Miracle-dz/UniFAR-FTUs huggingface dataset:
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework

Automatic video summarization is still an unsolved problem due to several challenges. We take steps towards making it more realistic by addressing the following challenges. Firstly, the currently available datasets either have very short videos or have few long videos of only a particular type. We introduce a new benchmarking dataset called VISIOCITY which comprises of longer videos across six different categories with dense concept annotations capable of supporting different flavors of video summarization and other vision problems. Secondly, for long videos, human reference summaries, necessary for supervised video summarization techniques, are difficult to obtain. We present a novel recipe based on pareto optimality to automatically generate multiple reference summaries from indirect ground truth present in VISIOCITY. We show that these summaries are at par with human summaries. Thirdly, we demonstrate that in the presence of multiple ground truth summaries (due to the highly subjective nature of the task), learning from a single combined ground truth summary using a single loss function is not a good idea. We propose a simple recipe VISIOCITY-SUM to enhance an existing model using a combination of losses and demonstrate that it beats the current state of the art techniques. We also present a study of different desired characteristics of a good summary and demonstrate that a single measure (say F1) to evaluate a summary, as is the current typical practice, falls short in some ways. We propose an evaluation framework for better quantitative assessment of summary quality which is closer to human judgment than a single measure. We report the performance of a few representative techniques of video summarization on VISIOCITY assessed using various measures and bring out the limitation of the techniques and/or the assessment mechanism in modeling human judgment and demonstrate the effectiveness of our evaluation framework in doing so.
On Loss Functions and Recurrency Training for GAN-based Speech Enhancement Systems

Recent work has shown that it is feasible to use generative adversarial networks (GANs) for speech enhancement, however, these approaches have not been compared to state-of-the-art (SOTA) non GAN-based approaches. Additionally, many loss functions have been proposed for GAN-based approaches, but they have not been adequately compared. In this study, we propose novel convolutional recurrent GAN (CRGAN) architectures for speech enhancement. Multiple loss functions are adopted to enable direct comparisons to other GAN-based systems. The benefits of including recurrent layers are also explored. Our results show that the proposed CRGAN model outperforms the SOTA GAN-based models using the same loss functions and it outperforms other non-GAN based systems, indicating the benefits of using a GAN for speech enhancement. Overall, the CRGAN model that combines an objective metric loss function with the mean squared error (MSE) provides the best performance over comparison approaches across many evaluation metrics.
Towards Domain-Specific Characterization of Misinformation

The rapid dissemination of health misinformation poses an increasing risk to public health. To best understand the way of combating health misinformation, it is important to acknowledge how the fundamental characteristics of misinformation differ from domain to domain. This paper presents a pathway towards domain-specific characterization of misinformation so that we can address the concealed behavior of health misinformation compared to others and take proper initiative accordingly for combating it. With this aim, we have mentioned several possible approaches to identify discriminating features of medical misinformation from other types of misinformation. Thereafter, we briefly propose a research plan followed by possible challenges to meet up. The findings of the proposed research idea will provide new directions to the misinformation research community.
Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning

Video summarization aims at generating concise video summaries from the lengthy videos, to achieve better user watching experience. Due to the subjectivity, purely supervised methods for video summarization may bring the inherent errors from the annotations. To solve the subjectivity problem, we study the general user summarization process. General users usually watch the whole video, compare interesting clips and select some clips to form a final summary. Inspired by the general user behaviours, we formulate the summarization process as multiple sequential decision-making processes, and propose Comparison-Selection Network (CoSNet) based on multi-agent reinforcement learning. Each agent focuses on a video clip and constantly changes its focus during the iterations, and the final focus clips of all agents form the summary. The comparison network provides the agent with the visual feature from clips and the chronological feature from the past round, while the selection network of the agent makes decisions on the change of its focus clip. The specially designed unsupervised reward and supervised reward together contribute to the policy advancement, each containing local and global parts. Extensive experiments on two benchmark datasets show that CoSNet outperforms state-of-the-art unsupervised methods with the unsupervised reward and surpasses most supervised methods with the complete reward.
Double-peak emission line galaxies in the SDSS catalogue

Double-peak narrow emission line galaxies have been studied extensively in the past years, in the hope of discovering late stages of mergers. It is difficult to disentangle this phenomenon from disc rotations and gas outflows with the sole spectroscopic measurement of the central 3″. We aim to properly detect such galaxies and distinguish the underlying mechanisms with a detailed analysis of the host-galaxy properties and their kinematics. Relying on the Reference Catalogue of Spectral Energy Distribution, we developed an automated selection procedure and found 5663 double-peak emission line galaxies at z < 0.34 corresponding to 0.8% of the parent database. To characterise these galaxies, we built a single-peak no-bias control sample (NBCS) with the same redshift and stellar mass distributions as the double-peak sample (DPS). These two samples are indeed very similar in terms of absolute magnitude, [OIII] luminosity, colour-colour diagrams, age and specific star formation rate, metallicity, and environment. We find an important excess of S0 galaxies in the DPS, not observed in the NBCS, which cannot be accounted for by the environment, as most of these galaxies are isolated or in poor groups. Similarly, we find a relative deficit of pure discs in the DPS late-type galaxies, which are preferentially of Sa type. In parallel, we observe a systematic central excess of star formation and extinction for double peak (DP) galaxies. Finally, there are noticeable differences in the kinematics: The gas velocity dispersion is correlated with the galaxy inclination in the NBCS, whereas this relation does not hold for the DPS. Furthermore, the DP galaxies show larger stellar velocity dispersions and they deviate from the Tully-Fisher relation for both late-type and S0 galaxies. These discrepancies can be reconciled if one considers the two peaks as two different components. Considering the morphological biases in favour of bulge-dominated galaxies and the star formation central enhancement, we suggest a scenario of multiple, sequential minor mergers driving the increase of the bulge size, leading to larger fractions of S0 galaxies and a deficit of pure disc galaxies.
How to Define Dissipation-Preserving Energy for Time-Fractional Phase-Field Equations

There exists a well defined energy for classical phase-field equations under which the dissipation law is satisfied, i.e., the energy is non-increasing with respect to time. However, it is not clear how to extend the energy definition to time-fractional phase-field equations so that the corresponding dissipation law is still satisfied. In this work, we will try to settle this problem for phase-field equations with Caputo time-fractional derivative, by defining a nonlocal energy as an averaging of the classical energy with a time-dependent weight function. As the governing equation exhibits both nonlocal and nonlinear behavior, the dissipation analysis is challenging. To deal with this, we propose a new theorem on judging the positive definiteness of a symmetric function, that is derived from a special Cholesky decomposition. Then, the nonlocal energy is proved to be dissipative under a simple restriction of the weight function. Within the same framework, the time fractional derivative of classical energy for time-fractional phase-field models can be proved to be always nonpositive.
The Low-latency Region of a Communication Link

This letter shows the existence of a proportional fair rate allocation in communication links carrying both time-sensitive (TS) and non-time-sensitive traffic (NTS). This optimal point results from maximizing a simple throughput-delay trade-off that considers a) the NTS traffic load, and b) the difference between the maximum and the current delay of the TS packets. To show how the presented trade-off could be used to allocate NTS traffic in a realistic scenario, we use Google Stadia traffic traces to generate the TS flow. Results from this use-case confirm that the throughput-delay trade-off also works when both the packet arrival process and packet service time distributions are general.
X-Ray Observations of a [C ii]-bright, z = 6.59 Quasar/Companion System

We present deep Chandra observations of PSO J231.6576−20.8335, a quasar at redshift z = 6.59 with a nearby (∼8 proper kpc) companion galaxy. The Atacama Large Millimeter/submillimeter Array observed both the quasar and companion to be bright in [C ii], and the system has significant extended Lyα emission around the quasar, suggesting that a galaxy merger is ongoing. Unlike previous studies of two similar systems, and despite observing the system with Chandra for 140 ks, we do not detect the companion in X-rays. The quasar itself is detected, but only net counts are observed. From a basic spectral analysis, the X-ray spectrum of the quasar is soft (hardness ratio of , power-law index of ), which results in a rest-frame X-ray luminosity comparable to other bright quasars ( ) despite the faint observed X-ray flux. We highlight two possible interpretations of this result: the quasar has a steep value of Γ—potentially related to observed ongoing Eddington accretion—thereby pushing much of the emission out of our observed band, or the quasar has a more normal spectrum (Γ ∼ 2) but is therefore less X-ray luminous ( ).
TopoAct: Visually Exploring the Shape of Activations in Deep Learning

Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present TopoAct, a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using TopoAct that provide valuable insights into learned representations of neural networks. We expect TopoAct to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.
Feedback Capacities of Gaussian Multiple-Access Wiretap Channels

The feedback capacities of the Gaussian multiple-access channel (GMAC) and the GMAC with noncausal channel state information at the transmitters (NCSIT) are achieved by Ozarow's Schalkwijk-Kailath (SK) type feedback scheme and its extension (also called Rosenzweig's feedback scheme), respectively. In this paper, first, we show that the above Ozarow's and Rosenzweig's feedback schemes, which are not designed with the consideration of secrecy, already achieve the secrecy capacity regions (capacity regions with perfect weak secrecy constraint) of the Gaussian multiple-access wiretap channel (GMAC-WT) with feedback and the GMAC-WT-NCSIT with feedback, respectively. This indicates that for Gaussian multiple-access wiretap channels, the traditional secret key based feedback schemes are not necessary. Next, we show that the feedback capacities of the GMAC with degraded message sets (GMAC-DMS) and the GMAC-NCSIT with degraded message sets (GMAC-NCSIT-DMS) can be achieved by two-step SK type feedback schemes. Furthermore, we show that the above capacity-achieving feedback schemes also achieve the secrecy capacity regions of the GMAC-WT-DMS with feedback and the GMAC-WT-NCSIT-DMS with feedback, i.e., the secrecy capacity regions of the GMAC-WT-DMS with feedback and the GMAC-WT-NCSIT-DMS with feedback equal the capacity regions of the same models without secrecy constraint. Finally, we derive outer bounds on the secrecy capacity regions of GMAC-WT, GMAC-WT-DMS and GMAC-WT-NCSIT-DMS, and show that feedback helps to increase the secrecy capacity regions of GMAC-WT and GMAC-WT-DMS, and in some cases helps to increase the secrecy capacity regions of GMAC-WT-NCSIT and GMAC-WT-NCSIT-DMS. Overall, the main contribution of this paper is to find the inherent secrecy nature of SK-type feedback schemes for the GMACs.
Volatile Sample Return in the Solar System

We advocate for the realization of volatile sample return from various destinations including: small bodies, the Moon, Mars, ocean worlds/satellites, and plumes. As part of recent mission studies (e.g., Comet Astrobiology Exploration SAmple Return (CAESAR) and Mars Sample Return), new concepts, technologies, and protocols have been considered for specific environments and cost. Here we provide a plan for volatile sample collection and identify the associated challenges with the environment, transit/storage, Earth re-entry, and curation. Laboratory and theoretical simulations are proposed to verify sample integrity during each mission phase. Sample collection mechanisms are evaluated for a given environment with consideration for alteration. Transport and curation are essential for sample return to maximize the science investment and ensure pristine samples for analysis upon return and after years of preservation. All aspects of a volatile sample return mission are driven by the science motivation: isotope fractionation, noble gases, organics and prebiotic species; plus planetary protection considerations for collection and for the sample. The science value of sample return missions has been clearly demonstrated by previous sample return programs and missions. Sample return of volatile material is key to understanding (exo)planet formation, evolution, and habitability. Returning planetary volatiles poses unique and potentially severe technical challenges. These include preventing changes to samples between (and including) collection and analyses, and meeting planetary protection requirements.
VCSEL-based sliceable bandwidth/bitrate variable transceivers

The use of vertical cavity surface emitting laser (VCSEL) at long wavelengths, especially if characterized by large bandwidth or tunable capability, is appearing as an attractive technology for the implementation of advanced transceivers to be used in optical metro networks at 100G and beyond. In this work, we report recent promising results on the adoption of different types of VCSEL for the sliceable bandwidth/bitrate variable transceiver (S-BVT) design. Special attention will be devoted to technological aspects and challenges, focusing on the added value of exploiting novel photonic technologies for the implementation of costeffective transceivers, suitable for future optical metro networks targeting high capacity and flexibility.
Fibonacci and k-Subsecting Recursive Feature Elimination

Feature selection is a data mining task with the potential of speeding up classification algorithms, enhancing model comprehensibility, and improving learning accuracy. However, finding a subset of features that is optimal in terms of predictive accuracy is usually computationally intractable. Out of several heuristic approaches to dealing with this problem, the Recursive Feature Elimination (RFE) algorithm has received considerable interest from data mining practitioners. In this paper, we propose two novel algorithms inspired by RFE, called Fibonacci- and k-Subsecting Recursive Feature Elimination, which remove features in logarithmic steps, probing the wrapped classifier more densely for the more promising feature subsets. The proposed algorithms are experimentally compared against RFE on 28 highly multidimensional datasets and evaluated in a practical case study involving 3D electron density maps from the Protein Data Bank. The results show that Fibonacci and k-Subsecting Recursive Feature Elimination are capable of selecting a smaller subset of features much faster than standard RFE, while achieving comparable predictive performance.
Visual Reasoning Strategies and Satisficing: How Uncertainty Visualization Design Impacts Effect Size Judgments and Decisions

Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies.
The ITensor Software Library for Tensor Network Calculations

ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagrams, allowing users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interface rules out common programming errors and enables rapid prototyping of algorithms. After discussing the philosophy behind the ITensor approach, we show examples of each part of the interface including Index objects, the ITensor product operator, tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, quantum number conserving block sparse tensors, and the NDTensors library. We also review publications that have used ITensor for quantum many-body physics and for other areas where tensor networks are increasingly applied. To conclude we discuss promising features and optimizations to be added in the future.
Towards Ecologically Valid Research on Language User Interfaces

Language User Interfaces (LUIs) could improve human-machine interaction for a wide variety of tasks, such as playing music, getting insights from databases, or instructing domestic robots. In contrast to traditional hand-crafted approaches, recent work attempts to build LUIs in a data-driven way using modern deep learning methods. To satisfy the data needs of such learning algorithms, researchers have constructed benchmarks that emphasize the quantity of collected data at the cost of its naturalness and relevance to real-world LUI use cases. As a consequence, research findings on such benchmarks might not be relevant for developing practical LUIs. The goal of this paper is to bootstrap the discussion around this issue, which we refer to as the benchmarks' low ecological validity. To this end, we describe what we deem an ideal methodology for machine learning research on LUIs and categorize five common ways in which recent benchmarks deviate from it. We give concrete examples of the five kinds of deviations and their consequences. Lastly, we offer a number of recommendations as to how to increase the ecological validity of machine learning research on LUIs.
Cache-Enabled HetNets With Limited Backhaul: A Stochastic Geometry Model

With the rapid explosion of data volume from mobile networks, edge caching has received significant attentions as an efficient approach to boost content delivery efficiency by bringing contents near users. In this article, cache-enabled heterogeneous networks (HetNets) considering the limited backhaul are analyzed with the aid of the stochastic geometry approach. A hybrid caching policy, in which the most popular contents are cached in the macro BSs tier with the deterministic caching strategy and the less popular contents are cached in the helpers tier with the probabilistic caching strategy, is proposed. Correspondingly, the content-centric association strategy is designed based on the comprehensive state of the access link, the cache and the backhaul link. Under the hybrid caching policy, new analytical results for successful content delivery probability, average successful delivery rate and energy efficiency are derived in the general scenario, the interference-limited scenario and the mean load scenario. The simulation results show that the proposed caching policy outperforms the most popular caching policy in HetNets with the limited backhaul. The performance gain is dramatically improved when the content popularity is less skewed, the cache capacity is sufficient and the helper density is relatively large. Furthermore, it is confirmed that there exists an optimal helper density to maximize the energy efficiency of the cache-enabled HetNets.
Presentation and Analysis of a Multimodal Dataset for Grounded LanguageLearning

Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI. In practice the data used for learning consists almost entirely of textual descriptions, which tend to be cleaner, clearer, and more grammatical than actual human interactions. In this work, we present the Grounded Language Dataset (GoLD), a multimodal dataset of common household objects described by people using either spoken or written language. We analyze the differences and present an experiment showing how the different modalities affect language learning from human in-put. This will enable researchers studying the intersection of robotics, NLP, and HCI to better investigate how the multiple modalities of image, text, and speech interact, as well as show differences in the vernacular of these modalities impact results.