Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Sentiment Polarity Reduction in News Presentation through Contextual Perturbation and Large Language Models

In today's media landscape, where news outlets play a pivotal role in shaping public opinion, it is imperative to address the issue of sentiment manipulation within news text. News writers often inject their own biases and emotional language, which can distort the objectivity of reporting. This paper introduces a novel approach to tackle this problem by reducing the polarity of latent sentiments in news content. Drawing inspiration from adversarial attack-based sentence perturbation techniques and a prompt based method using ChatGPT, we employ transformation constraints to modify sentences while preserving their core semantics. Using three perturbation methods: replacement, insertion, and deletion coupled with a context-aware masked language model, we aim to maximize the desired sentiment score for targeted news aspects through a beam search algorithm. Our experiments and human evaluations demonstrate the effectiveness of these two models in achieving reduced sentiment polarity with minimal modifications while maintaining textual similarity, fluency, and grammatical correctness. Comparative analysis confirms the competitive performance of the adversarial attack based perturbation methods and prompt-based methods, offering a promising solution to foster more objective news reporting and combat emotional language bias in the media.
Geometry controls diffusive target encounters and escape in tubular structures

The endoplasmic reticulum (ER) is a network of sheet-like and tubular structures that spans much of a cell and contains molecules undergoing diffusive searches for targets, such as unfolded proteins searching for chaperones and recently-folded proteins searching for export sites. By applying a Brownian dynamics algorithm to simulate molecule diffusion, we describe how ER tube geometry influences whether a searcher will encounter a nearby target or instead diffuse away to a region near to a distinct target, as well as the timescale of successful searches. We find that targets are more likely to be found for longer and narrower tubes, and larger targets, and that search in the tube volume is more sensitive to the search geometry compared to search on the tube surface. Our results suggest ER proteins searching for low-density targets in the membrane and the lumen are very likely to encounter the nearest target before diffusing to the vicinity of another target. Our results have implications for the design of target search simulations and calculations and interpretation of molecular trajectories on the ER network, as well as other organelles with tubular geometry.
Affordable Generative Agents

The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents. However, the substantial cost on maintaining the prolonged agent interactions poses challenge over the deployment of believable LLM-based agents. Therefore, in this paper, we develop Affordable Generative Agents (AGA), a framework for enabling the generation of believable and low-cost interactions on both agent-environment and inter-agents levels. Specifically, for agent-environment interactions, we substitute repetitive LLM inferences with learned policies; while for inter-agent interactions, we model the social relationships between agents and compress auxiliary dialogue information. Extensive experiments on multiple environments show the effectiveness and efficiency of our proposed framework. Also, we delve into the mechanisms of emergent believable behaviors lying in LLM agents, demonstrating that agents can only generate finite behaviors in fixed environments, based upon which, we understand ways to facilitate emergent interaction behaviors. Our code is publicly available at: \url{https://github.com/AffordableGenerativeAgents/Affordable-Generative-Agents}.
One-class anomaly detection through color-to-thermal AI for building envelope inspection

We present a label-free method for detecting anomalies during thermographic inspection of building envelopes. It is based on the AI-driven prediction of thermal distributions from color images. Effectively the method performs as a one-class classifier of the thermal image regions with high mismatch between the predicted and actual thermal distributions. The algorithm can learn to identify certain features as normal or anomalous by selecting the target sample used for training. We demonstrated this principle by training the algorithm with data collected at different outdoors temperature, which lead to the detection of thermal bridges. The method can be implemented to assist human professionals during routine building inspections or combined with mobile platforms for automating examination of large areas.
Towards a Flexible Scale-out Framework for Efficient Visual Data Query Processing

There is growing interest in visual data management systems that support queries with specialized operations ranging from resizing an image to running complex machine learning models. With a plethora of such operations, the basic need to receive query responses in minimal time takes a hit, especially when the client desires to run multiple such operations in a single query. Existing systems provide an ad-hoc approach where different solutions are clubbed together to provide an end-to-end visual data management system. Unlike such solutions, the Visual Data Management System (VDMS) natively executes queries with multiple operations, thus providing an end-to-end solution. However, a fixed subset of native operations and a synchronous threading architecture limit its generality and scalability. In this paper, we develop VDMS-Async that adds the capability to run user-defined operations with VDMS and execute operations within a query on a remote server. VDMS-Async utilizes an event-driven architecture to create an efficient pipeline for executing operations within a query. Our experiments have shown that VDMS-Async reduces the query execution time by 2-3X compared to existing state-of-the-art systems. Further, remote operations coupled with an event-driven architecture enables VDMS-Async to scale query execution time linearly with the addition of every new remote server. We demonstrate a 64X reduction in query execution time when adding 64 remote servers.
Prediction modelling with many correlated and zero-inflated predictors: assessing a nonnegative garrote approach

Building prediction models from mass-spectrometry data is challenging due to the abundance of correlated features with varying degrees of zero-inflation, leading to a common interest in reducing the features to a concise predictor set with good predictive performance. In this study, we formally established and examined regularized regression approaches, designed to address zero-inflated and correlated predictors. In particular, we describe a novel two-stage regularized regression approach (ridge-garrote) explicitly modelling zero-inflated predictors using two component variables, comprising a ridge estimator in the first stage and subsequently applying a nonnegative garrote estimator in the second stage. We contrasted ridge-garrote with one-stage methods (ridge, lasso) and other two-stage regularized regression approaches (lasso-ridge, ridge-lasso) for zero-inflated predictors. We assessed the predictive performance and predictor selection properties of these methods in a comparative simulation study and a real-data case study to predict kidney function using peptidomic features derived from mass-spectrometry. In the simulation study, the predictive performance of all assessed approaches was comparable, yet the ridge-garrote approach consistently selected more parsimonious models compared to its competitors in most scenarios. While lasso-ridge achieved higher predictive accuracy than its competitors, it exhibited high variability in the number of selected predictors. Ridge-lasso exhibited slightly superior predictive accuracy than ridge-garrote but at the expense of selecting more noise predictors. Overall, ridge emerged as a favourable option when variable selection is not a primary concern, while ridge-garrote demonstrated notable practical utility in selecting a parsimonious set of predictors, with only minimal compromise in predictive accuracy.
Central Limit Theorem for R\'enyi Divergence of Infinite Order

For normalized sums $Z_n$ of i.i. d. random variables, we explore necessary and sufficient conditions which guarantee the normal approximation with respect to the R\'enyi divergence of infinite order. In terms of densities $p_n$ of $Z_n$, this is a strengthened variant of the local limit theorem taking the form $\sup_x (p_n(x) - \varphi(x))/\varphi(x) \rightarrow 0$ as $n \rightarrow \infty$.
Superconducting Qubits Above 20 GHz Operating over 200 mK

Current state-of-the-art superconducting microwave qubits are cooled to extremely low temperatures to avoid sources of decoherence. Higher qubit operating temperatures would significantly increase the cooling power available, which is desirable for scaling up the number of qubits in quantum computing architectures and integrating qubits in experiments requiring increased heat dissipation. To operate superconducting qubits at higher temperatures, it is necessary to address both quasiparticle decoherence (which becomes significant for aluminum junctions above 160 mK) and dephasing from thermal microwave photons (which are problematic above 50 mK). Using low-loss niobium trilayer junctions, which have reduced sensitivity to quasiparticles due to niobium's higher superconducting transition temperature, we fabricate transmons with higher frequencies than previously studied, up to 24 GHz. We measure decoherence and dephasing times of about 1 us, corresponding to average qubit quality factors of approximately $10^5$, and find that decoherence is unaffected by quasiparticles up to 1 K. Without relaxation from quasiparticles, we are able to explore dephasing from purely thermal sources, finding that our qubits can operate up to approximately 250 mK while maintaining similar performance. The thermal resilience of these qubits creates new options for scaling up quantum processors, enables hybrid quantum experiments with high heat dissipation budgets, and introduces a material platform for even higher-frequency qubits.
Importance sampling for online variational learning

This article addresses online variational estimation in state-space models. We focus on learning the smoothing distribution, i.e. the joint distribution of the latent states given the observations, using a variational approach together with Monte Carlo importance sampling. We propose an efficient algorithm for computing the gradient of the evidence lower bound (ELBO) in the context of streaming data, where observations arrive sequentially. Our contributions include a computationally efficient online ELBO estimator, demonstrated performance in offline and true online settings, and adaptability for computing general expectations under joint smoothing distributions.
Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model

Recent advances in wave modeling use sufficiently accurate fine solver outputs to train a neural network that enhances the accuracy of a fast but inaccurate coarse solver. In this paper we build upon the work of Nguyen and Tsai (2023) and present a novel unified system that integrates a numerical solver with a deep learning component into an end-to-end framework. In the proposed setting, we investigate refinements to the network architecture and data generation algorithm. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to correct high-frequency wave components. Our results show that the cohesive structure improves performance without sacrificing speed, and demonstrate the importance of temporal dynamics, as well as Parareal, for accurate wave propagation.
UAV-based remote sensing of fluvial hydrogeomorphology and aquatic habitat dynamics

Recent advances in methodological and theoretical approaches in fluvial sciences have given rise to increased interest in riverscape perspectives that embrace environmental variability and spatial relationships. These approaches facilitate interdisciplinary understanding of complex fluvial processes that supports conscientious management of river systems. In this context, this dissertation presents the development and application of novel methods to study fluvial structure and ecohydrogeomorphic relationships. Specifically, the research addresses three main questions: (1) how can UAV-based remote sensing advance the study of fluvial forms and processes? (2) what are the reach-scale geomorphic effects of an extreme flood and to what extent are these changes predictable? and (3) what are the linkages between flood-induced geomorphic change, reach hydraulics, and aquatic habitat? Results based on the case of an extreme flood event on Elbow River, Alberta demonstrate the utility of UAVs to efficiently and accurately measure many aspects of fluvial ecosystem form and function. The combination of high resolution imagery and photogrammetrically derived elevation models provides a powerful way to characterize rivers for a wide range of applications, particularly when combined with numerical flow modeling for a seamless representation of fluvial hydrogeomorphology. Preand post-flood UAV surveys documented flood effects with unprecedented detail, showing a largely unpredictable fluvial response characterized by complete channel planform reorganization and widespread bank erosion. These geomorphic changes negatively impacted the study reach in terms of hydraulic diversity and habitat suitability for brown trout and constrain future fluvial adjustment during smaller floods. Overall, the dissertation presents a new way to
Direct-a-Video: Customized Video Generation with User-Directed Camera Movement and Object Motion

Recent text-to-video diffusion models have achieved impressive progress. In practice, users often desire the ability to control object motion and camera movement independently for customized video creation. However, current methods lack the focus on separately controlling object motion and camera movement in a decoupled manner, which limits the controllability and flexibility of text-to-video models. In this paper, we introduce Direct-a-Video, a system that allows users to independently specify motions for one or multiple objects and/or camera movements, as if directing a video. We propose a simple yet effective strategy for the decoupled control of object motion and camera movement. Object motion is controlled through spatial cross-attention modulation using the model's inherent priors, requiring no additional optimization. For camera movement, we introduce new temporal cross-attention layers to interpret quantitative camera movement parameters. We further employ an augmentation-based approach to train these layers in a self-supervised manner on a small-scale dataset, eliminating the need for explicit motion annotation. Both components operate independently, allowing individual or combined control, and can generalize to open-domain scenarios. Extensive experiments demonstrate the superiority and effectiveness of our method. Project page: https://direct-a-video.github. io/.
Sample, estimate, aggregate: A recipe for causal discovery foundation models

Causal discovery, the task of inferring causal structure from data, promises to accelerate scientific research, inform policy making, and more. However, the per-dataset nature of existing causal discovery algorithms renders them slow, data hungry, and brittle. Inspired by foundation models, we propose a causal discovery framework where a deep learning model is pretrained to resolve predictions from classical discovery algorithms run over smaller subsets of variables. This method is enabled by the observations that the outputs from classical algorithms are fast to compute for small problems, informative of (marginal) data structure, and their structure outputs as objects remain comparable across datasets. Our method achieves state-of-the-art performance on synthetic and realistic datasets, generalizes to data generating mechanisms not seen during training, and offers inference speeds that are orders of magnitude faster than existing models.
UniMem: Towards a Unified View of Long-Context Large Language Models

Long-context processing is a critical ability that constrains the applicability of large language models. Although there exist various methods devoted to enhancing the long-context processing ability of large language models (LLMs), they are developed in an isolated manner and lack systematic analysis and integration of their strengths, hindering further developments. In this paper, we introduce UniMem, a unified framework that reformulates existing long-context methods from the view of memory augmentation of LLMs. UniMem is characterized by four key dimensions: Memory Management, Memory Writing, Memory Reading, and Memory Injection, providing a systematic theory for understanding various long-context methods. We reformulate 16 existing methods based on UniMem and analyze four representative methods: Transformer-XL, Memorizing Transformer, RMT, and Longformer into equivalent UniMem forms to reveal their design principles and strengths. Based on these analyses, we propose UniMix, an innovative approach that integrates the strengths of these algorithms. Experimental results show that UniMix achieves superior performance in handling long contexts with significantly lower perplexity than baselines.
Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs

Effective solutions for intelligent data collection in terrestrial cellular networks are crucial, especially in the context of Internet of Things applications. The limited spectrum and coverage area of terrestrial base stations pose challenges in meeting the escalating data rate demands of network users. Unmanned aerial vehicles, known for their high agility, mobility, and flexibility, present an alternative means to offload data traffic from terrestrial BSs, serving as additional access points. This paper introduces a novel approach to efficiently maximize the utilization of multiple UAVs for data traffic offloading from terrestrial BSs. Specifically, the focus is on maximizing user association with UAVs by jointly optimizing UAV trajectories and users association indicators under quality of service constraints. Since, the formulated UAVs control problem is nonconvex and combinatorial, this study leverages the multi agent reinforcement learning framework. In this framework, each UAV acts as an independent agent, aiming to maintain inter UAV cooperative behavior. The proposed approach utilizes the finite state Markov decision process to account for UAVs velocity constraints and the relationship between their trajectories and state space. A low complexity distributed state action reward state action algorithm is presented to determine UAVs optimal sequential decision making policies over training episodes. The extensive simulation results validate the proposed analysis and offer valuable insights into the optimal UAV trajectories. The derived trajectories demonstrate superior average UAV association performance compared to benchmark techniques such as Q learning and particle swarm optimization.
A classical density functional theory for solvation across length scales

A central aim of multiscale modeling is to use results from the Schr\"odinger Equation to predict phenomenology on length scales that far exceed those of typical molecular correlations. In this work, we present a new approach rooted in classical density functional theory (cDFT) that allows us to accurately describe the solvation of apolar solutes across length scales. Our approach builds on the Lum, Chandler and Weeks (LCW) theory of hydrophobicity [J. Phys. Chem. B 103, 4570 (1999)] by constructing a free energy functional that uses a slowly-varying component of the density field as a reference. From a practical viewpoint, the theory we present is numerically simpler and generalizes to solutes with soft-core repulsion more easily than LCW theory. Furthermore, we also provide two important conceptual insights. First, the coarse-graining of the density field emerges naturally and justifies a coarse-graining length much smaller than the molecular diameter of water. Second, by assessing the local compressibility and its critical scaling behavior, we demonstrate that our LCW-style cDFT approach contains the physics of critical drying, which has been emphasized as an essential aspect of hydrophobicity by recent theories. As our theory is parameterized on the two-body direct correlation function of the uniform fluid and the liquid-vapor surface tension, it straightforwardly captures the temperature dependence of solvation. Moreover, we use our theory to describe solvation at a first-principles level, on length scales that vastly exceed what is accessible to molecular simulations.
DogSurf: Quadruped Robot Capable of GRU-based Surface Recognition for Blind Person Navigation

This paper introduces DogSurf - a newapproach of using quadruped robots to help visually impaired people navigate in real world. The presented method allows the quadruped robot to detect slippery surfaces, and to use audio and haptic feedback to inform the user when to stop. A state-of-the-art GRU-based neural network architecture with mean accuracy of 99.925% was proposed for the task of multiclass surface classification for quadruped robots. A dataset was collected on a Unitree Go1 Edu robot. The dataset and code have been posted to the public domain.
CFD-DEM modeling of fracture initiation with polymer injection in granular media

We numerically study mechanisms and conditions of fracture initiation in granular material induced by non-Newtonian polymer solutions. A coupling approach of computational fluid dynamics and discrete element method is utilized to model the fluid flow in a porous medium. The flow behavior of polymer solutions and drag force acting on particles are calculated based on a power-law model. The adequacy of the numerical model is confirmed by comparing the results with a laboratory experiment. The numerical results are consistent with the experimental data presenting similar tendencies in dimensionless parameters that incorporate fluid flow rate, rheology, peak pressure, and confining stress. Results show that fluid flow rate, rheology, and solid material characteristics strongly influence fracture initiation behavior. Injecting a more viscous guar-based solution results in wider fractures induced by a grain displacement. A less viscous XG-based solution creates more linear fractures dominated by an infiltration. The peak pressure ratio between two fluids is higher in rigid material compared to softer material. Finally, the dimensionless parameters $1/\Pi_1$ and $\tau_2$, which consider fluid and solid material properties accordingly, are good indicators in determining fracture initiation induced by shear-thinning fluids. Our numerical results show that fracture initiation occurs above $1/\Pi_1 = 0.06$ and $\tau_2 = 2\cdot 10^{-7}$.