Miracle-dz/UniFAR-FTUs huggingface dataset:
Technological Shocks and Algorithmic Decision Aids in Credence Goods Markets

In credence goods markets such as health care or repair services, consumers rely on experts with superior information to adequately diagnose and treat them. Experts, however, are constrained in their diagnostic abilities, which hurts market efficiency and consumer welfare. Technological breakthroughs that substitute or complement expert judgments have the potential to alleviate consumer mistreatment. This article studies how competitive experts adopt novel diagnostic technologies when skills are heterogeneously distributed and obfuscated to consumers. We differentiate between novel technologies that increase expert abilities, and algorithmic decision aids that complement expert judgments, but do not affect an expert's personal diagnostic precision. We show that high-ability experts may be incentivized to forego the decision aid in order to escape a pooling equilibrium by differentiating themselves from low-ability experts. Results from an online experiment support our hypothesis, showing that high-ability experts are significantly less likely than low-ability experts to invest into an algorithmic decision aid. Furthermore, we document pervasive under-investments, and no effect on expert honesty.
A Survey on Data-Centric Recommender Systems

Recommender systems (RSs) have become an essential tool for mitigating information overload in a range of real-world applications. Recent trends in RSs have revealed a major paradigm shift, moving the spotlight from model-centric innovations to data-centric efforts (e.g., improving data quality and quantity). This evolution has given rise to the concept of data-centric recommender systems (Data-Centric RSs), marking a significant development in the field. This survey provides the first systematic overview of Data-Centric RSs, covering 1) the foundational concepts of recommendation data and Data-Centric RSs; 2) three primary issues of recommendation data; 3) recent research developed to address these issues; and 4) several potential future directions of Data-Centric RSs.
How Being Inside or Outside of Buildings Affects the Causal Relationship Between Weather and Pain Among People Living with Chronic Pain

Although many people believe their pain fluctuates with weather conditions, both weather and pain may be associated with time spent outside. For example, pleasant weather may mean that people spend more time outside doing physical activity and exposed to the weather, leading to more (or less) pain, and poor weather or severe pain may keep people inside, sedentary, and not exposed to the weather. We conducted a smartphone study where participants with chronic pain reported daily pain severity, as well as time spent outside. We address the relationship between four weather variables (temperature, dewpoint temperature, pressure, and wind speed) and pain by proposing a three-step approach to untangle their effects: (i) propose a set of plausible directed acyclic graphs (also known as DAGs) that account for potential roles of time spent outside (e.g., collider, effect modifier, mediator), (ii) analyze the compatibility of the observed data with the assumed model, and (iii) identify the most plausible model by combining evidence from the observed data and domain-specific knowledge. We found that the data do not support time spent outside as a collider or mediator of the relationship between weather variables and pain. On the other hand, time spent outside modifies the effect between temperature and pain, as well as wind speed and pain, with the effect being absent on days that participants spent inside and present if they spent some or all of the day outside. Our results show the utility of using directed acyclic graphs for studying causal inference.
Computation and Parameter Efficient Multi-Modal Fusion Transformer for Cued Speech Recognition

Cued Speech (CS) is a pure visual coding method used by hearing-impaired people that combines lip reading with several specific hand shapes to make the spoken language visible. Automatic CS recognition (ACSR) seeks to transcribe visual cues of speech into text, which can help hearing-impaired people to communicate effectively. The visual information of CS contains lip reading and hand cueing, thus the fusion of them plays an important role in ACSR. However, most previous fusion methods struggle to capture the global dependency present in long sequence inputs of multi-modal CS data. As a result, these methods generally fail to learn the effective cross-modal relationships that contribute to the fusion. Recently, attention-based transformers have been a prevalent idea for capturing the global dependency over the long sequence in multi-modal fusion, but existing multi-modal fusion transformers suffer from both poor recognition accuracy and inefficient computation for the ACSR task. To address these problems, we develop a novel computation and parameter efficient multi-modal fusion transformer by proposing a novel Token-Importance-Aware Attention mechanism (TIAA), where a token utilization rate (TUR) is formulated to select the important tokens from the multi-modal streams. More precisely, TIAA firstly models the modality-specific fine-grained temporal dependencies over all tokens of each modality, and then learns the efficient cross-modal interaction for the modality-shared coarse-grained temporal dependencies over the important tokens of different modalities. Besides, a light-weight gated hidden projection is designed to control the feature flows of TIAA. The resulting model, named Economical Cued Speech Fusion Transformer (EcoCued), achieves state-of-the-art performance on all existing CS datasets (i.e., Mandarin Chinese, French, and British CS), compared with existing transformer-based fusion methods and ACSR fusion methods. Notably, our method dramatically reduces the computational complexity from $\mathcal {O}(T^{2})$ to $\mathcal {O}(T)$. We will release the source code and data as open source.
Desiderata For The Context Use Of Question Answering Systems

Prior work has uncovered a set of common problems in state-of-the-art context-based question answering (QA) systems: a lack of attention to the context when the latter conflicts with a model’s parametric knowledge, little robustness to noise, and a lack of consistency with their answers. However, most prior work focus on one or two of those problems in isolation, which makes it difficult to see trends across them. We aim to close this gap, by first outlining a set of – previously discussed as well as novel – desiderata for QA models. We then survey relevant analysis and methods papers to provide an overview of the state of the field. The second part of our work presents experiments where we evaluate 15 QA systems on 5 datasets according to all desiderata at once. We find many novel trends, including (1) systems that are less susceptible to noise are not necessarily more consistent with their answers when given irrelevant context; (2) most systems that are more susceptible to noise are more likely to correctly answer according to a context that conflicts with their parametric knowledge; and (3) the combination of conflicting knowledge and noise can reduce system performance by up to 96%. As such, our desiderata help increase our understanding of how these models work and reveal potential avenues for improvements.
The impact of plant morphology on flow structure: comparative analysis of two types of submerged flexible macrophyte

ABSTRACT The presence of aquatic vegetation in riverine and lacustrine environments alters the mean and turbulent flow structure and thus impacts the fate and transport of sediment and contaminants. Turbulent flows through Vallisneria natans (V. natans) and Potamogeton malaianus (P. malaianus) were investigated in a laboratory flume. The impact of plant morphology on mean velocity profile and turbulence distribution was analysed and discrepancies in flow alteration caused by different types of macrophyte were highlighted. Results show that a dense canopy of submerged macrophyte leads to a velocity profile featuring a counter velocity gradient in the lower part of the canopy. Negative Reynolds stress and its local maximum were observed there. Discrepancies in flow structure caused by different morphologies of both tested plants were further identified. With smaller frontal area in the lower part of the canopy, P. malaianus causes a much bigger gradient and local maximum in the velocity profile, and thus a larger local stress maximum than V. natans. The mean velocity gradient around the top of canopy, the Reynolds stress and the turbulence kinetic energy at the canopy interface are smaller than for the flow through the V. natans canopy. Larger reduction of the mean velocity within the V. natans canopy makes the suspended sediment of fine particles more easily deposited than in the P. malaianus canopy.
Identifying possible mechanism for quantum needle in chemical magnetoreception

The radical pair mechanism is an important model that may provide a basis for biological magnetoreception. To account for the high orientation precision of the real avian compass, P. J. Hore et al. proposed an intriguing phenomenon called quantum needle [Proc. Natl. Acad. Sci. 113, 4634 (2016)], where a spike-like feature emerges in the fractional yield signal. However, it is believed that quantum needle requires the radical pair lifetime to be longer than a few microseconds and thus poses stern challenges in realistic biological systems. Here, we exploit the optimization techniques and find a novel class of model system, which sustains much more prominent features of quantum needle and significantly relaxes the requirement for radical pair lifetime. Even more surprisingly, we find that the characteristics of quantum needle retain a narrow functional window around the geomagnetic field, which is absent in the previous model systems. Therefore, our work provides essential evidence for identifying the possible physical mechanism for quantum needle in chemical magnetoreception.
Electronic conduction and superconducting properties of CoSi$_2$ films on silicon--an unconventional superconductor with technological potential

We report observations of unusual normal-state electronic conduction properties and superconducting characteristics of high-quality CoSi$_2$/Si films grown on silicon Si(100) and Si(111) substrates. A good understanding of these features shall help to address the underlying physics of the unconventional pairing symmetry recently observed in transparent CoSi$_2$/TiSi$_2$ heterojunctions [S. P. Chiu \textit{et al.}, Sci. Adv. \textbf{7}, eabg6569 (2021); Nanoscale \textbf{15}, 9179 (2023)], where CoSi$_2$/Si is a superconductor with a superconducting transition temperature $T_c \simeq$ (1.1--1.5) K, dependent on its dimensions, and TiSi$_2$ is a normal metal. In CoSi$_2$/Si films, we find a pronounced positive magnetoresistance caused by the weak-antilocalization effect, indicating a strong Rashba spin-orbit coupling (SOC). This SOC generates two-component superconductivity in CoSi$_2$/TiSi$_2$ heterojunctions. The CoSi$_2$/Si films are stable under ambient conditions and have ultralow 1/$f$ noise. Moreover, they can be patterned via the standard lithography techniques, which might be of considerable practical value for future scalable superconducting and quantum device fabrication.
Joining Entities Across Relation and Graph with a Unified Model

This paper introduces RG (Relational Genetic) model, a revised relational model to represent graph-structured data in RDBMS while preserving its topology, for efficiently and effectively extracting data in different formats from disparate sources. Along with: (a) SQL$_\delta$, an SQL dialect augmented with graph pattern queries and tuple-vertex joins, such that one can extract graph properties via graph pattern matching, and"semantically"match entities across relations and graphs; (b) a logical representation of graphs in RDBMS, which introduces an exploration operator for efficient pattern querying, supports also browsing and updating graph-structured data; and (c) a strategy to uniformly evaluate SQL, pattern and hybrid queries that join tuples and vertices, all inside an RDBMS by leveraging its optimizer without performance degradation on switching different execution engines. A lightweight system, WhiteDB, is developed as an implementation to evaluate the benefits it can actually bring on real-life data. We empirically verified that the RG model enables the graph pattern queries to be answered as efficiently as in native graph engines; can consider the access on graph and relation in any order for optimal plan; and supports effective data enrichment.
On skew corner-free sets

We construct skew corner-free sets in $[n]^2$ of size $n^{5/4}$, thereby disproving a conjecture of Kevin Pratt. We also show that any skew corner-free set in $\mathbb{F}_{q}^{n} \times \mathbb{F}_{q}^{n}$ must have size at most $q^{(2-c)n}$, for some positive constant $c$ which depends on $q$.
Pixel to Elevation: Learning to Predict Elevation Maps at Long Range using Images for Autonomous Offroad Navigation

Understanding terrain topology at long-range is crucial for the success of off-road robotic missions, especially when navigating at high-speeds. LiDAR sensors, which are currently heavily relied upon for geometric mapping, provide sparse measurements when mapping at greater distances. To address this challenge, we present a novel learning-based approach capable of predicting terrain elevation maps at long-range using only onboard egocentric images in real-time. Our proposed method is comprised of three main elements. First, a transformer-based encoder is introduced that learns cross-view associations between the egocentric views and prior bird-eye-view elevation map predictions. Second, an orientation-aware positional encoding is proposed to incorporate the 3D vehicle pose information over complex unstructured terrain with multi-view visual image features. Lastly, a history-augmented learn-able map embedding is proposed to achieve better temporal consistency between elevation map predictions to facilitate the downstream navigational tasks. We experimentally validate the applicability of our proposed approach for autonomous offroad robotic navigation in complex and unstructured terrain using real-world offroad driving data. Furthermore, the method is qualitatively and quantitatively compared against the current state-of-the-art methods. Extensive field experiments demonstrate that our method surpasses baseline models in accurately predicting terrain elevation while effectively capturing the overall terrain topology at long-ranges. Finally, ablation studies are conducted to highlight and understand the effect of key components of the proposed approach and validate their suitability to improve offroad robotic navigation capabilities.
Supercloseness and asymptotic analysis of the Crouzeix-Raviart and enriched Crouzeix-Raviart elements for the Stokes problem

For the Crouzeix-Raviart and enriched Crouzeix-Raviart elements, asymptotic expansions of eigenvalues of the Stokes operator are derived by establishing two pseudostress interpolations, which admit a full one-order supercloseness with respect to the numerical velocity and the pressure, respectively. The design of these interpolations overcomes the difficulty caused by the lack of supercloseness of the canonical interpolations for the two nonconforming elements, and leads to an intrinsic and concise asymptotic analysis of numerical eigenvalues, which proves an optimal superconvergence of eigenvalues by the extrapolation algorithm. Meanwhile, an optimal superconvergence of postprocessed approximations for the Stokes equation is proved by use of this supercloseness. Finally, numerical experiments are tested to verify the theoretical results.
LeTO: Learning Constrained Visuomotor Policy with Differentiable Trajectory Optimization

This paper introduces LeTO, a method for learning constrained visuomotor policy via differentiable trajectory optimization. Our approach uniquely integrates a differentiable optimization layer into the neural network. By formulating the optimization layer as a trajectory optimization problem, we enable the model to end-to-end generate actions in a safe and controlled fashion without extra modules. Our method allows for the introduction of constraints information during the training process, thereby balancing the training objectives of satisfying constraints, smoothing the trajectories, and minimizing errors with demonstrations. This"gray box"method marries the optimization-based safety and interpretability with the powerful representational abilities of neural networks. We quantitatively evaluate LeTO in simulation and on the real robot. In simulation, LeTO achieves a success rate comparable to state-of-the-art imitation learning methods, but the generated trajectories are of less uncertainty, higher quality, and smoother. In real-world experiments, we deployed LeTO to handle constraints-critical tasks. The results show the effectiveness of LeTO comparing with state-of-the-art imitation learning approaches. We release our code at https://github.com/ZhengtongXu/LeTO.
Suppression of Blowup by Slightly Superlinear Degradation in a Parabolic-Elliptic Keller--Segel System with Signal-dependent Motility

In this paper, we consider an initial-Neumann boundary value problem for a parabolic-elliptic Keller-Segel system with signal-dependent motility and a source term. Previous research has rigorously shown that the source-free version of this system exhibits an infinite-time blowup phenomenon when dimension $N \geq 2$. In the current work, when $N \leq 3$, we establish uniform boundedness of global classical solutions with an additional source term that involves slightly super-linear degradation effect on the density, of a maximum growth order $s\log s$, unveiling a sufficient blowup suppression mechanism. The motility function considered in our work takes a rather general form compared with recent works \cite{FuJi2020, LyWa2023} which were restricted to the monotone non-increasing case. The cornerstone of our proof lies in deriving an upper bound for the second component of the system and an entropy-like estimate, which are achieved through tricky comparison skills and energy methods, respectively.
A Graph-Native Query Optimization Framework

Graph queries that combine pattern matching with relational operations, referred as PatRelQuery, are widely used in many real-world applications. It allows users to identify arbitrary patterns in a graph and further perform in-depth relational analysis on the results. To effectively support PatRelQuery, two key challenges need to be addressed: (1) how to optimize PatRelQuery in a unified framework, and (2) how to handle the arbitrary type constraints in patterns in PatRelQuery. In this paper, we present a graph-native query optimization framework named GOpt, to tackle these issues. GOpt is built on top of a unified intermediate representation (IR) that is capable of capturing both graph and relational operations, thereby streamlining the optimization of PatRelQuery. To handle the arbitrary type constraints, GOpt employs an automatic type inference approach to identify implicit type constraints. Additionally, GOpt introduces a graph-native optimizer, which encompasses an extensive collection of optimization rules along with cost-based techniques tailored for arbitrary patterns, to optimize PatRelQuery. Through comprehensive experiments, we demonstrate that GOpt can achieve significant query performance improvements, in both crafted benchmarks and real-world applications.
Prediction of stable nanoscale skyrmions in monolayer Fe$_5$GeTe$_2$

Using first-principles calculations and atomistic spin simulations, we predict stable isolated skyrmions with a diameter below 10 nm in a monolayer of the two-dimensional van der Waals ferromagnet Fe$_5$GeTe$_2$, a material of significant experimental interest. A very large Dzyaloshinskii-Moriya interaction (DMI) is observed due to the intrinsic broken inversion symmetry and strong spin-orbit coupling for monolayer Fe$_5$GeTe$_2$. We show that the nearest-neighbor approximation, often used in literature, fails to describe the DMI. The strong DMI together with moderate in-plane magnetocrystalline anisotropy energy allows to stabilize nanoscale skyrmions in out-of-plane magnetic fields above $\approx 2$~T. The energy barriers of skyrmions in monolayer Fe$_5$GeTe$_2$ are comparable to those of state-of-the-art transition-metal ultra-thin films. We further predict that these nanoscale skyrmions can be stable for hours at temperatures up to 20 K.
Double InfoGAN for Contrastive Analysis

Contrastive Analysis (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many applications, such as medical imaging. Current state-of-the-art (SOTA) methods are latent variable models based on VAE (CA-VAEs). However, they all either ignore important constraints or they don't enforce fundamental assumptions. This may lead to sub-optimal solutions where distinctive factors are mistaken for common ones (or viceversa). Furthermore, the generated images have a rather poor quality, typical of VAEs, decreasing their interpretability and usefulness. Here, we propose Double InfoGAN, the first GAN based method for CA that leverages the high-quality synthesis of GAN and the separation power of InfoGAN. Experimental results on four visual datasets, from simple synthetic examples to complex medical images, show that the proposed method outperforms SOTA CA-VAEs in terms of latent separation and image quality. Datasets and code are available online.
Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures

Distillation-based federated learning has emerged as a promising collaborative learning approach, where clients share the output logit vectors of a public dataset rather than their private model parameters. This practice reduces the risk of privacy invasion attacks and facilitates heterogeneous learning. The landscape of poisoning attacks within distillation-based federated learning is complex, with existing research employing traditional data poisoning strategies targeting the models' parameters. However, these attack schemes primarily have shortcomings rooted in their original designs, which target the model parameters rather than the logit vectors. Furthermore, they do not adequately consider the role of logit vectors in carrying information during the knowledge transfer process. This misalignment results in less efficiency in the context of distillation-based federated learning. Due to the limitations of existing methodologies, our research delves into the intrinsic properties of the logit vector, striving for a more nuanced understanding. We introduce a two-stage scheme for logit poisoning attacks, addressing previous shortcomings. Initially, we collect the local logits, generate the representative vectors, categorize the logit elements within the vector, and design a shuffling table to maximize information entropy. Then, we intentionally scale the shuffled logit vectors to enhance the magnitude of the target vectors. Concurrently, we propose an efficient defense algorithm to counter this new poisoning scheme by calculating the distance between estimated benign vectors and vectors uploaded by users. Through extensive experiments, our study illustrates the significant threat posed by the proposed logit poisoning attack and highlights the effectiveness of our defense algorithm.