Miracle-dz/UniFAR-FTUs huggingface dataset:
Still Waters Run Deep: Comparing Assertive and Suggestive Language in Water Conservation Campaigns

The current work focuses on non-price policies to achieve residential water conservation, specifically on water conservation campaigns. The authors report the results of a large-scale longitudinal field experiment encouraging residential water conservation among 1500 households. The effectiveness of two commonly-used message phrasings is compared: an assertive and a suggestive message. Assertive messages employ a commanding tone, such as “You must conserve water”, whereas suggestive messages employ a more gentle approach, as in “Please consider conserving water”. Despite the ubiquitous use of assertive phrasing in pro-social messages, and previous research that suggests that, in some cases, assertive language can increase message compliance, the authors show here that the suggestive, gentler, message language can make a more accentuated change in residential water conservation behavior. This may stem from the status of water as a basic needs resource, which may reduce the appropriateness of freedom restricting language, such as an assertive tone.
Synthesis of applica(cid:415)on speciﬁc processor architectures for ultra-low energy consump(cid:415)on

—In this paper we suggest that further energy savings can be achieved by a new approach to synthesis of embedded processor cores, where the architecture is tailored to the algorithms that the core executes. In the context of embedded processor synthesis, both single-core and many-core, the types of algorithms and demands on the execution eﬃciency are usually known at the chip design time. This knowledge can be utilised at the design stage to synthesise architectures optimised for energy consumption. Firstly, we present an overview of both traditional energy saving techniques and new developments in architectural approaches to energy-eﬃcient processing. Secondly, we propose a pi-coMIPS architecture that serves as an architectural template for energy-eﬃcient synthesis. As a case study, we show how the picoMIPS architecture can be tailored to an energy eﬃcient execution of the DCT algorithm.
A systematic review and meta-analysis

Background: Glucocorticoid as the standard treatment of autoimmune hepatitis has been recommended with different doses. The purpose of this study is to compare the efficacy and safety of high and low doses for clinical practice. Methods: Medline, Embase, and Cochrane Library were searched until January 16th, 2019 for cohort studies or randomized controlled trials in patients with autoimmune hepatitis. Glucocorticoid 60mg/d or 1mg/kg/d was defined as high dose and 40 to 50 mg/d or 0.5mg/d as low dose. Outcome of interests includes the incidence of the biochemical remission, adverse event, and endpoint events. Double arcsine method with a random-effect model was used to combine the incidence. Potential heterogeneity was explored by meta-regression and subgroup analysis. Results:Overall, 25 studies (3305 patients) were included, with 10 studies in the high dose group and 15 in low dose group. The biochemical remission rate in the high and low dose group was 0.79 (95% confidence interval [CI] [0.72, 0.85]) and 0.72 (95% CI [0.65, 0.78]), respectively. The incidence of endpoint events and adverse event in the high were slightly higher (0.03, 95% CI [0.02, 0.04]; 0.42, 95% CI [0.30, 0.53]) than that of the low dose group (0.01, 95% CI [0.00, 0.01]; 0.39, 95% CI [0.15, 0.63]). Conclusions: For autoimmune hepatitis patients, 60mg/d or 1mg/kg/d of glucocorticoid gives higher biochemical remission rate and higher incidence of endpoint events and adverse events. Abbreviations: AASLD = American Association for the Study of Liver Diseases, AHRQ = Agency for Healthcare Research and Quality, AIH= autoimmune hepatitis, ALT= alanine aminotransferase, CI = confidence interval, EASL= European Association for the Study of the Liver.
A"Perspectival"Mirror of the Elephant: Investigating Language Bias on Google, ChatGPT, YouTube, and Wikipedia

Contrary to Google Search's mission of delivering information from"many angles so you can form your own understanding of the world,"we find that Google and its most prominent returned results - Wikipedia and YouTube - simply reflect a narrow set of culturally dominant views tied to the search language for complex topics like"Buddhism,""Liberalism,""colonization,""Iran"and"America. "Simply stated, they present, to varying degrees, distinct information across the same search in different languages, a phenomenon we call language bias. This paper presents evidence and analysis of language bias and discusses its larger social implications. We find that our online searches and emerging tools like ChatGPT turn us into the proverbial blind person touching a small portion of an elephant, ignorant of the existence of other cultural perspectives. Language bias sets a strong yet invisible cultural barrier online, where each language group thinks they can see other groups through searches, but in fact, what they see is their own reflection.
Binaural Speech Enhancement Using Deep Complex Convolutional Transformer Networks

Studies have shown that in noisy acoustic environments, providing binaural signals to the user of an assistive listening device may improve speech intelligibility and spatial awareness. This paper presents a binaural speech enhancement method using a complex convolutional neural network with an encoder-decoder architecture and a complex multi-head attention transformer. The model is trained to estimate individual complex ratio masks in the time-frequency domain for the left and right-ear channels of binaural hearing devices. The model is trained using a novel loss function that incorporates the preservation of spatial information along with speech intelligibility improvement and noise reduction. Simulation results for acoustic scenarios with a single target speaker and isotropic noise of various types show that the proposed method improves the estimated binaural speech intelligibility and preserves the binaural cues better in comparison with several baseline algorithms.
Technology-assisted Journal Writing for Improving Student Mental Wellbeing: Humanoid Robot vs. Voice Assistant

Conversational agents have a potential in improving student mental wellbeing while assisting them in self-disclosure activities such as journalling. Their embodiment might have an effect on what students disclose, and how they disclose this, and students overall adherence to the disclosure activity. However, the effect of embodiment in the context of agent assisted journal writing has not been studied. Therefore, this study aims to investigate the viability of using social robots (SR) and voice assistants (VA) for eliciting rich disclosures in journal writing that contributes to mental health status improvement in students over time. Forty two undergraduate and graduate students participated in the study that assessed the mood changes (via Brief Mood Introspection Scale, BMIS), level of subjective self-disclosure (via Subjective Self-Disclosure Questionnaire, SSDQ), and perceptions toward the agents (via Robot Social Attributes Scale, RoSAS) with and without agent (SR or VA) assisted journal writing. Results suggest that only in robot condition there are mood improvements, higher levels of disclosure, and positive perceptions over time in technology-assisted journal writing. Our results suggest that robot assisted journal writing has some advantages over voice assistant one for eliciting rich disclosures that contributes to mental health status improvement in students over time.
Overcoming Data Inequality across Domains with Semi-Supervised Domain Generalization

While there have been considerable advancements in machine learning driven by extensive datasets, a significant disparity still persists in the availability of data across various sources and populations. This inequality across domains poses challenges in modeling for those with limited data, which can lead to profound practical and ethical concerns. In this paper, we address a representative case of data inequality problem across domains termed Semi-Supervised Domain Generalization (SSDG), in which only one domain is labeled while the rest are unlabeled. We propose a novel algorithm, ProUD, which can effectively learn domain-invariant features via domain-aware prototypes along with progressive generalization via uncertainty-adaptive mixing of labeled and unlabeled domains. Our experiments on three different benchmark datasets demonstrate the effectiveness of ProUD, outperforming all baseline models including single domain generalization and semi-supervised learning. Source code will be released upon acceptance of the paper.
Restricted Bayesian Neural Network

Modern deep learning tools are remarkably effective in addressing intricate problems. However, their operation as black-box models introduces increased uncertainty in predictions. Additionally, they contend with various challenges, including the need for substantial storage space in large networks, issues of overfitting, underfitting, vanishing gradients, and more. This study explores the concept of Bayesian Neural Networks, presenting a novel architecture designed to significantly alleviate the storage space complexity of a network. Furthermore, we introduce an algorithm adept at efficiently handling uncertainties, ensuring robust convergence values without becoming trapped in local optima, particularly when the objective function lacks perfect convexity.
Load Balancing For High Performance Computing Using Quantum Annealing

With the advent of exascale computing, effective load balancing in massively parallel software applications is critically important for leveraging the full potential of high performance computing systems. Load balancing is the distribution of computational work between available processors. Here, we investigate the application of quantum annealing to load balance two paradigmatic algorithms in high performance computing. Namely, adaptive mesh refinement and smoothed particle hydrodynamics are chosen as representative grid and off-grid target applications. While the methodology for obtaining real simulation data to partition is application specific, the proposed balancing protocol itself remains completely general. In a grid based context, quantum annealing is found to outperform classical methods such as the round robin protocol but lacks a decisive advantage over more advanced methods such as steepest descent or simulated annealing despite remaining competitive. The primary obstacle to scalability is found to be limited coupling on current quantum annealing hardware. However, for the more complex particle formulation, approached as a multi-objective optimization, quantum annealing solutions are demonstrably Pareto dominant to state of the art classical methods across both objectives. This signals a noteworthy advancement in solution quality which can have a large impact on effective CPU usage.
AQuA: Automated Question-Answering in Software Tutorial Videos with Visual Anchors

Tutorial videos are a popular help source for learning feature-rich software. However, getting quick answers to questions about tutorial videos is difficult. We present an automated approach for responding to tutorial questions. By analyzing 633 questions found in 5,944 video comments, we identified different question types and observed that users frequently described parts of the video in questions. We then asked participants (N=24) to watch tutorial videos and ask questions while annotating the video with relevant visual anchors. Most visual anchors referred to UI elements and the application workspace. Based on these insights, we built AQuA, a pipeline that generates useful answers to questions with visual anchors. We demonstrate this for Fusion 360, showing that we can recognize UI elements in visual anchors and generate answers using GPT-4 augmented with that visual information and software documentation. An evaluation study (N=16) demonstrates that our approach provides better answers than baseline methods.
Spatiotemporal Predictive Pre-training for Robotic Motor Control

Robotic motor control necessitates the ability to predict the dynamics of environments and interaction objects. However, advanced self-supervised pre-trained visual representations (PVRs) in robotic motor control, leveraging large-scale egocentric videos, often focus solely on learning the static content features of sampled image frames. This neglects the crucial temporal motion clues in human video data, which implicitly contain key knowledge about sequential interacting and manipulating with the environments and objects. In this paper, we present a simple yet effective robotic motor control visual pre-training framework that jointly performs spatiotemporal predictive learning utilizing large-scale video data, termed as STP. Our STP samples paired frames from video clips. It adheres to two key designs in a multi-task learning manner. First, we perform spatial prediction on the masked current frame for learning content features. Second, we utilize the future frame with an extremely high masking ratio as a condition, based on the masked current frame, to conduct temporal prediction of future frame for capturing motion features. These efficient designs ensure that our representation focusing on motion information while capturing spatial details. We carry out the largest-scale evaluation of PVRs for robotic motor control to date, which encompasses 21 tasks within a real-world Franka robot arm and 5 simulated environments. Extensive experiments demonstrate the effectiveness of STP as well as unleash its generality and data efficiency by further post-pre-training and hybrid pre-training.
Quantum-critical properties of the one- and two-dimensional random transverse-field Ising model from large-scale quantum Monte Carlo simulations

We study the ferromagnetic transverse-field Ising model with quenched disorder at $T = 0$ in one and two dimensions by means of stochastic series expansion quantum Monte Carlo simulations using a rigorous zero-temperature scheme. Using a sample-replication method and averaged Binder ratios, we determine the critical shift and width exponents $\nu_\mathrm{s}$ and $\nu_\mathrm{w}$ as well as unbiased critical points by finite-size scaling. Further, scaling of the disorder-averaged magnetisation at the critical point is used to determine the order-parameter critical exponent $\beta$ and the critical exponent $\nu_{\mathrm{av}}$ of the average correlation length. The dynamic scaling in the Griffiths phase is investigated by measuring the local susceptibility in the disordered phase and the dynamic exponent $z'$ is extracted. By applying various finite-size scaling protocols, we provide an extensive and comprehensive comparison between the different approaches on equal footing. The emphasis on effective zero-temperature simulations resolves several inconsistencies in existing literature.
Generalized Yang Poisson models on canonical phase space

We discuss the generalized Yang-Poisson models. We costruct generalizations of the Yang Poisson algebra related to so(1, 5) algebra. The exact realizations of this generalized algebra on canonical phase space are presented and the corresponding differential equations are solved in simple case. Futhermore, we discuss a Poisson algebras related to so(3, 3) and so(2, 4) algebras.
Gravity as a topological gauge theory

We describe a topological gauge theory with underlying Cartan geometry $G/H$ and principal $H$-bundle. The topological action is constructed as a linear combination of the Euler and Pontrjagin numbers coming from entries of the curvature $\bar{\Omega}$ of a Cartan connection. As an example we show that for $G = SO(4,1)$ (respectively $SO(3,2)$) for $\Lambda_0>0$, (respectively $\Lambda_0<0$) and $H=SO(3,1)$, a very particular linear combination yields the Holst + Euler and Pontrjagin of the curvature $R$ of the spin connection + Nieh-Yan + bare cosmological constant $\Lambda_0$ terms. Additionally, in this construction, the coupling constants of these different terms are inherently linked together. The other example treated is the one of the M\"obius group $G= SO(4,2) /\{\pm I\}$ and $H = CO(4,2) \ltimes \mathbb{R}^{3,1}$. By considering a constraint on the"pair of frames"$(\alpha,\beta)$ we retrieve an action comprising all terms compatible with diffeomorphism invariance and local Lorentz invariance described in the previous example + kinetic term for a scalar field $+$ an interaction of that scalar field with the Torsion $T$, the scalar field being related to dilation. Finally, in both examples, we study the equations of motion associated to the total (Gauge + Matter) action. It is especially shown that one of the solutions in the M\"obius case consists of Einstein's equations modified by the Holst and Nieh-Yan terms with an additional source term for curvature depending on specific variations of spin density of matter.
UniTable: Towards a Unified Framework for Table Structure Recognition via Self-Supervised Pretraining

Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table structure recognition (TSR) has mainly centered around complex task-specific combinations of available inputs and tools. We present UniTable, a training framework that unifies both the training paradigm and training objective of TSR. Its training paradigm combines the simplicity of purely pixel-level inputs with the effectiveness and scalability empowered by self-supervised pretraining (SSP) from diverse unannotated tabular images. Our framework unifies the training objectives of all three TSR tasks - extracting table structure, cell content, and cell bounding box (bbox) - into a unified task-agnostic training objective: language modeling. Extensive quantitative and qualitative analyses highlight UniTable's state-of-the-art (SOTA) performance on four of the largest TSR datasets. To promote reproducible research, enhance transparency, and SOTA innovations, we open-source our code at https://github.com/poloclub/unitable and release the first-of-its-kind Jupyter Notebook of the whole inference pipeline, fine-tuned across multiple TSR datasets, supporting all three TSR tasks.
HGIC: A Hand Gesture Based Interactive Control System for Efficient and Scalable Multi-UAV Operations

As technological advancements continue to expand the capabilities of multi unmanned-aerial-vehicle systems (mUAV), human operators face challenges in scalability and efficiency due to the complex cognitive load and operations associated with motion adjustments and team coordination. Such cognitive demands limit the feasible size of mUAV teams and necessitate extensive operator training, impeding broader adoption. This paper developed a Hand Gesture Based Interactive Control (HGIC), a novel interface system that utilize computer vision techniques to intuitively translate hand gestures into modular commands for robot teaming. Through learning control models, these commands enable efficient and scalable mUAV motion control and adjustments. HGIC eliminates the need for specialized hardware and offers two key benefits: 1) Minimal training requirements through natural gestures; and 2) Enhanced scalability and efficiency via adaptable commands. By reducing the cognitive burden on operators, HGIC opens the door for more effective large-scale mUAV applications in complex, dynamic, and uncertain scenarios. HGIC will be open-sourced after the paper being published online for the research community, aiming to drive forward innovations in human-mUAV interactions.
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation

This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of anomaly detection that aligns with the real-time needs of distributed learning environments. Our method necessitates a majority consensus among participating clients to validate updates, ensuring that only vetted and consensual modifications are applied to the global model. The efficacy of our approach is validated through experiments on two benchmark datasets in deep learning, CIFAR-10 and MNIST. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience. This method transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead. The innovation of our approach rests in striking an optimal balance between heightened security measures and the inherent limitations of FL systems, such as computational efficiency and data privacy. Implementing a consensus mechanism specifically tailored for FL environments paves the way for more secure, robust, and trustworthy distributed machine learning applications, where safeguarding data integrity and model robustness is critical.
Simulating conditioned diffusions on manifolds

To date, most methods for simulating conditioned diffusions are limited to the Euclidean setting. The conditioned process can be constructed using a change of measure known as Doob's $h$-transform. The specific type of conditioning depends on a function $h$ which is typically unknown in closed form. To resolve this, we extend the notion of guided processes to a manifold $M$, where one replaces $h$ by a function based on the heat kernel on $M$. We consider the case of a Brownian motion with drift, constructed using the frame bundle of $M$, conditioned to hit a point $x_T$ at time $T$. We prove equivalence of the laws of the conditioned process and the guided process with a tractable Radon-Nikodym derivative. Subsequently, we show how one can obtain guided processes on any manifold $N$ that is diffeomorphic to $M$ without assuming knowledge of the heat kernel on $N$. We illustrate our results with numerical simulations and an example of parameter estimation where a diffusion process on the torus is observed discretely in time.
Real-Time Planning Under Uncertainty for AUVs Using Virtual Maps

Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation can be improved by incorporating the uncertainty prediction of future poses into the planning process and choosing actions that reduce uncertainty. However, performing belief propagation is computationally costly, especially when operating in large-scale environments. This work proposes a computationally efficient planning under uncertainty frame-work suitable for large-scale, feature-sparse environments. Our strategy leverages SLAM graph and occupancy map data obtained from a prior exploration phase to create a virtual map, describing the uncertainty of each map cell using a multivariate Gaussian. The virtual map is then used as a cost map in the planning phase, and performing belief propagation at each step is avoided. A receding horizon planning strategy is implemented, managing a goal-reaching and uncertainty-reduction tradeoff. Simulation experiments in a realistic underwater environment validate this approach. Experimental comparisons against a full belief propagation approach and a standard shortest-distance approach are conducted.