Miracle-dz/UniFAR-FTUs huggingface dataset:
Attribute Diversity Determines the Systematicity Gap in VQA

The degree to which neural networks can generalize to new combinations of familiar concepts, and the conditions under which they are able to do so, has long been an open question. In this work, we study the systematicity gap in visual question answering: the performance difference between reasoning on previously seen and unseen combinations of object attributes. To test, we introduce a novel diagnostic dataset, CLEVR-HOPE. We find that while increased quantity of training data does not reduce the systematicity gap, increased training data diversity of the attributes in the unseen combination does. In all, our experiments suggest that the more distinct attribute type combinations are seen during training, the more systematic we can expect the resulting model to be.
Fusion-Eval: Integrating Evaluators with LLMs

Evaluating natural language systems poses significant challenges, particularly in the realms of natural language understanding and high-level reasoning. In this paper, we introduce"Fusion-Eval", an innovative approach that leverages Large Language Models (LLMs) to integrate insights from various assistant evaluators. Each of these evaluators specializes in assessing distinct aspects of responses. This unique strategy enables Fusion-Eval to function effectively across a diverse range of tasks and criteria, enhancing the effectiveness of existing evaluation methods. Fusion-Eval achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.744 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods. These results highlight Fusion-Eval's significant potential in the realm of natural language system evaluation.
It Takes Two to Negotiate: Modeling Social Exchange in Online Multiplayer Games

Online games are dynamic environments where players interact with each other, which offers a rich setting for understanding how players negotiate their way through the game to an ultimate victory. This work studies online player interactions during the turn-based strategy game, Diplomacy. We annotated a dataset of over 10,000 chat messages for different negotiation strategies and empirically examined their importance in predicting long- and short-term game outcomes. Although negotiation strategies can be predicted reasonably accurately through the linguistic modeling of the chat messages, more is needed for predicting short-term outcomes such as trustworthiness. On the other hand, they are essential in graph-aware reinforcement learning approaches to predict long-term outcomes, such as a player's success, based on their prior negotiation history. We close with a discussion of the implications and impact of our work. The dataset is available at https://github.com/kj2013/claff-diplomacy.
Incorporating Preferences Into Treatment Assignment Problems

This study investigates the problem of individualizing treatment allocations using stated preferences for treatments. If individuals know in advance how the assignment will be individualized based on their stated preferences, they may state false preferences. We derive an individualized treatment rule (ITR) that maximizes welfare when individuals strategically state their preferences. We also show that the optimal ITR is strategy-proof, that is, individuals do not have a strong incentive to lie even if they know the optimal ITR a priori. Constructing the optimal ITR requires information on the distribution of true preferences and the average treatment effect conditioned on true preferences. In practice, the information must be identified and estimated from the data. As true preferences are hidden information, the identification is not straightforward. We discuss two experimental designs that allow the identification: strictly strategy-proof randomized controlled trials and doubly randomized preference trials. Under the presumption that data comes from one of these experiments, we develop data-dependent procedures for determining ITR, that is, statistical treatment rules (STRs). The maximum regret of the proposed STRs converges to zero at a rate of the square root of the sample size. An empirical application demonstrates our proposed STRs.
Low-light Pedestrian Detection in Visible and Infrared Image Feeds: Issues and Challenges

Pedestrian detection has become a cornerstone for several high-level tasks, including autonomous driving, intelligent transportation, and traffic surveillance. There are several works focussed on pedestrian detection using visible images, mainly in the daytime. However, this task is very intriguing when the environmental conditions change to poor lighting or nighttime. Recently, new ideas have been spurred to use alternative sources, such as Far InfraRed (FIR) temperature sensor feeds for detecting pedestrians in low-light conditions. This study comprehensively reviews recent developments in low-light pedestrian detection approaches. It systematically categorizes and analyses various algorithms from region-based to non-region-based and graph-based learning methodologies by highlighting their methodologies, implementation issues, and challenges. It also outlines the key benchmark datasets that can be used for research and development of advanced pedestrian detection algorithms, particularly in low-light situations
STABILITY OF SEMI-ARID SAVANNA GRAZING SYSTEMS

SUMMARY (2) The model is based on Walter's two layer hypothesis. Woody vegetation and grasses compete for water in the surface layers of the soil, but woody vegetation has exclusive access to a source of water relatively deep underground. Where there is only a small biomass of grass the soil surface tends to become impermeable and, in these conditions, the model shows that two different steady states may develop: with a lot of woody vegetation alone, or with a relatively large biomass of grass and rather little woody vegetation. (3) The results are discussed in terms of the concept of resilience. The continued existence of both stable states under ranching conditions seems to depend on periodic heavy, or over-, grazing which allows for the maintenance of unpalatable or unstable grass species, which thus set a minimum to grass biomass-a minimum which cannot be reduced by herbivores. (4) Comparison of the dynamics of various savanna and other natural systems leads to the conclusion that the resilience of the systems decreases as their stability (usually induced) increases.
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?

Knowledge graphs (KGs) consist of links that describe relationships between entities. Due to the difficulty of manually enumerating all relationships between entities, automatically completing them is essential for KGs. Knowledge Graph Completion (KGC) is a task that infers unseen relationships between entities in a KG. Traditional embedding-based KGC methods, such as RESCAL, TransE, DistMult, ComplEx, RotatE, HAKE, HousE, etc., infer missing links using only the knowledge from training data. In contrast, the recent Pre-trained Language Model (PLM)-based KGC utilizes knowledge obtained during pre-training. Therefore, PLM-based KGC can estimate missing links between entities by reusing memorized knowledge from pre-training without inference. This approach is problematic because building KGC models aims to infer unseen links between entities. However, conventional evaluations in KGC do not consider inference and memorization abilities separately. Thus, a PLM-based KGC method, which achieves high performance in current KGC evaluations, may be ineffective in practical applications. To address this issue, we analyze whether PLM-based KGC methods make inferences or merely access memorized knowledge. For this purpose, we propose a method for constructing synthetic datasets specified in this analysis and conclude that PLMs acquire the inference abilities required for KGC through pre-training, even though the performance improvements mostly come from textual information of entities and relations.
Local near-field scattering data enables unique reconstruction of rough electric potentials

The focus of this paper is the study of the inverse point-source scattering problem, specifically in relation to a certain class of electric potentials. Our research provides a novel uniqueness result for the inverse problem with local data, obtained from the near field pattern. Our work improves the work of Caro and Garcia, who investigated both the direct problem and the inverse problem with global near field data for critically singular and $\delta$-shell potentials. The primary contribution of our research is the introduction of a Runge approximation result for the near field data on the scattering problem which, in combination with an interior regularity argument, enables us to establish a uniqueness result for the inverse problem with local data. Additionaly, we manage to consider a slightly wider class of potentials.
MELA: Multilingual Evaluation of Linguistic Acceptability

Recent benchmarks for Large Language Models (LLMs) have mostly focused on application-driven tasks such as complex reasoning and code generation, and this has led to a scarcity in purely linguistic evaluation of LLMs. Against this background, we introduce Multilingual Evaluation of Linguistic Acceptability -- MELA, the first multilingual benchmark on linguistic acceptability with 48K samples covering 10 languages from a diverse set of language families. We establish baselines of commonly used LLMs along with supervised models, and conduct cross-lingual transfer and multi-task learning experiments with XLM-R. In pursuit of multilingual interpretability, we analyze the weights of fine-tuned XLM-R to explore the possibility of identifying transfer difficulty between languages. Our results show that ChatGPT benefits much from in-context examples but still lags behind fine-tuned XLM-R, while the performance of GPT-4 is on par with fine-tuned XLM-R even in zero-shot setting. Cross-lingual and multi-task learning experiments show that unlike semantic tasks, in-language training data is crucial in acceptability judgements. Results in layerwise probing indicate that the upper layers of XLM-R become a task-specific but language-agnostic region for multilingual acceptability judgment. We also introduce the concept of conflicting weight, which could be a potential indicator for the difficulty of cross-lingual transfer between languages. Our data will be available at https://github.com/sjtu-compling/MELA.
CLIMB: Curriculum Learning for Infant-inspired Model Building

We describe our team's contribution to the STRICT-SMALL track of the BabyLM Challenge. The challenge requires training a language model from scratch using only a relatively small training dataset of ten million words. We experiment with three variants of cognitively-motivated curriculum learning and analyze their effect on the performance of the model on linguistic evaluation tasks. In the vocabulary curriculum, we analyze methods for constraining the vocabulary in the early stages of training to simulate cognitively more plausible learning curves. In the data curriculum experiments, we vary the order of the training instances based on i) infant-inspired expectations and ii) the learning behavior of the model. In the objective curriculum, we explore different variations of combining the conventional masked language modeling task with a more coarse-grained word class prediction task to reinforce linguistic generalization capabilities. Our results did not yield consistent improvements over our own non-curriculum learning baseline across a range of linguistic benchmarks; however, we do find marginal gains on select tasks. Our analysis highlights key takeaways for specific combinations of tasks and settings which benefit from our proposed curricula. We moreover determine that careful selection of model architecture, and training hyper-parameters yield substantial improvements over the default baselines provided by the BabyLM challenge.
Brain Functional Connectivity under Teleoperation Latency: a fNIRS Study

Objective: This study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. Background: Latency between command, execution, and feedback in teleoperation can impair performance and affect operators mental state. The neural underpinnings of these effects are not well understood. Method: A human subject experiment (n = 41) of a simulated remote robot manipulation task was performed. Three conditions were tested: no latency, with visual and haptic latency, with visual latency and no haptic latency. fNIRS and performance data were recorded and analyzed. Results: The presence of latency in teleoperation significantly increased functional connectivity within and between prefrontal and motor cortexes. Maintaining visual latency while providing real-time haptic feedback reduced the average functional connectivity in all cortical networks and showed a significantly different connectivity ratio within prefrontal and motor cortical networks. The performance results showed the worst performance in the all-delayed condition and best performance in no latency condition, which echoes the neural activity patterns. Conclusion: The study provides neurological evidence that latency in teleoperation increases cognitive load, anxiety, and challenges in motion planning and control. Real-time haptic feedback, however, positively influences neural pathways related to cognition, decision-making, and sensorimotor processes. Application: This research can inform the design of ergonomic teleoperation systems that mitigate the effects of latency.
TableLlama: Towards Open Large Generalist Models for Tables

Semi-structured tables are ubiquitous. There has been a variety of tasks that aim to automatically interpret, augment, and query tables. Current methods often require pretraining on tables or special model architecture design, are restricted to specific table types, or have simplifying assumptions about tables and tasks. This paper makes the first step towards developing open-source large language models (LLMs) as generalists for a diversity of table-based tasks. Towards that end, we construct TableInstruct, a new dataset with a variety of realistic tables and tasks, for instruction tuning and evaluating LLMs. We further develop the first open-source generalist model for tables, TableLlama, by fine-tuning Llama 2 (7B) with LongLoRA to address the long context challenge. We experiment under both in-domain setting and out-of-domain setting. On 7 out of 8 in-domain tasks, TableLlama achieves comparable or better performance than the SOTA for each task, despite the latter often has task-specific design. On 6 out-of-domain datasets, it achieves 5-44 absolute point gains compared with the base model, showing that training on TableInstruct enhances the model's generalizability. We open-source our dataset and trained model to boost future work on developing open generalist models for tables.
On Doubly Robust Estimation with Nonignorable Missing Data Using Instrumental Variables

Suppose we are interested in the mean of an outcome that is subject to nonignorable nonresponse. This paper develops new semiparametric estimation methods with instrumental variables which affect nonresponse, but not the outcome. The proposed estimators remain consistent and asymptotically normal even under partial model misspecifications for two variation independent nuisance functions. We evaluate the performance of the proposed estimators via a simulation study, and apply them in adjusting for missing data induced by HIV testing refusal in the evaluation of HIV seroprevalence in Mochudi, Botswana, using interviewer experience as an instrumental variable.
Efficiently Escaping Saddle Points for Non-Convex Policy Optimization

Policy gradient (PG) is widely used in reinforcement learning due to its scalability and good performance. In recent years, several variance-reduced PG methods have been proposed with a theoretical guarantee of converging to an approximate first-order stationary point (FOSP) with the sample complexity of $O(\epsilon^{-3})$. However, FOSPs could be bad local optima or saddle points. Moreover, these algorithms often use importance sampling (IS) weights which could impair the statistical effectiveness of variance reduction. In this paper, we propose a variance-reduced second-order method that uses second-order information in the form of Hessian vector products (HVP) and converges to an approximate second-order stationary point (SOSP) with sample complexity of $\tilde{O}(\epsilon^{-3})$. This rate improves the best-known sample complexity for achieving approximate SOSPs by a factor of $O(\epsilon^{-0.5})$. Moreover, the proposed variance reduction technique bypasses IS weights by using HVP terms. Our experimental results show that the proposed algorithm outperforms the state of the art and is more robust to changes in random seeds.
PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers

Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication. However, a barrier to effective assistance is the lack of personalization in LLM outputs to the author's communication style and specialized knowledge. In this paper, we address this challenge by proposing PEARL, a retrieval-augmented LLM writing assistant personalized with a generation-calibrated retriever. Our retriever is trained to select historic user-authored documents for prompt augmentation, such that they are likely to best personalize LLM generations for a user request. We propose two key novelties for training our retriever: 1) A training data selection method that identifies user requests likely to benefit from personalization and documents that provide that benefit; and 2) A scale-calibrating KL-divergence objective that ensures that our retriever closely tracks the benefit of a document for personalized generation. We demonstrate the effectiveness of PEARL in generating personalized workplace social media posts and Reddit comments. Finally, we showcase the potential of a generation-calibrated retriever to double as a performance predictor and further improve low-quality generations via LLM chaining.
Difference-in-Differences Estimators of Intertemporal Treatment Effects

We study treatment-effect estimation, with a panel where groups may experience multiple changes of their treatment dose. We make parallel trends assumptions, but do not restrict treatment effect heterogeneity, unlike the linear regressions that have been used in such designs. We extend the event-study approach for binary-and-staggered treatments, by redefining the event as the first time a group’s treatment changes. This yields an eventstudy graph, with reduced-form estimates of the effect of having been exposed to a weakly higher amount of treatment for l periods. We show that the reduced-form estimates can be combined into an economically interpretable cost-benefit ratio.
Microbial Distribution in a Hydrothermal Plume of the Southwest Indian Ridge

abstract Hydrothermal plumes are widely distributed throughout the global spreading ridges, yet few of them are microbiologically explored. The ultraslow-spreading ridges, recently recognized as a unique, new class of mid-ocean-ridge system, have provided surprises and new insights in hydrothermal system research. A suite of water column samples including both hydrothermal plume samples and ambient seawater were collected at different depths from the ultraslow-spreading Southwest Indian Ridge (SWIR) in 2010. We use molecular approaches such as clone libraries, denaturing gradient gel electrophoresis (DGGE) and quantitative PCR to determine microbial community compositions and their spatial variability within the hydrothermal plume and seawater. Phylogenetic analysis showed that plume samples were mainly dominated by members of α-Proteobacteria and γ-Proteobacteria and members of marine group I group within the Crenarchaeota. Within the hydrothermal plume, archaeal populations were spatially homogeneous, while bacterial compositions were heterogeneous and remarkably distinct at different depths. Moreover, several lineages, closely related to known Mn(II) oxidizers were found to be abundant and even predominant within the plume bacterial communities. DGGE band patterns showed that there was no significant difference in microbial compositions between the samples of hydrothermal plume and ambient seawater. Taken together, we inferred that microbial communities in the SWIR hydrothermal plumes were sourced from ambient seawater rather than from seafloor vent-derived niches. This is the first report on the characteristics of microbial community structures in hydrothermal plume and ambient seawater in the Southwest Indian Ridge.
New Challenges in Accounting and Finance

Introduction Tax aggressiveness can be defined as the management of the tax base, which occurs via tax planning, which seeks to reduce explicit taxes through evasion and tax avoidance activities (Chen et al., 2010). Tax planning should not be regarded as an activity that provides for tax evasion a priori; it also takes advantage of legitimate exemptions and concessions provided for in the tax system in the search, among the legal methods available, the one that causes the lowest tax burden for the company (Martinez, 2017). The complexity of tax rules and the interpretation of tax legislation can lead the inspection to have a different understanding from the decision-makers’ understanding of the calculation and settlement of taxes (Hanlon, Maydew & Saavedra, 2014). In Brazil, tax aggressiveness is the cause of recurring concerns since there is a code-law legal structure. Ederaldo José Pereira de Lima, PolianoBastos da Cruz*, Antonio Lopo Martinez
Non-muscle myosin II regulates presynaptic actin assemblies and neuronal mechanobiology

Neuromuscular junctions (NMJs) are evolutionarily ancient, specialized contacts between neurons and muscles. Axons and NMJs must endure mechanical strain through a lifetime of muscle contraction, making them vulnerable to aging and neurodegenerative conditions. However, cellular strategies for mitigating this mechanical stress remain unknown. In this study, we used Drosophila larval NMJs to investigate the role of actin and myosin (actomyosin)-mediated contractility in generating and responding to cellular forces at the neuron-muscle interface. We identified a new long-lived, low-turnover presynaptic actin core traversing the NMJ, which partly co-localizes with non-muscle myosin II (NMII). Neuronal RNAi of NMII induced disorganization of this core, suggesting that this structure might have contractile properties. Interestingly, neuronal RNAi of NMII also decreased NMII levels in the postsynaptic muscle proximal to neurons, suggesting that neuronal actomyosin rearrangements propagate their effects trans-synaptically. We also observed reduced Integrin levels upon NMII knockdown, indicating that neuronal actomyosin disruption triggers rearrangements of Integrin-mediated connections between neurons and surrounding muscle tissue. In summary, our study identifies a previously uncharacterized presynaptic actomyosin subpopulation that upholds the neuronal mechanical continuum, transmits signals to adjacent muscle tissue, and collaborates with Integrin receptors to govern the mechanobiology of the neuromuscular junction.