Miracle-dz/UniFAR-FTUs huggingface dataset:
Event-based Motion-Robust Accurate Shape Estimation for Mixed Reflectance Scenes

Event-based structured light systems have recently been introduced as an exciting alternative to conventional frame-based triangulation systems for the 3D measurements of diffuse surfaces. Important benefits include the fast capture speed and the high dynamic range provided by the event camera - albeit at the cost of lower data quality. So far, both low-accuracy event-based as well as high-accuracy frame-based 3D imaging systems are tailored to a specific surface type, such as diffuse or specular, and can not be used for a broader class of object surfaces ("mixed reflectance scenes"). In this paper, we present a novel event-based structured light system that enables fast 3D imaging of mixed reflectance scenes with high accuracy. On the captured events, we use epipolar constraints that intrinsically enable decomposing the measured reflections into diffuse, two-bounce specular, and other multi-bounce reflections. The diffuse objects in the scene are reconstructed using triangulation. Eventually, the reconstructed diffuse scene parts are used as a"display"to evaluate the specular scene parts via deflectometry. This novel procedure allows us to use the entire scene as a virtual screen, using only a scanning laser and an event camera. The resulting system achieves fast and motion-robust (14Hz) reconstructions of mixed reflectance scenes with<500 $\mu$m accuracy. Moreover, we introduce a"superfast"capture mode (250Hz) for the 3D measurement of diffuse scenes.
Interpreting User Requests in the Context of Natural Language Standing Instructions

Users of natural language interfaces, generally powered by Large Language Models (LLMs),often must repeat their preferences each time they make a similar request. We describe an approach to LLM-based dialogue modeling in which persistent user constraints and preferences -- collectively termed standing instructions -- as additional context for such interfaces. For example, when a user states"I'm hungry", a previously expressed preference for Persian food can be automatically added to the LLM prompt, influencing the search for relevant restaurants. We develop NLSI, a language-to-program dataset consisting of over 2.4K dialogues spanning 17 domains, where each dialogue is paired with a user profile (a set of users specific standing instructions) and corresponding structured representations (API calls). A key challenge in NLSI is to identify which subset of the standing instructions is applicable to a given dialogue. NLSI contains diverse phenomena, from simple preferences to interdependent instructions such as triggering a hotel search whenever the user is booking tickets to an event. We conduct experiments on NLSI using prompting with large language models and various retrieval approaches, achieving a maximum of 44.7% exact match on API prediction. Our results demonstrate the challenges in identifying the relevant standing instructions and their interpretation into API calls.
RENI++ A Rotation-Equivariant, Scale-Invariant, Natural Illumination Prior

Inverse rendering is an ill-posed problem. Previous work has sought to resolve this by focussing on priors for object or scene shape or appearance. In this work, we instead focus on a prior for natural illuminations. Current methods rely on spherical harmonic lighting or other generic representations and, at best, a simplistic prior on the parameters. This results in limitations for the inverse setting in terms of the expressivity of the illumination conditions, especially when taking specular reflections into account. We propose a conditional neural field representation based on a variational auto-decoder and a transformer decoder. We extend Vector Neurons to build equivariance directly into our architecture, and leveraging insights from depth estimation through a scale-invariant loss function, we enable the accurate representation of High Dynamic Range (HDR) images. The result is a compact, rotation-equivariant HDR neural illumination model capable of capturing complex, high-frequency features in natural environment maps. Training our model on a curated dataset of 1.6K HDR environment maps of natural scenes, we compare it against traditional representations, demonstrate its applicability for an inverse rendering task and show environment map completion from partial observations. We share our PyTorch implementation, dataset and trained models at https://github.com/JADGardner/ns_reni
HAL 9000: Skynet's Risk Manager

Intrusion Tolerant Systems (ITSs) are a necessary component for cyber-services/infrastructures. Additionally, as cyberattacks follow a multi-domain attack surface, a similar defensive approach should be applied, namely, the use of an evolving multi-disciplinary solution that combines ITS, cybersecurity and Artificial Intelligence (AI). With the increased popularity of AI solutions, due to Big Data use-case scenarios and decision support and automation scenarios, new opportunities to apply Machine Learning (ML) algorithms have emerged, namely ITS empowerment. Using ML algorithms, an ITS can augment its intrusion tolerance capability, by learning from previous attacks and from known vulnerabilities. As such, this work's contribution is twofold: (1) an ITS architecture (Skynet) based on the state-of-the-art and incorporates new components to increase its intrusion tolerance capability and its adaptability to new adversaries; (2) an improved Risk Manager design that leverages AI to improve ITSs by automatically assessing OS risks to intrusions, and advise with safer configurations. One of the reasons that intrusions are successful is due to bad configurations or slow adaptability to new threats. This can be caused by the dependency that systems have for human intervention. One of the characteristics in Skynet and HAL 9000 design is the removal of human intervention. Being fully automatized lowers the chance of successful intrusions caused by human error. Our experiments using Skynet, shows that HAL is able to choose 15% safer configurations than the state-of-the-art risk manager.
Structured Chemistry Reasoning with Large Language Models

Large Language Models (LLMs) excel in diverse areas, yet struggle with complex scientific reasoning, especially in the field of chemistry. Different from the simple chemistry tasks (e.g., molecule classification) addressed in previous studies, complex chemistry problems require not only vast knowledge and precise calculation, but also compositional reasoning about rich dynamic interactions of different concepts (e.g., temperature changes). Our study shows that even advanced LLMs, like GPT-4, can fail easily in different ways. Interestingly, the errors often stem not from a lack of domain knowledge within the LLMs, but rather from the absence of an effective reasoning structure that guides the LLMs to elicit the right knowledge, incorporate the knowledge in step-by-step reasoning, and iteratively refine results for further improved quality. On this basis, we introduce StructChem, a simple yet effective prompting strategy that offers the desired guidance and substantially boosts the LLMs' chemical reasoning capability. Testing across four chemistry areas -- quantum chemistry, mechanics, physical chemistry, and kinetics -- StructChem substantially enhances GPT-4's performance, with up to 30\% peak improvement. Our analysis also underscores the unique difficulties of precise grounded reasoning in science with LLMs, highlighting a need for more research in this area. Code is available at \url{https://github.com/ozyyshr/StructChem}.
AMRFact: Enhancing Summarization Factuality Evaluation with AMR-driven Training Data Generation

Ensuring factual consistency is crucial in various natural language processing tasks, particularly in abstractive summarization, where preserving the integrity of information is paramount. Prior entailment-based approaches often generate factually inconsistent summaries and then train a classifier on the generated data. However, summaries produced by these approaches are either of low coherence or lack error-type coverage. To address these issues, we propose AMRFact, a novel framework that generates factually inconsistent summaries using Abstract Meaning Representation (AMR). Our approach parses factually correct summaries into AMR graphs and injects controlled factual inconsistencies to create negative examples, allowing for coherent factually inconsistent summaries to be generated with high error-type coverage. Additionally, we present a data selection module NegFilter based on natural language inference and BARTScore to ensure the quality of the generated negative samples. Experimental results demonstrate that our approach significantly outperforms previous systems on the AggreFact-SOTA dataset, showcasing its efficacy in assessing factuality in abstractive summarization.
Test-time Backdoor Mitigation for Black-Box Large Language Models with Defensive Demonstrations

Existing studies in backdoor defense have predominantly focused on the training phase, overlooking the critical aspect of testing time defense. This gap becomes particularly pronounced in the context of Large Language Models (LLMs) deployed as Web Services, which typically offer only black-box access, rendering training-time defenses impractical. To bridge this gap, our work introduces defensive demonstrations, an innovative backdoor defense strategy for blackbox large language models. Our method involves identifying the task and retrieving task-relevant demonstrations from an uncontaminated pool. These demonstrations are then combined with user queries and presented to the model during testing, without requiring any modifications/tuning to the black-box model or insights into its internal mechanisms. Defensive demonstrations are designed to counteract the adverse effects of triggers, aiming to recalibrate and correct the behavior of poisoned models during test-time evaluations. Extensive experiments show that defensive demonstrations are effective in defending both instance-level and instruction-level backdoor attacks, not only rectifying the behavior of poisoned models but also surpassing existing baselines in most scenarios.
PixT3: Pixel-based Table To Text generation

Table-to-text generation involves generating appropriate textual descriptions given structured tabular data. It has attracted increasing attention in recent years thanks to the popularity of neural network models and the availability of large-scale datasets. A common feature across existing methods is their treatment of the input as a string, i.e., by employing linearization techniques that do not always preserve information in the table, are verbose, and lack space efficiency. We propose to rethink data-to-text generation as a visual recognition task, removing the need for rendering the input in a string format. We present PixT3, a multimodal table-to-text model that overcomes the challenges of linearization and input size limitations encountered by existing models. PixT3 is trained with a new self-supervised learning objective to reinforce table structure awareness and is applicable to open-ended and controlled generation settings. Experiments on the ToTTo and Logic2Text benchmarks show that PixT3 is competitive and, in some settings, superior to generators that operate solely on text.
Capacitated Network Bargaining Games: Stability and Structure

Capacitated network bargaining games are popular combinatorial games that involve the structure of matchings in graphs. We show that it is always possible to stabilize unit-weight instances of this problem (that is, ensure that they admit a stable outcome) via capacity-reduction and edge-removal operations, without decreasing the total value that the players can get. Furthermore, for general weighted instances, we show that computing a minimum amount of vertex-capacity to reduce to make an instance stable is a polynomial-time solvable problem. We then exploit this to give approximation results for the NP-hard problem of stabilizing a graph via edge-removal operations. Our work extends and generalizes previous results in the literature that dealt with a unit-capacity version of the problem, using several new arguments. In particular, while previous results mainly used combinatorial techniques, we here rely on polyhedral arguments and, more specifically, on the notion of circuits of a polytope.
Unifying Physical Framework for Stretched-Exponential, Compressed-Exponential, and Logarithmic Relaxation Phenomena in Glassy Polymers

We develop a simple yet comprehensive nonlinear model to describe relaxation phenomena in amorphous glass-formers near the glass transition temperature. The model is based on the two-state, two-(time)scale (TS2) framework, and describes the isothermal relaxation of specific volume, enthalpy, or shear stress via a simple first-order nonlinear differential equation (the Trachenko-Zaccone [TZ] equation) for local cooperative events. These nonlinear dynamics of cooperatively rearranging regions (CRR) naturally arise from the TS2 framework. We demonstrate that the solutions of the TZ equation comprehensively encompass the Debye exponential relaxation, the Kohlrausch-Williams-Watts (KWW) stretched and compressed relaxations, and the Guiu-Pratt logarithmic relaxation. Furthermore, for the case of stress relaxation modeling, our model recovers, as one of its limits, the Eyring law for plastic flow, where the Eyring activation volume is related to thermodynamic parameters of the material. Using the example of polystyrene (PS), we demonstrate how our model successfully describes the Kovacs"asymmetry of approach"specific volume and enthalpy experiments, as well as the stress relaxation. Other potential applications of the model, including the dielectric relaxation, are also discussed. The presented approach disentangles the physical origins of different relaxation laws within a single general framework based on the underlying physics.
Numerical Simulations of Buoyant Ekman Layers. Part II: Rectification in Zero-Mean, Time-Dependent Forcing, and Feedback on the Interior Flow

Abstract The response of the turbulent buoyant bottom Ekman layer near a temperature front over uniform topography is studied here. The background stratification is variable across the slope; the upper slope is either neutrally or stably stratified at one-half of the gradient of the lower slope region. In case 1, a time-dependent, spatially uniform, along-isobath interior current with zero mean causes residual circulation across the boundary layer and net detachment of the fluid from the boundary layer. For forcing with time scales longer than the shutdown time scale [τ0 = f/(Nα)2; e.g., as defined by McCready and Rhines, where f is the Coriolis parameter, N is the Brunt– Vaisala frequency in the lower slope region, and α is the bottom slope], it is shown that the front represents an area of strong mean flow convergence and subsequent net detrainment of boundary layer fluid into the interior and is also a region of significant relative vorticity generation by the mean field. The residual circulation occur.
Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model

Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-task learning strategy that decouples the number of training tasks from memory requirements. We trained a Universal bioMedical PreTrained model (UMedPT) on a multi-task database including tomographic, microscopic, and X-ray images, with various labelling strategies such as classification, segmentation, and object detection. The UMedPT foundational model outperformed ImageNet pretraining and the previous state-of-the-art models. For tasks related to the pretraining database, it maintained its performance with only 1% of the original training data and without fine-tuning. For out-of-domain tasks it required not more than 50% of the original training data. In an external independent validation imaging features extracted using UMedPT proved to be a new standard for cross-center transferability.
Well-being in Isolation: Exploring Artistic Immersive Virtual Environments in a Simulated Lunar Habitat to Alleviate Asthenia Symptoms

Revived interest in lunar and planetary exploration is heralding a new era for human spaceflight, characterized by frequent strain on astronaut’s mental well-being, which stems from increased exposure to isolated, confined, and extreme (ICE) conditions. Whilst Immersive Virtual Reality (IVR) has been employed to facilitate self-help interventions to mitigate challenges caused by isolated environments in several domains, its applicability in support of future space expeditions remains largely unexplored. To address this limitation, we administered the use of distinct IVR environments to crew members $(n=5)$ partaking in a simulated lunar habitat study. Utilizing a Bayesian approach to scrutinize small group data, we discovered a significant relationship between IVR usage and a reduction in perceived stress-related symptoms, particularly those associated with asthenia (syndrome often linked to chronic fatigue and weakness; a condition characterized by feelings of energy depletion or exhaustion that can be amplified in ICE conditions). The reductions were most prominent with the use of interactive virtual environments. The ’Aesthetic Realities’ - virtual environments conceived as art exhibits - received exceptional praise from our participants. These environments mark a fascinating convergence of art and science, holding promise to mitigate effects related to isolation in spaceflight training and beyond
Investigating the Emergent Audio Classification Ability of ASR Foundation Models

Text and vision foundation models can perform many tasks in a zero-shot setting, a desirable property that enables these systems to be applied in general and low-resource settings. There has been far less work, however, on the zero-shot abilities of ASR foundation models, with these systems typically fine-tuned to specific tasks or constrained to applications that match their training criterion and data annotation. In this work we investigate the ability of Whisper and MMS, ASR foundation models trained primarily for speech recognition, to perform zero-shot audio classification. We use simple template-based text prompts at the decoder and use the resulting decoding probabilities to generate zero-shot predictions. Without training the model on extra data or adding any new parameters, we demonstrate that Whisper shows promising zero-shot classification performance on a range of 8 audio-classification datasets, outperforming the accuracy of existing state-of-the-art zero-shot baselines by an average of 9%. One important step to unlock the emergent ability is debiasing, where a simple unsupervised reweighting method of the class probabilities yields consistent significant performance gains. We further show that performance increases with model size, implying that as ASR foundation models scale up, they may exhibit improved zero-shot performance.
LePaRD: A Large-Scale Dataset of Judges Citing Precedents

We present the Legal Passage Retrieval Dataset LePaRD. LePaRD is a massive collection of U.S. federal judicial citations to precedent in context. The dataset aims to facilitate work on legal passage prediction, a challenging practice-oriented legal retrieval and reasoning task. Legal passage prediction seeks to predict relevant passages from precedential court decisions given the context of a legal argument. We extensively evaluate various retrieval approaches on LePaRD, and find that classification appears to work best. However, we note that legal precedent prediction is a difficult task, and there remains significant room for improvement. We hope that by publishing LePaRD, we will encourage others to engage with a legal NLP task that promises to help expand access to justice by reducing the burden associated with legal research. A subset of the LePaRD dataset is freely available and the whole dataset will be released upon publication.
Mapping Eye Vergence Angle to the Depth of Real and Virtual Objects as an Objective Measure of Depth Perception

Recently, extended reality (XR) displays including augmented reality (AR) and virtual reality (VR) have integrated eye tracking capabilities, which could enable novel ways of interacting with XR content. The vergence angle of the eyes constantly changes according to the distance of fixated objects. Here we measured vergence angle for eye fixations on real and simulated target objects in three different environments: real objects in the real-world (real), virtual objects in the real-world (AR), and virtual objects in the virtual world (VR) using gaze data from an eye-tracking device. In a repeated-measures design with 13 participants, Gaze-measured Vergence Angle (GVA) was measured while participants fixated on targets at varying distances. As expected, results showed a significant main effect of target depth such that increasing GVA was associated with closer targets. However, there were consistent individual differences in baseline GVA. When these individual differences were controlled for, there was a small but statistically-significant main effect of environment (real, AR, VR). Importantly, GVA was stable with respect to the starting depth of previously fixated targets and invariant to directionality (convergence vs. divergence). In addition, GVA proved to be a more veridical depth estimate than subjective depth judgements.
SQATIN: Supervised Instruction Tuning Meets Question Answering for Improved Dialogue NLU

Task-oriented dialogue (ToD) systems help users execute well-defined tasks across a variety of domains (e.g., $\textit{flight booking}$ or $\textit{food ordering}$), with their Natural Language Understanding (NLU) components being dedicated to the analysis of user utterances, predicting users' intents ($\textit{Intent Detection}$, ID) and extracting values for informational slots ($\textit{Value Extraction}$, VE). In most domains, labelled NLU data is scarce, making sample-efficient learning -- enabled with effective transfer paradigms -- paramount. In this work, we introduce SQATIN, a new framework for dialog NLU based on (i) instruction tuning and (ii) question-answering-based formulation of ID and VE tasks. According to the evaluation on established NLU benchmarks, SQATIN sets the new state of the art in dialogue NLU, substantially surpassing the performance of current models based on standard fine-tuning objectives in both in-domain training and cross-domain transfer. SQATIN yields particularly large performance gains in cross-domain transfer, owing to the fact that our QA-based instruction tuning leverages similarities between natural language descriptions of classes (i.e., slots and intents) across domains.