Miracle-dz/UniFAR-FTUs huggingface dataset:
Do We Price Happiness? Evidence from Korean Stock Market

This study explores the potential of internet search volume data, specifically Google Trends, as an indicator for cross-sectional stock returns. Unlike previous studies, our research specifically investigates the search volume of the topic 'happiness' and its impact on stock returns in the aspect of risk pricing rather than as sentiment measurement. Empirical results indicate that this 'happiness' search exposure (HSE) can explain future returns, particularly for big and value firms. This suggests that HSE might be a reflection of a firm's ability to produce goods or services that meet societal utility needs. Our findings have significant implications for institutional investors seeking to leverage HSE-based strategies for outperformance. Additionally, our research suggests that, when selected judiciously, some search topics on Google Trends can be related to risks that impact stock prices.
Solutions to discrete fractional Sch\"{o}dinger equations

In this paper, we study the discrete fractional Schr\"{o}dinger equation $$ (-\Delta)^\alpha u+h(x) u=f(x,u),\quad x\in \mathbb{Z}^d,$$ where $d\in\mathbb{N}^*,\,\alpha \in(0, 1)$ and the nonlocal operator $(-\Delta)^\alpha $ is defined by discrete Fourier transform, which differs from the continuous case. Under suitable assumptions on $h$ and $f$, we prove the existence and multiplicity of solutions to this equation by variational method.
Omnidirectional Information Gathering for Knowledge Transfer-based Audio-Visual Navigation

Audio-visual navigation is an audio-targeted wayfinding task where a robot agent is entailed to travel a never-before-seen 3D environment towards the sounding source. In this article, we present ORAN, an omnidirectional audiovisual navigator based on cross-task navigation skill transfer. In particular, ORAN sharpens its two basic abilities for a such challenging task, namely wayfinding and audiovisual information gathering. First, ORAN is trained with a confidence-aware cross-task policy distillation (CCPD) strategy. CCPD transfers the fundamental, point-to-point wayfinding skill that is well trained on the large-scale Point-Goal task to ORAN, so as to help ORAN to better master audio-visual navigation with far fewer training samples. To improve the efficiency of knowledge transfer and address the domain gap, CCPD is made to be adaptive to the decision confidence of the teacher policy. Second, ORAN is equipped with an omnidirectional information gathering (OIG) mechanism, i.e., gleaning visual-acoustic observations from different directions before decision-making. As a result, ORAN yields more robust navigation behaviour. Taking CCPD and OIG together, ORAN significantly outperforms previous competitors. After the model ensemble, we got 1st in Soundspaces Challenge 2022, improving SPL and SR by 53% and 35% relatively.
Dexterous Soft Hands Linearize Feedback-Control for In-Hand Manipulation

This paper presents a feedback-control framework for in-hand manipulation (IHM) with dexterous soft hands that enables the acquisition of manipulation skills in the real-world within minutes. We choose the deformation state of the soft hand as the control variable. To control for a desired deformation state, we use coarsley approximated Jacobians of the actuation-deformation dynamics. These Jacobian are obtained via explorative actions. This is enabled by the self-stabilizing properties of compliant hands, which allow us to use linear feedback control in the presence of complex contact dynamics. To evaluate the effectiveness of our approach, we show the generalization capabilities for a learned manipulation skill to variations in object size by 100 %, 360 degree changes in palm inclination and to disabling up to 50 % of the involved actuators. In addition, complex manipulations can be obtained by sequencing such feedback-skills.
Stochastic Optimization of Coupled Power Distribution-Urban Transportation Network Operations with Autonomous Mobility on Demand Systems

Autonomous mobility on demand systems (AMoDS) will significantly affect the operation of coupled power distribution-urban transportation networks (PTNs) by the optimal dispatch of electric vehicles (EVs). This paper proposes an uncertainty method to analyze the operational states of PTNs with AMoDS. First, a PTN operation framework is designed considering the controllable EVs dispatched by AMoDS as well as the uncontrollable driving behaviors of other vehicle users. Then, a bi-level power-traffic flow (PTF) model is proposed to characterize the interaction of power distribution networks (PDNs) and urban transportation networks (UTNs). In the upper level, a social optimum model is established to minimize the operating cost of PDNs and UTNs embedded with controllable EVs. In the lower level, a stochastic user equilibrium (SUE) model is established to minimize the operating cost of uncontrollable EVs and gasoline vehicles (GVs) in UTNs. Finally, a probabilistic PTF analysis method is developed to evaluate PTN operations under environmental and human uncertainties. A regional sensitivity analysis method is proposed to identify the critical uncertainties and quantify the impacts of their distribution ranges on PTN operations. The effectiveness of the proposed method is verified by the PTN consisting of a 21-bus PDN and a 20-node UTN.
Bayesian polynomial neural networks and polynomial neural ordinary differential equations

Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of many science and engineering problems. However, these methods provide point estimates for the model parameters and are currently unable to accommodate noisy data. We address this challenge by developing and validating the following Bayesian inference methods: the Laplace approximation, Markov Chain Monte Carlo (MCMC) sampling methods, and variational inference. We have found the Laplace approximation to be the best method for this class of problems. Our work can be easily extended to the broader class of symbolic neural networks to which the polynomial neural network belongs.
Modeling Random Networks with Heterogeneous Reciprocity

Reciprocity, or the tendency of individuals to mirror behavior, is a key measure that describes information exchange in a social network. Users in social networks tend to engage in different levels of reciprocal behavior. Differences in such behavior may indicate the existence of communities that reciprocate links at varying rates. In this paper, we develop methodology to model the diverse reciprocal behavior in growing social networks. In particular, we present a preferential attachment model with heterogeneous reciprocity that imitates the attraction users have for popular users, plus the heterogeneous nature by which they reciprocate links. We compare Bayesian and frequentist model fitting techniques for large networks, as well as computationally efficient variational alternatives. Cases where the number of communities are known and unknown are both considered. We apply the presented methods to the analysis of a Facebook wallpost network where users have non-uniform reciprocal behavior patterns. The fitted model captures the heavy-tailed nature of the empirical degree distributions in the Facebook data and identifies multiple groups of users that differ in their tendency to reply to and receive responses to wallposts.
Preserving Specificity in Federated Graph Learning for fMRI-based Neurological Disorder Identification

Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive approach to examining abnormal brain connectivity associated with brain disorders. Graph neural network (GNN) gains popularity in fMRI representation learning and brain disorder analysis with powerful graph representation capabilities. Training a general GNN often necessitates a large-scale dataset from multiple imaging centers/sites, but centralizing multi-site data generally faces inherent challenges related to data privacy, security, and storage burden. Federated Learning (FL) enables collaborative model training without centralized multi-site fMRI data. Unfortunately, previous FL approaches for fMRI analysis often ignore site-specificity, including demographic factors such as age, gender, and education level. To this end, we propose a specificity-aware federated graph learning (SFGL) framework for rs-fMRI analysis and automated brain disorder identification, with a server and multiple clients/sites for federated model aggregation and prediction. At each client, our model consists of a shared and a personalized branch, where parameters of the shared branch are sent to the server while those of the personalized branch remain local. This can facilitate knowledge sharing among sites and also helps preserve site specificity. In the shared branch, we employ a spatio-temporal attention graph isomorphism network to learn dynamic fMRI representations. In the personalized branch, we integrate vectorized demographic information (i.e., age, gender, and education years) and functional connectivity networks to preserve site-specific characteristics. Representations generated by the two branches are then fused for classification. Experimental results on two fMRI datasets with a total of 1218 subjects suggest that SFGL outperforms several state-of-the-art approaches.
Asymptotics of solving a singularly perturbed system of transport equations with fast and slow components in the critical case

An asymptotic expansion with respect to a small parameter of the solution of the Cauchy problem is constructed for a system of three transfer equations, two of which are singularly perturbed by the degeneracy of the entire senior part of the transfer operator, and the third equation clearly does not contain a small parameter. The peculiarity of the problem is that it belongs to the so-called critical case: the solution of a degenerate problem is a one-parameter family. The asymptotic expansion of the solution under smooth initial conditions is constructed as the sum of the regular part and boundary functions.
Reliability and Delay Analysis of 3-Dimensional Networks With Multi-Connectivity: Satellite, HAPs, and Cellular Communications

Aerial vehicles (AVs) such as electric vertical take-off and landing (eVTOL) aircraft make aerial passenger transportation a reality in urban environments. However, their communication connectivity is still under research to realize their safe and full-scale operation. This paper envisages a multi-connectivity (MC) enabled aerial network to provide ubiquitous and reliable service to AVs. Vertical heterogeneous networks with direct air-to-ground (DA2G) and air-to-air (A2A) communication, high altitude platforms (HAPs), and low Earth orbit (LEO) satellites are considered. We evaluate the end-to-end (E2E) multi-hop reliability and network availability of the downlink of AVs for remote piloting scenarios, and control/telemetry traffic. Command and control (C2) connectivity service requires ultra-reliable and low-latency communication (URLLC), therefore we analyse E2E reliability and latency under the finite blocklength (FBL) regime. We explore how different MC options satisfy the demanding E2E connectivity requirements taking into account antenna radiation patterns and unreliable backhaul links. Since providing seamless connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antennas, we use coordinated multi-point (CoMP) among ground BSs to alleviate the inter-cell interference. Furthermore, we solve an optimization problem to select the best MC path under the quality of service (QoS) constraints. We maximize spectral efficiency (SE) to specify the optimum MC path with the minimum number of required links. Based on the simulation results, we find out that even with very efficient interference mitigation, MC is the key enabler for safe remote piloting operations.
False Negative/Positive Control for SAM on Noisy Medical Images

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code will be released soon.
Tree Drawings with Columns

Our goal is to visualize an additional data dimension of a tree with multifaceted data through superimposition on vertical strips, which we call columns. Specifically, we extend upward drawings of unordered rooted trees where vertices have assigned heights by mapping each vertex to a column. Under an orthogonal drawing style and with every subtree within a column drawn planar, we consider different natural variants concerning the arrangement of subtrees within a column. We show that minimizing the number of crossings in such a drawing can be achieved in fixed-parameter tractable (FPT) time in the maximum vertex degree $\Delta$ for the most restrictive variant, while becoming NP-hard (even to approximate) already for a slightly relaxed variant. However, we provide an FPT algorithm in the number of crossings plus $\Delta$, and an FPT-approximation algorithm in $\Delta$ via a reduction to feedback arc set.
Rafting Towards Consensus: Formation Control of Distributed Dynamical Systems

In this paper, we introduce a novel adaptation of the Raft consensus algorithm for achieving emergent formation control in multi-agent systems with a single integrator dynamics. This strategy, dubbed"Rafting,"enables robust cooperation between distributed nodes, thereby facilitating the achievement of desired geometric configurations. Our framework takes advantage of the Raft algorithm's inherent fault tolerance and strong consistency guarantees to extend its applicability to distributed formation control tasks. Following the introduction of a decentralized mechanism for aggregating agent states, a synchronization protocol for information exchange and consensus formation is proposed. The Raft consensus algorithm combines leader election, log replication, and state machine application to steer agents toward a common, collaborative goal. A series of detailed simulations validate the efficacy and robustness of our method under various conditions, including partial network failures and disturbances. The outcomes demonstrate the algorithm's potential and open up new possibilities in swarm robotics, autonomous transportation, and distributed computation. The implementation of the algorithms presented in this paper is available at https://github.com/abbas-tari/raft.git.
How susceptible are LLMs to Logical Fallacies?

This paper investigates the rational thinking capability of Large Language Models (LLMs) in multi-round argumentative debates by exploring the impact of fallacious arguments on their logical reasoning performance. More specifically, we present Logic Competence Measurement Benchmark (LOGICOM), a diagnostic benchmark to assess the robustness of LLMs against logical fallacies. LOGICOM involves two agents: a persuader and a debater engaging in a multi-round debate on a controversial topic, where the persuader tries to convince the debater of the correctness of its claim. First, LOGICOM assesses the potential of LLMs to change their opinions through reasoning. Then, it evaluates the debater's performance in logical reasoning by contrasting the scenario where the persuader employs logical fallacies against one where logical reasoning is used. We use this benchmark to evaluate the performance of GPT-3.5 and GPT-4 using a dataset containing controversial topics, claims, and reasons supporting them. Our findings indicate that both GPT-3.5 and GPT-4 can adjust their opinion through reasoning. However, when presented with logical fallacies, GPT-3.5 and GPT-4 are erroneously convinced 41% and 69% more often, respectively, compared to when logical reasoning is used. Finally, we introduce a new dataset containing over 5k pairs of logical vs. fallacious arguments. The source code and dataset of this work are made publicly available.
Conformal Theory of Gravitation and Cosmic Expansion

The postulate of universal Weyl conformal symmetry for all elementary physical fields introduces nonclassical gravitational effects in both conformal gravitation (CG) and the conformal Higgs model (CHM). The resulting theory is found to explain major observed phenomena, including excessive galactic rotation velocities and accelerating Hubble expansion, without invoking dark matter (DM). The recent history of this development is surveyed here. The argument is confined to implications of classical field theory, which include galactic baryonic Tully–Fisher relationships and dark galactic haloes of a definite large radius. Cosmological CHM parameters exclude a massive Higgs boson but are consistent with a novel alternative particle of the observed mass.
Analytical valuation of vulnerable derivative contracts with bilateral cash ﬂows under credit, funding and wrong-way risks

We study the problem of valuing a vulnerable derivative with bilateral cash ﬂows between two counterparties in the presence of funding, credit and wrong-way risks, and derive a closed-form valuation formula for an at-the-money (ATM) forward contract as well as a second order approximation for the general case. We posit a model with heterogeneous interest rates and default occurrence and infer a Cauchy problem for the pre-default valuation function of the contract, which includes ab initio any counterparty risk – as opposed to calculating valuation adjustments collectively known as XVA. Under a speciﬁc funding policy which linearises the Cauchy problem, we obtain a generic probabilistic representation for the pre-default valuation (Theorem 1). We apply this general framework to the valuation of an equity forward and establish the contract can be expressed as a continuous portfolio of European options with suitably chosen strikes and expiries under a particular probability measure (Theorem 2). Our valuation formula admits a closed-form expression when the forward contract is ATM (Corollary 2) and we derive a second order approximation in moneyness when the contract is close to ATM (Theorem 3). Numerical results of our model show that the forward is more sensitive to funding factors than credit ones, while higher stock funding costs increase sensitivity to credit spreads and wrong-way risk.
Ultrafast and Ultralight Network-Based Intelligent System for Real-time Diagnosis of Ear diseases in Any Devices

Traditional ear disease diagnosis heavily depends on experienced specialists and specialized equipment, frequently resulting in misdiagnoses, treatment delays, and financial burdens for some patients. Utilizing deep learning models for efficient ear disease diagnosis has proven effective and affordable. However, existing research overlooked model inference speed and parameter size required for deployment. To tackle these challenges, we constructed a large-scale dataset comprising eight ear disease categories and normal ear canal samples from two hospitals. Inspired by ShuffleNetV2, we developed Best-EarNet, an ultrafast and ultralight network enabling real-time ear disease diagnosis. Best-EarNet incorporates the novel Local-Global Spatial Feature Fusion Module which can capture global and local spatial information simultaneously and guide the network to focus on crucial regions within feature maps at various levels, mitigating low accuracy issues. Moreover, our network uses multiple auxiliary classification heads for efficient parameter optimization. With 0.77M parameters, Best-EarNet achieves an average frames per second of 80 on CPU. Employing transfer learning and five-fold cross-validation with 22,581 images from Hospital-1, the model achieves an impressive 95.23% accuracy. External testing on 1,652 images from Hospital-2 validates its performance, yielding 92.14% accuracy. Compared to state-of-the-art networks, Best-EarNet establishes a new state-of-the-art (SOTA) in practical applications. Most importantly, we developed an intelligent diagnosis system called Ear Keeper, which can be deployed on common electronic devices. By manipulating a compact electronic otoscope, users can perform comprehensive scanning and diagnosis of the ear canal using real-time video. This study provides a novel paradigm for ear endoscopy and other medical endoscopic image recognition applications.
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method

Neural ranking models (NRMs) and dense retrieval (DR) models have given rise to substantial improvements in overall retrieval performance. In addition to their effectiveness, and motivated by the proven lack of robustness of deep learning-based approaches in other areas, there is growing interest in the robustness of deep learning-based approaches to the core retrieval problem. Adversarial attack methods that have so far been developed mainly focus on attacking NRMs, with very little attention being paid to the robustness of DR models. In this paper, we introduce the adversarial retrieval attack (AREA) task. The AREA task is meant to trick DR models into retrieving a target document that is outside the initial set of candidate documents retrieved by the DR model in response to a query. We consider the decision-based black-box adversarial setting, which is realistic in real-world search engines. To address the AREA task, we first employ existing adversarial attack methods designed for NRMs. We find that the promising results that have previously been reported on attacking NRMs, do not generalize to DR models: these methods underperform a simple term spamming method. We attribute the observed lack of generalizability to the interaction-focused architecture of NRMs, which emphasizes fine-grained relevance matching. DR models follow a different representation-focused architecture that prioritizes coarse-grained representations. We propose to formalize attacks on DR models as a contrastive learning problem in a multi-view representation space. The core idea is to encourage the consistency between each view representation of the target document and its corresponding viewer via view-wise supervision signals. Experimental results demonstrate that the proposed method can significantly outperform existing attack strategies in misleading the DR model with small indiscernible text perturbations.