the ambiguity, we introduce \textbf{Concentration Principle}, which centrally allocates high attributions to indispensable features, thereby endowing aesthetic and sparsity. We then present \textbf{SAMP}, a model-agnostic interpreter, which efficiently searches the near-optimal path from a pre-defined set of manipulation paths. Moreover, we propose the infinitesimal constraint (IC) and momentum strategy (MS) to improve the rigorousness and optimality. Visualizations show that SAMP can precisely reveal DNNs by pinpointing salient image pixels. We also perform quantitative experiments and observe that our method significantly outperforms the counterparts. Code: https://github.com/zbr17/SAMP.
Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?

In this work, we investigate the controllability of large language models (LLMs) on scientific summarization tasks. We identify key stylistic and content coverage factors that characterize different types of summaries such as paper reviews, abstracts, and lay summaries. By controlling stylistic features, we find that non-fine-tuned LLMs outperform humans in the MuP review generation task, both in terms of similarity to reference summaries and human preferences. Also, we show that we can improve the controllability of LLMs with keyword-based classifier-free guidance (CFG) while achieving lexical overlap comparable to strong fine-tuned baselines on arXiv and PubMed. However, our results also indicate that LLMs cannot consistently generate long summaries with more than 8 sentences. Furthermore, these models exhibit limited capacity to produce highly abstractive lay summaries. Although LLMs demonstrate strong generic summarization competency, sophisticated content control without costly fine-tuning remains an open problem for domain-specific applications.
Observation of tunable topological polaritons in a cavity waveguide

Topological polaritons characterized by light-matter interactions have become a pivotal platform in exploring new topological phases of matter. Recent theoretical advances unveiled a novel mechanism for tuning topological phases of polaritons by modifying the surrounding photonic environment (light-matter interactions) without altering the lattice structure. Here, by embedding a dimerized chain of microwave helical resonators (electric dipole emitters) in a metallic cavity waveguide, we report the pioneering observation of tunable topological phases of polaritons by varying the cavity width which governs the surrounding photonic environment and the strength of light-matter interactions. Moreover, we experimentally identified a new type of topological phase transition which includes three non-coincident critical points in the parameter space: the closure of the polaritonic bandgap, the transition of the Zak phase, and the hybridization of the topological edge states with the bulk states. These results reveal some remarkable and uncharted properties of topological matter when strongly coupled to light and provide an innovative design principle for tunable topological photonic devices.
Quantitative infinite mixing for non-compact skew products

We consider skew products over subshifts of finite type in which the fibers are copies of the real line, and we study their mixing properties with respect to any infinite invariant measure given by the product of a Gibbs measure on the base and Lebesgue measure on the fibers. Assuming that the system is accessible, we prove a quantitative version of Krickeberg mixing for a class of observables which is dense in the space of continuous functions vanishing at infinity.
Accurately Computing Expected Visiting Times and Stationary Distributions in Markov Chains

We study the accurate and efficient computation of the expected number of times each state is visited in discrete- and continuous-time Markov chains. To obtain sound accuracy guarantees efficiently, we lift interval iteration and topological approaches known from the computation of reachability probabilities and expected rewards. We further study applications of expected visiting times, including the sound computation of the stationary distribution and expected rewards conditioned on reaching multiple goal states. The implementation of our methods in the probabilistic model checker Storm scales to large systems with millions of states. Our experiments on the quantitative verification benchmark set show that the computation of stationary distributions via expected visiting times consistently outperforms existing approaches - sometimes by several orders of magnitude.
A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges

Vehicle re-identification (ReID) endeavors to associate vehicle images collected from a distributed network of cameras spanning diverse traffic environments. This task assumes paramount importance within the spectrum of vehicle-centric technologies, playing a pivotal role in deploying Intelligent Transportation Systems (ITS) and advancing smart city initiatives. Rapid advancements in deep learning have significantly propelled the evolution of vehicle ReID technologies in recent years. Consequently, undertaking a comprehensive survey of methodologies centered on deep learning for vehicle re-identification has become imperative and inescapable. This paper extensively explores deep learning techniques applied to vehicle ReID. It outlines the categorization of these methods, encompassing supervised and unsupervised approaches, delves into existing research within these categories, introduces datasets and evaluation criteria, and delineates forthcoming challenges and potential research directions. This comprehensive assessment examines the landscape of deep learning in vehicle ReID and establishes a foundation and starting point for future works. It aims to serve as a complete reference by highlighting challenges and emerging trends, fostering advancements and applications in vehicle ReID utilizing deep learning models.
Grouping cells in primate visual cortex

Our perception of how objects are laid out in visual scenes is remarkably stable, despite rapid shifts in the patterns of light that fall on the retina with each saccade. One mechanism that may help establish perceptual stability is border ownership assignment. Studies in macaque area V2 have identified border ownership neurons that signal which side of a border belongs to a foreground surface. This signal persists for hundreds of milliseconds after border ownership has been rendered ambiguous by deleting the stimulus features that distinguish foreground from background. Remarkably, this signal survives eye movements: border ownership neurons also exhibit border ownership signals de novo when an eye movement places the newly ambiguous border within their receptive field. The grouping cell hypothesis proposes the existence of hypothetical grouping cells in a downstream brain area. These cells would compute persistent proto-object representations and therefore have the properties to endow cells in upstream brain areas with selectivity for border ownership. Such grouping cells have been predicted to show a centripetal and persistent pattern of preferred side of ownership for a border placed parallel to the perimeter of their classical receptive field, and such a centripetal ownership preference pattern should also occur de novo in these same cells if an ambiguous border lands in their receptive field after a saccade. It is unknown if grouping cells exist. Here we used laminar multielectrodes in area V4 – the main source of feedback to V2 – of behaving macaques to determine whether such grouping cells exist. Consistent with the model prediction we find a substantial population of neurons with these properties, in all laminar compartments, and they exhibit a response latency that is short enough to act as the source that endows neurons in V2 with selectivity for border ownership. While grouping cell activity provides information about the location of foreground surfaces, these neurons are, counterintuitively, not as strongly tuned for luminance contrast polarity, a feature of those surfaces, as are border ownership cells. Our data suggest a division of labor in which these newly discovered grouping cells provide spatiotemporal continuity of segmented surfaces whereas border ownership cells link this location information with surface features such as luminance contrast.
Information Systems and Healthcare XXIX: Information Technology Investments and Returns - Uniqueness in the Healthcare Industry

The way in which information technology (IT) impacts firm productivity is an enduring question in organizational research and practice. Rather than adopting the common explanation that IT spending improves organizational performance, we hypothesize that IT spending determines the amount of IT assets, such as IT hardware, IT personnel, IT systems, and IT outsourcing, that can be acquired. These IT assets, in turn, affect productivity. The context of our study is the healthcare industry. Because of the unique set of managerial values, incentives, and constraints in this industry, we also hypothesize that IT personnel play a key role in determining hospital productivity. Analysis of panel data on acute-care centers provides support for our hypotheses. This paper contributes to literature by (1) refining earlier research that explains that IT spending improves organizational productivity; (2) examining the interrelationships between various types of IT assets; and (3) providing initial indications that outsourcing of the IT function may not have effects in healthcare that it does in other industries.
The Communications of the Association for Information Systems

Human Computer Interaction (HCI)or Human Factors studies in MIS are concerned with the ways humans interact with information, technologies, and tasks, especially in business, managerial, organizational, and cultural contexts. This article describes the existence and importance of HCI research in the MIS discipline, its historical development, some of its characteristics, publication opportunities, and future research directions. It is believed that HCI is the subject of a strong research stream in MIS, and will continue to be strong in the foreseeable future. It is hoped that HCI studies can provide the evolution of the human centered technology development that enhances our work/job, our various needs, our organizations, our societies, and ourselves. 334 Communications of the Association for Information Systems (Volume 9, 2002) 334-355 Human-Computer Interaction Research in the MIS Discipline by P. Zhang, et al. AMCIS 2002 PANELS AND WORKSHOPS I: HUMAN-COMPUTER INTERACTION RESEARCH IN THE MIS DISCIPLINE Ping Zhang Syracuse University pzhang@syr.edu Izak Benbasat University of British Columbia Jane Carey Arizona State University West Fred Davis University of Arkansas Dennis Galletta University of Pittsburgh Diane Strong Worcester Polytechnic Institute
The Influence of Entrepreneurial Action on Strategic Alignment in New Ventures

Introduction The alignment of IT strategy with business strategy has been among the top concerns of business leaders for several decades (Kappelman et al. 2014; Niederman et al. 1991). This interest from practitioners has stimulated researchers to produce a voluminous body of literature where definitions and dimensions of alignment have been proposed, measures and models have been developed, and antecedents and outcomes have been identified (Chan et al. 2007). One specific area of interest within strategic alignment research has been the study of how alignment develops and changes in an organization over time. Researchers have described stages of growth in how firms strategically use IT (Burn 1993), phases in the pursuit of alignment (Street 2006), and a model of punctuated equilibrium where extended periods of stability with evolutionary change are interrupted by short periods of rapid transformation with revolutionary change (Sabherwal et al. 2001b). In spite of this foregoing work, few if any have examined the genesis of strategic alignment and its related processes in new ventures. Some researchers have investigated the strategic role of IT in SMEs, an area of research closely related to new ventures (Bergeron et al. 2001; Bergeron et al. 2004; Cragg et al. 2002; Hussin et al. 2002; Levy et al. 2011; Raymond et al. 2009), others have discussed IT as a tool for innovation (Beckman et al. 2012a; Chen et al. 2010; Fichman et al. 2014), and still others have explored the role of the entrepreneur-leader in technology entrepreneurship (Beckman et al. 2012b). Nevertheless, strategic alignment in new ventures, and specifically the development of alignment from its very outset remains under investigated. The purpose of this paper is to explore the manner in which strategic alignment begins and evolves in a new venture. New ventures are an area of active research interest within the broader community of business scholars. Researchers studying new ventures have observed that there are discernible differences in the way that entrepreneurs build and grow their businesses and have therefore constructed several
EECA Regional SORT IT A five-year audit of gaps in HIV testing and associations with TB treatment outcomes in Armenia, 2015-2019

Introduction: Poor human immunodeficiency virus (HIV) testing practices and underreporting of HIV-related data in TB information systems remain barriers to effective care for TB-HIV co-infected patients. HIV testing and recording practices in national TB program have not been formally evaluated in Armenia. This study aimed to assess the recording completeness of HIV testing and HIV status in the national TB program electronic database, and to determine trend in HIV testing and the association between HIV testing and treatment outcomes for all TB patients registered in Armenia (2015-2019). Methodology: A cohort study of TB patients using routine programmatic data from the national TB program of Armenia. Results: From 2015 to 2019, the electronic database was completed for HIV testing and HIV status by 48.1% and 97.5%, respectively. Of all registered TB patients 93.6% were tested for HIV. Of a total 4,674 patients, 1,085 (23.2%) had unsuccessful outcomes. Patients with HIV status “not tested” and “not recorded” compared to HIV “negatives” had 1.76 (95%CI 1.42-2.11) and 1.6 (95%CI 1.20-2.06) times higher risk of unsuccessful outcomes, respectively. Lost to follow-up was the most frequent unsuccessful outcome in HIV status “not tested” group. Conclusions: An analysis of nationwide data revealed incompleteness of the national TB electronic database for HIV data. Patients with HIV status “not tested” and “not recorded” had higher risk of unsuccessful TB treatment outcomes. Upgrade of the electronic database with information on key indicators of TB-HIV services will facilitate improved monitoring and reporting.
Speaker Veriﬁcation Score Normalization Using Speaker Model Clusters

Among the various proposed score normalizations, T-and Z-norm are most widely used in speaker veriﬁcation systems. The main idea in these normalizations is to reduce the variations in impostor scores in order to improve accuracy. These normalizations require selection of a set of cohort models or utterances in order to estimate the impostor score distribution. In this paper we investigate basing this selection on recently-proposed speaker model clusters (SMCs). We evaluate this approach using the NTIMIT and NIST-2002 corpora and compare against T-and Z-norm which use other cohort selection methods. We also propose three new normalization techniques, ∆ -, ∆ T-and TC-norm, which also use SMCs to estimate the normalization parameters. Our results show that we can lower the equal error rate and minimum decision cost function with fewer cohort models using SMC-based score normalization approaches.
UvA-DARE (Digital Academic Repository) Not All Media Multitasking Is the Same: The Frequency of Media Multitasking Depends on Cognitive and Affective Characteristics of Media Combinations

Media multitasking comprises a variety of different behaviors, ranging from watching TV while sending a text message to listening to music while gaming. However, we still know little about which media multitasking behaviors are selected more frequently and whether speci ﬁ c characteristics of the media activities determine this choice. Therefore, the present study examined to what extent media multitasking is predicted by 4 cognitive dimensions (Wang et al., 2015) and by instant emotional grati ﬁ cation. We reanalyzed 15 data sets that assessed between 36 and 144 media multitasking combinations each. The ﬁ ndings show that media multitasking occurs more frequently among media combinations that are characterized by a high control over task switching, do not present information in a transient manner, do not access the same sensory modality, and do not require a behavioral response. Moreover, media multi-tasking occurs more frequently among media combinations that provide instant emotional grati ﬁ cation. These ﬁ ndings further illuminate media multitasking by unraveling the cognitive and emotional characteristics of media multitasking. The ﬁ ndings demonstrate that in addition to cognitive resources and demands, media multitasking is predicted by instant emotional grati ﬁ cation.
The Effect of Sea Surface Temperature and 2m Air Temperature on Precipitation Events in the Southern Coasts of Caspian Sea

There are a number of ideas to generate cloud and precipitation in the southern coasts of Caspian Sea, but none of them explain the cause of precipitation particularly heavy and super heavy precipitations precisely. This study describes main thermodynamic factors when the situation and location of synoptic patterns are effective. On the basis of daily data, monthly regimes and monthly trends of the Sea Surface Temperature (SST), difference between 2m air temperature and SST over the Caspian Sea as well as the SST gradients in different distances on latitude and its anomaly were calculated. For recognition of synoptic conditions, humidity advection, geopotential and sea level pressure maps were drawn. The results showed that there are three thermodynamic factors over the Caspian Sea to produce precipitation particularly from September to December. The first factor is arrangement and well organized of the SST gradients as it decreases from the south to the north of Caspian Sea. Also, the SST over the Sea must be enough warm to produce clouds and precipitation. The last factor is the difference between 2m air temperature and SST. When the synoptic patterns in different pressure levels are suitable for instability, the air-sea interaction process is the most important factor to produce the advection humidity, clouds and precipitation particularly heavier precipitation events in the north of Iran.