Miracle-dz/UniFAR-FTUs huggingface dataset:
COSMIC: Mutual Information for Task-Agnostic Summarization Evaluation

Assessing the quality of summarizers poses significant challenges. In response, we propose a novel task-oriented evaluation approach that assesses summarizers based on their capacity to produce summaries that are useful for downstream tasks, while preserving task outcomes. We theoretically establish a direct relationship between the resulting error probability of these tasks and the mutual information between source texts and generated summaries. We introduce $\texttt{COSMIC}$ as a practical implementation of this metric, demonstrating its strong correlation with human judgment-based metrics and its effectiveness in predicting downstream task performance. Comparative analyses against established metrics like $\texttt{BERTScore}$ and $\texttt{ROUGE}$ highlight the competitive performance of $\texttt{COSMIC}$.
On Efficient Computation of DiRe Committees

Consider a committee election consisting of (i) a set of candidates who are divided into arbitrary groups each of size \emph{at most} two and a diversity constraint that stipulates the selection of \emph{at least} one candidate from each group and (ii) a set of voters who are divided into arbitrary populations each approving \emph{at most} two candidates and a representation constraint that stipulates the selection of \emph{at least} one candidate from each population who has a non-null set of approved candidates. The DiRe (Diverse + Representative) committee feasibility problem (a.k.a. the minimum vertex cover problem on unweighted undirected graphs) concerns the determination of the smallest size committee that satisfies the given constraints. Here, for this problem, we discover an unconditional deterministic polynomial-time algorithm that is an amalgamation of maximum matching, breadth-first search, maximal matching, and local minimization.
Entity-Aware Multimodal Alignment Framework for News Image Captioning

News image captioning task is a variant of image captioning task which requires model to generate a more informative caption with news image and the associated news article. Multimodal Large Language models have developed rapidly in recent years and is promising in news image captioning task. However, according to our experiments, common MLLMs are not good at generating the entities in zero-shot setting. Their abilities to deal with the entities information are still limited after simply fine-tuned on news image captioning dataset. To obtain a more powerful model to handle the multimodal entity information, we design two multimodal entity-aware alignment tasks and an alignment framework to align the model and generate the news image captions. Our method achieves better results than previous state-of-the-art models in CIDEr score (72.33 ->86.29) on GoodNews dataset and (70.83 ->85.61) on NYTimes800k dataset.
Wreath-like products of groups and their von Neumann algebras III: Embeddings

For a class of wreath-like product groups with property (T), we describe explicitly all the embeddings between their von Neumann algebras. This allows us to provide a continuum of ICC groups with property (T) whose von Neumann algebras are pairwise non (stably) embeddable. We also give a construction of groups in this class only having inner injective homomorphisms. As an application, we obtain examples of group von Neumann algebras which admit only inner endomorphisms.
MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation

With the increasing multimedia information, multimodal recommendation has received extensive attention. It utilizes multimodal information to alleviate the data sparsity problem in recommendation systems, thus improving recommendation accuracy. However, the reliance on labeled data severely limits the performance of multimodal recommendation models. Recently, self-supervised learning has been used in multimodal recommendations to mitigate the label sparsity problem. Nevertheless, the state-of-the-art methods cannot avoid the modality noise when aligning multimodal information due to the large differences in the distributions of different modalities. To this end, we propose a Multi-level sElf-supervised learNing for mulTimOdal Recommendation (MENTOR) method to address the label sparsity problem and the modality alignment problem. Specifically, MENTOR first enhances the specific features of each modality using the graph convolutional network (GCN) and fuses the visual and textual modalities. It then enhances the item representation via the item semantic graph for all modalities, including the fused modality. Then, it introduces two multilevel self-supervised tasks: the multilevel cross-modal alignment task and the general feature enhancement task. The multilevel cross-modal alignment task aligns each modality under the guidance of the ID embedding from multiple levels while maintaining the historical interaction information. The general feature enhancement task enhances the general feature from both the graph and feature perspectives to improve the robustness of our model. Extensive experiments on three publicly available datasets demonstrate the effectiveness of our method. Our code is publicly available at https://github.com/Jinfeng-Xu/MENTOR.
Seeking Soulmate via Voice: Understanding Promises and Challenges of Online Synchronized Voice-Based Mobile Dating

Online dating has become a popular way for individuals to connect with potential romantic partners. Many dating apps use personal profiles that include a headshot and self-description, allowing users to present themselves and search for compatible matches. However, this traditional model often has limitations. In this study, we explore a non-traditional voice-based dating app called"Soul". Unlike traditional platforms that rely heavily on profile information, Soul facilitates user interactions through voice-based communication. We conducted semi-structured interviews with 18 dedicated Soul users to investigate how they engage with the platform and perceive themselves and others in this unique dating environment. Our findings indicate that the role of voice as a moderator influences impression management and shapes perceptions between the sender and the receiver of the voice. Additionally, the synchronous voice-based and community-based dating model offers benefits to users in the Chinese cultural context. Our study contributes to understanding the affordances introduced by voice-based interactions in online dating in China.
OzMAC: An Energy-Efficient Sparsity-Exploiting Multiply-Accumulate-Unit Design for DL Inference

General Matrix Multiply (GEMM) hardware, employing large arrays of multiply-accumulate (MAC) units, perform bulk of the computation in deep learning (DL). Recent trends have established 8-bit integer (INT8) as the most widely used precision for DL inference. This paper proposes a novel MAC design capable of dynamically exploiting bit sparsity (i.e., number of `0' bits within a binary value) in input data to achieve significant improvements on area, power and energy. The proposed architecture, called OzMAC (Omit-zero-MAC), skips over zeros within a binary input value and performs simple shift-and-add-based compute in place of expensive multipliers. We implement OzMAC in SystemVerilog and present post-synthesis performance-power-area (PPA) results using commercial TSMC N5 (5nm) process node. Using eight pretrained INT8 deep neural networks (DNNs) as benchmarks, we demonstrate the existence of high bit sparsity in real DNN workloads and show that 8-bit OzMAC improves all three metrics of area, power, and energy significantly by 21%, 70%, and 28%, respectively. Similar improvements are achieved when scaling data precisions (4, 8, 16 bits) and clock frequencies (0.5 GHz, 1 GHz, 1.5 GHz). For the 8-bit OzMAC, scaling its frequency to normalize the throughput relative to conventional MAC, it still achieves 30% improvement on both power and energy.
Infinite Order Hydrodynamics: an Analytical Example

We construct a kinetic model for matter-radiation interactions where the hydrodynamic gradient expansion can be computed analytically up to infinite order in derivatives, in the fully non-linear regime, and for arbitrary flows. The frequency dependence of the opacity of matter is chosen to mimic the relaxation time of a self-interacting scalar field. In this way, the transient sector simulates that of a realistic quantum field theory. As expected, the gradient series is divergent for most flows. We identify the mechanism at the origin of the divergence, and we provide a successful regularization scheme. Additionally, we propose a universal qualitative framework for predicting the breakdown of the gradient expansion of an arbitrary microscopic system undergoing a given flow. This framework correctly predicts the factorial divergence of the gradient expansion in most non-linear flows and its breakdown due to stochastic fluctuations. It also predicts that jets may induce an ultraviolet divergence in the gradient expansion of quark matter hydrodynamics.
Curiosity-driven Red-teaming for Large Language Models

Large language models (LLMs) hold great potential for many natural language applications but risk generating incorrect or toxic content. To probe when an LLM generates unwanted content, the current paradigm is to recruit a \textit{red team} of human testers to design input prompts (i.e., test cases) that elicit undesirable responses from LLMs. However, relying solely on human testers is expensive and time-consuming. Recent works automate red teaming by training a separate red team LLM with reinforcement learning (RL) to generate test cases that maximize the chance of eliciting undesirable responses from the target LLM. However, current RL methods are only able to generate a small number of effective test cases resulting in a low coverage of the span of prompts that elicit undesirable responses from the target LLM. To overcome this limitation, we draw a connection between the problem of increasing the coverage of generated test cases and the well-studied approach of curiosity-driven exploration that optimizes for novelty. Our method of curiosity-driven red teaming (CRT) achieves greater coverage of test cases while mantaining or increasing their effectiveness compared to existing methods. Our method, CRT successfully provokes toxic responses from LLaMA2 model that has been heavily fine-tuned using human preferences to avoid toxic outputs. Code is available at \url{https://github.com/Improbable-AI/curiosity_redteam}
SeMoLi: What Moves Together Belongs Together

We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects and use these as supervisory signals to train 3D object detectors in Lidar data without manual supervision. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner. We leverage recent advances in scene flow estimation to obtain point trajectories from which we extract long-term, class-agnostic motion patterns. Revisiting correlation clustering in the context of message passing networks, we learn to group those motion patterns to cluster points to object instances. By estimating the full extent of the objects, we obtain per-scan 3D bounding boxes that we use to supervise a Lidar object detection network. Our method not only outperforms prior heuristic-based approaches (57.5 AP, +14 improvement over prior work), more importantly, we show we can pseudo-label and train object detectors across datasets.
TV-TREES: Multimodal Entailment Trees for Neuro-Symbolic Video Reasoning

It is challenging to perform question-answering over complex, multimodal content such as television clips. This is in part because current video-language models rely on single-modality reasoning, have lowered performance on long inputs, and lack interpetability. We propose TV-TREES, the first multimodal entailment tree generator. TV-TREES serves as an approach to video understanding that promotes interpretable joint-modality reasoning by producing trees of entailment relationships between simple premises directly entailed by the videos and higher-level conclusions. We then introduce the task of multimodal entailment tree generation to evaluate the reasoning quality of such methods. Our method's experimental results on the challenging TVQA dataset demonstrate intepretable, state-of-the-art zero-shot performance on full video clips, illustrating a best-of-both-worlds contrast to black-box methods.
Dynamics and Attributions of Baseflow in the Semiarid Loess Plateau

Baseflow is an all‐important replenishment feature of the hydrologic cycle for maintaining surface water in basins across the world. Understanding its dynamics and the influence of climate variability and human activities will be beneficial for developing sustainable water‐management strategies. In this study, we selected 11 basins within the Loess Plateau (a typical semiarid region in China) for assessment of the variations in baseflow and baseflow index (BFI; the ratio of baseflow volume to total streamflow volume) during the period 1961–2014. We compared eight baseflow separation methods and selected the median series in order to reduce the uncertainty. Results showed that values of the “Bflow” and Eckhardt filter methods were higher than values of the other six methods for the period of peak discharge. Annual baseflow exhibited a statistically significant downward trend (p < 0.01) in all 11 basins except Qingjian and Yanhe basins, while nine basins trended upward for BFI. All basins exhibited downward trends for seasonal baseflow in all seasons, with Yanhe and Qingjian showing the least significant trend. Variations in both baseflow and the BFI were more sensitive to variations in potential evapotranspiration than to variations in precipitation. Across these 11 basins for both the conservation engineering period (1971–1999) and the reforestation period (2000–2014), human activities (73% and 76%, respectively) contributed more than climate variability (27% and 24%, respectively) to average baseflow decline. However, climate variability was the main factor altering the BFI, with the contribution accounting for about 54% and 76%, respectively, for the two periods.
An Efficient Secured Multicasting Routing Protocol in MANET: A Review

MANET is one of the most common ad hoc network with lot of problems related to congestion and routing. We are providing one of the solutions to secure the key and data transmission over the network. Security will be provide in two way: one for key exchange by GDH for n parties and another for data security by RSA. Security is done after Distributed Bellman Ford applied for finding shortest path among source and various destination nodes. Keywords— MANET, DBF, GDH, RSA, Multicasting
Aversion to Extreme Temperatures, Climate Change, and Quality of Life

This paper uses hedonic methods and variation in wages and housing costs to estimate households’ valuation of climate amenities. We find that, on the margin, households are willing to pay more to reduce extreme heat than to reduce extreme cold. Combining these estimates with “business as usual” climate forecasts for the United States, we find welfare losses in most areas by 2100, with particularly large effects in California, southern states, and urban centers. On average, the cost of hotter summers exceeds the gain from warmer winters by 2 to 3 percent of income per year. These results account for taste heterogeneity and sorting; moreover, they are not substantially attenuated by allowing for migration.
Reassessing Design and Analysis of two-Colour Microarray Experiments Using Mixed Effects Models

Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs.
Composition of Gabbroic Xenoliths in Flores Island (Azores, Portugal)

• Basal Volcanic Complex (BVC): includes products and structures from both submarine and subarerial volcanism. This complex is formed by pyroclastic deposits and interbedded flows of alkali basalts (Azevedo, 1999). • Upper Volcanic Complex (UVC): represents the main subaerial activity of the island. Includes three main volcanostratigraphic units. Basaltic to trachytic lava flows with interbedded pyroclastic deposits form the first two units. The most recent products are exclusively pyroclastic.
When compliance is not the solution but the problem: from changes in law to changes in attitude

The task of the Centre for Tax System Integrity is to extend understanding of how and why cooperation and contestation occur within the tax system. This goal presupposes a clear distinction between cooperation and contestation. In practice, however, this distinction may be open to creative management, demonstrated clearly in the practice of ‘creative compliance’. Taxpayer compliance with tax law may seem to fall, by definition, into the category of cooperation with the tax system. Indeed, securing compliance with the law is the driving force behind the compliance model of enforcement (Ayres & Braithwaite, 1992) which has been adopted by the Australian Taxation Office (Tax Office) and other regulatory agencies (Hawkins, 1984). Securing compliance is seen as the key, as the solution to the regulatory problem of making policy effective in practice. Yet compliance with the law can in practice be used, and used very effectively, to frustrate tax policy. This working paper focuses on the issue of compliance, and on the Tax Office’s compliance model of enforcement. It asks: what happens to the compliance model when compliance is not the solution but the problem? When compliance is not the solution but the problem: From changes in law to changes in attitude Doreen McBarnet The Australian Taxation Office compliance model (ATO compliance model) focuses on the strategies adopted by those enforcing the law. They should be cooperative, understanding, focusing on fostering compliance rather than on leaping immediately to punishing non-compliance, with ‘big stick’ sanctions reserved for those recalcitrants who continue with non-compliance regardless (Ayres & Braithwaite, 1992). But choice of strategies is not the preserve of enforcement agencies. Different strategic responses can be adopted by those on the receiving end of the law too, and these translate into different approaches to compliance. Faced with a tax bill, people may choose to comply willingly. We might think of this as: • committed compliance. They may choose to comply unwillingly, complain but pay up nonetheless. We might think of this as: • capitulative compliance. They may invent false expenses, pack their cash in a suitcase and whisk it out of the country without declaring it, or simply operate in the cash economy, opting for:
A Survey of Dynamic Analysis and Test Generation for JavaScript

JavaScript has become one of the most prevalent programming languages. Unfortunately, some of the unique properties that contribute to this popularity also make JavaScript programs prone to errors and difﬁcult for program analyses to reason about. These properties include the highly dynamic nature of the language, a set of unusual language features, a lack of encapsulation mechanisms, and the “no crash” philosophy. This paper surveys dynamic program analysis and test generation techniques for JavaScript targeted at improving the correctness, reliability, performance, security, and privacy of JavaScript-based software.
The intrinsic constraint model for stereo-motion

How the visual system integrates the information provided by several depth cues is central for vision research. Here, we present a model for how the human visual system combines disparity and velocity information. The model provides a depth interpretation to a subspace defined by the covariation of the two signals. We show that human performance is consistent with the predictions of the model, and compare them with those of another theoretical approach, the modified weak-fusion model. We discuss the validity of each approach as a model for human perception of 3-D shape from multiple cues to depth.
Proposing the Affect-Trust Infusion Model (ATIM) to Explain and Predict the Influence of High- and Low-Affect Infusion on Web Vendor Trust

Trust is just as essential to online business as it is to offline transactions but can be more difficult to achieve - especially for newer websites with unknown web vendors. Research on web-based trust development explains that web vendor trust can be created by both cognitive and affective (e.g., emotion-based) influences. But under what circumstances will emotion or cognition be more dominate in trust establishment? Theory-based answers to these questions can help online web vendors design better websites that account for unleveraged factors that will increase trust in the web vendor. To this end, we use the Affect Infusion Model and trust transference to propose the Affect-Trust Infusion Model (ATIM) that explains and predicts how and when cognition, through perceived website performance (PwP), and positive emotion (PEmo) each influence web vendor trust. ATIM explains the underlying causal mechanisms that determine the degree of affect infusion and the subsequent processing strategy that a user adopts when interacting with a new website. Under high-affect infusion, PEmo acts as a mediator between PwP and vendor trust; under low-affect infusion, PwP primarily impacts trust and PEmo is dis-intermediated. We review two distinct, rigorously validated experiments that empirically support ATIM. To further extend the contributions of ATIM, we demonstrate how use of specific contextual features-rooted in theory and that drive one's choice of affect infusion and cognitive processing-can be leveraged into a methodology that we propose to further enhance user-centered design (UCD). We further detail several exciting research opportunities that can leverage ATIM.
Fault Tolerance in Wireless Sensor Networks

Reliability is an important issue in a network and when the network is energy constrained as in a wireless network then it becomes a high priority to ensure that this network is highly reliable. But this reliability is severely affected by errors and faults that occur due to multiple reasons such as Hardware malfunctioning, bugs in software, environmental hazards etc. Thus a sensor network should be fault resistant so that it can deal with these erroneous conditions effectively. This paper tries to cover issues related to fault detection and recovery mechanisms in wireless sensor networks in both theoretical and application oriented research. KeywordsFault Tolerant, Wireless sensor network, Multihop Routing, Replication, Aggregation