Miracle-dz/UniFAR-FTUs huggingface dataset:
LLpowershap: Logistic Loss-based Automated Shapley Values Feature Selection Method

Shapley values have been used extensively in machine learning, not only to explain black box machine learning models, but among other tasks, also to conduct model debugging, sensitivity and fairness analyses and to select important features for robust modelling and for further follow-up analyses. Shapley values satisfy certain axioms that promote fairness in distributing contributions of features toward prediction or reducing error, after accounting for non-linear relationships and interactions when complex machine learning models are employed. Recently, a number of feature selection methods utilising Shapley values have been introduced. Here, we present a novel feature selection method, LLpowershap, which makes use of loss-based Shapley values to identify informative features with minimal noise among the selected sets of features. Our simulation results show that LLpowershap not only identifies higher number of informative features but outputs fewer noise features compared to other state-of-the-art feature selection methods. Benchmarking results on four real-world datasets demonstrate higher or at par predictive performance of LLpowershap compared to other Shapley based wrapper methods, or filter methods.
Study of non-Fourier heat conduction in FinFETs/GAAFETs via synthetic iterative scheme with large temperature variance

Non-Fourier heat conduction in hotspot systems including fin or gate-all-around field effect transistors (FinFETs/GAAFETs) is studied via synthetic iterative scheme, where a macroscopic iteration is introduced for preprocessing based on the iterative solutions of the stationary phonon Boltzmann transport equation (BTE). Different from previous work with small temperature difference assumption and linearized equilibrium state, the original Bose-Einstein distribution function and temperature-dependent relaxation time are considered in this paper due to large temperature variance in practical thermal engineering. Numerical tests show that the present scheme could correctly capture the heat conduction in both the ballistic and diffusive regimes. Selective phonon excitations including only heating single phonon branch, equally heating all phonon modes and Joule heating are studied. Numerical results show that the differences of selective phonon excitation on micro/nano scale heat conduction in monocrystalline silicon are mainly reflected in the energy weight absorbed by longitude acoustic phonon from external heat source. Unexpectedly, the temperature contours in quasi-2D FinFETs between equally heating all phonon modes and Joule heating are similar. Finally, the thermal applications of the present scheme in three-dimensional single/double-fin bulk FETs and GAA naonsheet FETs with Joule heating are implemented. It is found that that $6$-naonsheets with width $25$ nm promote heat dissipation than $3$-naonsheets with width $50$ nm. Effective Fourier's law with size- and temperature- dependent thermal conductivity can also give reasonable predictions although there are some deviations compared to the phonon BTE.
FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth Manipulation

Gaze interaction presents a promising avenue in Virtual Reality (VR) due to its intuitive and efficient user experience. Yet, the depth control inherent in our visual system remains underutilized in current methods. In this study, we introduce FocusFlow, a hands-free interaction method that capitalizes on human visual depth perception within the 3D scenes of Virtual Reality. We first develop a binocular visual depth detection algorithm to understand eye input characteristics. We then propose a layer-based user interface and introduce the concept of 'Virtual Window' that offers an intuitive and robust gaze-depth VR interaction, despite the constraints of visual depth accuracy and precision spatially at further distances. Finally, to help novice users actively manipulate their visual depth, we propose two learning strategies that use different visual cues to help users master visual depth control. Our user studies on 24 participants demonstrate the usability of our proposed virtual window concept as a gaze-depth interaction method. In addition, our findings reveal that the user experience can be enhanced through an effective learning process with adaptive visual cues, helping users to develop muscle memory for this brand-new input mechanism. We conclude the paper by discussing strategies to optimize learning and potential research topics of gaze-depth interaction.
ESC: Edge-attributed Skyline Community Search in Large-scale Bipartite Graphs

Due to the ability of modeling relationships between two different types of entities, bipartite graphs are naturally employed in many real-world applications. Community Search in bipartite graphs is a fundamental problem and has gained much attention. However, existing studies focus on measuring the structural cohesiveness between two sets of vertices, while either completely ignoring the edge attributes or only considering one-dimensional importance in forming communities. In this paper, we introduce a novel community model, named edge-attributed skyline community (ESC), which not only preserves the structural cohesiveness but unravels the inherent dominance brought about by multi-dimensional attributes on the edges of bipartite graphs. To search the ESCs, we develop an elegant peeling algorithm by iteratively deleting edges with the minimum attribute in each dimension. In addition, we also devise a more efficient expanding algorithm to further reduce the search space and speed up the filtering of unpromising vertices, where a upper bound is proposed and proven. Extensive experiments on real-world large-scale datasets demonstrate the efficiency, effectiveness, and scalability of the proposed ESC search algorithms. A case study was conducted to compare with existing community models, substantiating that our approach facilitates the precision and diversity of results.
Program Decomposition and Translation with Static Analysis

The rising popularity of Large Language Models (LLMs) has motivated exploring their use in code-related tasks. Code LLMs with more than millions of parameters are trained on a massive amount of code in different Programming Languages (PLs). Such models are used for automating various Software Engineering (SE) tasks using prompt engineering. However, given the very large size of industry-scale project files, a major issue of these LLMs is their limited context window size, motivating the question of"Can these LLMs process very large files and can we effectively perform prompt engineering? ". Code translation aims to convert source code from one PL to another. In this work, we assess the effect of method-level program decomposition on context window of LLMs and investigate how this approach can enable translation of very large files which originally could not be done due to out-of-context issue. Our observations from 20 well-known java projects and approximately 60K methods suggest that method-level program decomposition significantly improves the limited context window problem of LLMs by 99.5%. Furthermore, our empirical analysis indicate that with method-level decomposition, each input fragment on average only consumes 5% of the context window, leaving more context space for prompt engineering and the output. Finally, we investigate the effectiveness of a Call Graph (CG) approach for translating very large files when doing method-level program decomposition.
ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation

Recently, foundational models such as CLIP and SAM have shown promising performance for the task of Zero-Shot Anomaly Segmentation (ZSAS). However, either CLIP-based or SAM-based ZSAS methods still suffer from non-negligible key drawbacks: 1) CLIP primarily focuses on global feature alignment across different inputs, leading to imprecise segmentation of local anomalous parts; 2) SAM tends to generate numerous redundant masks without proper prompt constraints, resulting in complex post-processing requirements. In this work, we innovatively propose a CLIP and SAM collaboration framework called ClipSAM for ZSAS. The insight behind ClipSAM is to employ CLIP's semantic understanding capability for anomaly localization and rough segmentation, which is further used as the prompt constraints for SAM to refine the anomaly segmentation results. In details, we introduce a crucial Unified Multi-scale Cross-modal Interaction (UMCI) module for interacting language with visual features at multiple scales of CLIP to reason anomaly positions. Then, we design a novel Multi-level Mask Refinement (MMR) module, which utilizes the positional information as multi-level prompts for SAM to acquire hierarchical levels of masks and merges them. Extensive experiments validate the effectiveness of our approach, achieving the optimal segmentation performance on the MVTec-AD and VisA datasets.
Formally Specifying the High-Level Behavior of LLM-Based Agents

Autonomous, goal-driven agents powered by LLMs have recently emerged as promising tools for solving challenging problems without the need for task-specific finetuned models that can be expensive to procure. Currently, the design and implementation of such agents is ad hoc, as the wide variety of tasks that LLM-based agents may be applied to naturally means there can be no one-size-fits-all approach to agent design. In this work we aim to alleviate the difficulty of designing and implementing new agents by proposing a minimalistic generation framework that simplifies the process of building agents. The framework we introduce allows the user to define desired agent behaviors in a high-level, declarative specification that is then used to construct a decoding monitor which guarantees the LLM will produce an output exhibiting the desired behavior. Our declarative approach, in which the behavior is described without concern for how it should be implemented or enforced, enables rapid design, implementation, and experimentation with different LLM-based agents. We demonstrate how the proposed framework can be used to implement recent LLM-based agents (e.g., ReACT), and show how the flexibility of our approach can be leveraged to define a new agent with more complex behavior, the Plan-Act-Summarize-Solve (PASS) agent. Lastly, we demonstrate that our method outperforms other agents on multiple popular reasoning-centric question-answering benchmarks.
1/c deformations of AdS$_3$ boundary conditions and the Dym hierarchy

This work introduces a novel family of boundary conditions for AdS$_3$ General Relativity, constructed through a polynomial expansion in negative integer powers of the Brown-Henneaux central charge. The associated dynamics is governed by the Dym hierarchy of integrable equations. It is shown that the infinite set of Dym conserved charges generates an abelian asymptotic symmetry group. Additionally, these boundary conditions encompass black hole solutions, whose thermodynamic properties are examined.
Remote Sensing Technologies for Enhancing Forest Inventories: A Review

Abstract Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time.
New lower bounds for three-term progression free sets in $\mathbb{F}_p^n$

We prove new lower bounds on the maximum size of sets $A\subseteq \mathbb{F}_p^n$ or $A\subseteq \mathbb{Z}_m^n$ not containing three-term arithmetic progressions (consisting of three distinct points). More specifically, we prove that for any fixed integer $m\ge 2$ and sufficiently large $n$ (in terms of $m$), there exists a three-term progression free subset $A\subseteq \mathbb{Z}_m^n$ of size $|A|\ge (cm)^n$ for some absolute constant $c>1/2$. Such a bound for $c=1/2$ can be obtained with a classical construction of Salem and Spencer from 1942, and improving upon this value of $1/2$ has been a well-known open problem (our proof gives $c= 0.54$). Our construction relies on finding a subset $S\subset \mathbb{Z}_m^2$ of size at least $(7/24)m^2$ with a certain type of reducibility property. This property allows us to ``lift'' $S$ to a three-term progression free subset of $\mathbb{Z}_m^n$ for large $n$ (even though the original set $S\subset \mathbb{Z}_m^2$ does contain three-term arithmetic progressions).
Truck Parking Usage Prediction with Decomposed Graph Neural Networks

Truck parking on freight corridors faces various challenges, such as insufficient parking spaces and compliance with Hour-of-Service (HOS) regulations. These constraints often result in unauthorized parking practices, causing safety concerns. To enhance the safety of freight operations, providing accurate parking usage prediction proves to be a cost-effective solution. Despite the existing research demonstrating satisfactory accuracy for predicting individual truck parking site usage, few approaches have been proposed for predicting usage with spatial dependencies of multiple truck parking sites. We present the Regional Temporal Graph Neural Network (RegT-GCN) as a predictive framework for assessing parking usage across the entire state to provide better truck parking information and mitigate unauthorized parking. The framework leverages the topological structures of truck parking site distributions and historical parking data to predict occupancy rates across a state. To achieve this, we introduce a Regional Decomposition approach, which effectively captures the geographical characteristics. We also introduce the spatial module working efficiently with the temporal module. Evaluation results demonstrate that the proposed model surpasses other baseline models, improving the performance by more than $20\%$ compared with the original model. The proposed model allows truck parking sites' percipience of the topological structures and provides higher performance.
Instructional Fingerprinting of Large Language Models

The exorbitant cost of training Large language models (LLMs) from scratch makes it essential to fingerprint the models to protect intellectual property via ownership authentication and to ensure downstream users and developers comply with their license terms (e.g. restricting commercial use). In this study, we present a pilot study on LLM fingerprinting as a form of very lightweight instruction tuning. Model publisher specifies a confidential private key and implants it as an instruction backdoor that causes the LLM to generate specific text when the key is present. Results on 11 popularly-used LLMs showed that this approach is lightweight and does not affect the normal behavior of the model. It also prevents publisher overclaim, maintains robustness against fingerprint guessing and parameter-efficient training, and supports multi-stage fingerprinting akin to MIT License. Code is available in https://cnut1648.github. io/Model-Fingerprint/.
DAFA: Distance-Aware Fair Adversarial Training

The disparity in accuracy between classes in standard training is amplified during adversarial training, a phenomenon termed the robust fairness problem. Existing methodologies aimed to enhance robust fairness by sacrificing the model's performance on easier classes in order to improve its performance on harder ones. However, we observe that under adversarial attacks, the majority of the model's predictions for samples from the worst class are biased towards classes similar to the worst class, rather than towards the easy classes. Through theoretical and empirical analysis, we demonstrate that robust fairness deteriorates as the distance between classes decreases. Motivated by these insights, we introduce the Distance-Aware Fair Adversarial training (DAFA) methodology, which addresses robust fairness by taking into account the similarities between classes. Specifically, our method assigns distinct loss weights and adversarial margins to each class and adjusts them to encourage a trade-off in robustness among similar classes. Experimental results across various datasets demonstrate that our method not only maintains average robust accuracy but also significantly improves the worst robust accuracy, indicating a marked improvement in robust fairness compared to existing methods.
Constraint-Generation Policy Optimization (CGPO): Nonlinear Programming for Policy Optimization in Mixed Discrete-Continuous MDPs

We propose Constraint-Generation Policy Optimization (CGPO) for optimizing policy parameters within compact and interpretable policy classes for mixed discrete-continuous Markov Decision Processes (DC-MDPs). CGPO is not only able to provide bounded policy error guarantees over an infinite range of initial states for many DC-MDPs with expressive nonlinear dynamics, but it can also provably derive optimal policies in cases where it terminates with zero error. Furthermore, CGPO can generate worst-case state trajectories to diagnose policy deficiencies and provide counterfactual explanations of optimal actions. To achieve such results, CGPO proposes a bi-level mixed-integer nonlinear optimization framework for optimizing policies within defined expressivity classes (i.e. piecewise (non)-linear) and reduces it to an optimal constraint generation methodology that adversarially generates worst-case state trajectories. Furthermore, leveraging modern nonlinear optimizers, CGPO can obtain solutions with bounded optimality gap guarantees. We handle stochastic transitions through explicit marginalization (where applicable) or chance-constraints, providing high-probability policy performance guarantees. We also present a road-map for understanding the computational complexities associated with different expressivity classes of policy, reward, and transition dynamics. We experimentally demonstrate the applicability of CGPO in diverse domains, including inventory control, management of a system of water reservoirs, and physics control. In summary, we provide a solution for deriving structured, compact, and explainable policies with bounded performance guarantees, enabling worst-case scenario generation and counterfactual policy diagnostics.
Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms

Sound event localization and detection (SELD) is an important task in machine listening. Major advancements rely on simulated data with sound events in specific rooms and strong spatio-temporal labels. SELD data is simulated by convolving spatialy-localized room impulse responses (RIRs) with sound waveforms to place sound events in a soundscape. However, RIRs require manual collection in specific rooms. We present SpatialScaper, a library for SELD data simulation and augmentation. Compared to existing tools, SpatialScaper emulates virtual rooms via parameters such as size and wall absorption. This allows for parameterized placement (including movement) of foreground and background sound sources. SpatialScaper also includes data augmentation pipelines that can be applied to existing SELD data. As a case study, we use SpatialScaper to add rooms to the DCASE SELD data. Training a model with our data led to progressive performance improves as a direct function of acoustic diversity. These results show that SpatialScaper is valuable to train robust SELD models.
EL-VIT: Probing Vision Transformer with Interactive Visualization

Nowadays, Vision Transformer (ViT) is widely utilized in various computer vision tasks, owing to its unique self-attention mechanism. However, the model architecture of ViT is complex and often challenging to comprehend, leading to a steep learning curve. ViT developers and users frequently encounter difficulties in interpreting its inner workings. Therefore, a visualization system is needed to assist ViT users in understanding its functionality. This paper introduces EL-VIT, an interactive visual analytics system designed to probe the Vision Transformer and facilitate a better understanding of its operations. The system consists of four layers of visualization views. The first three layers include model overview, knowledge background graph, and model detail view. These three layers elucidate the operation process of ViT from three perspectives: the overall model architecture, detailed explanation, and mathematical operations, enabling users to understand the underlying principles and the transition process between layers. The fourth interpretation view helps ViT users and experts gain a deeper understanding by calculating the cosine similarity between patches. Our two usage scenarios demonstrate the effectiveness and usability of EL-VIT in helping ViT users understand the working mechanism of ViT.
A Multi-scale Yarn Appearance Model with Fiber Details

Rendering realistic cloth has always been a challenge due to its intricate structure. Cloth is made up of fibers, plies, and yarns, and previous curved-based models, while detailed, were computationally expensive and inflexible for large cloth. To address this, we propose a simplified approach. We introduce a geometric aggregation technique that reduces ray-tracing computation by using fewer curves, focusing only on yarn curves. Our model generates ply and fiber shapes implicitly, compensating for the lack of explicit geometry with a novel shadowing component. We also present a shading model that simplifies light interactions among fibers by categorizing them into four components, accurately capturing specular and scattered light in both forward and backward directions. To render large cloth efficiently, we propose a multi-scale solution based on pixel coverage. Our yarn shading model outperforms previous methods, achieving rendering speeds 3-5 times faster with less memory in near-field views. Additionally, our multi-scale solution offers a 20% speed boost for distant cloth observation.