Miracle-dz/UniFAR-FTUs huggingface dataset:
Imbalanced Node Processing Method in Graph Neural Network Classification Task

In recent years, the node classification task in graph neural networks(GNNs) has developed rapidly, driving the development of research in various fields. However, there are a large number of class imbalances in the graph data, and there is a large gap between the number of different classes, resulting in suboptimal results in classification. Proposing a solution to the imbalance problem has become indispensable for the successful advancement of our downstream missions. Therefore, we start with the loss function and try to find a loss function that can effectively solve the imbalance of graph nodes to participate in the node classification task. thence, we introduce GHMC Loss into the graph neural networks to deal with difficult samples that are not marginal. Attenuate the loss contribution of marginal samples and simple samples. Experiments on multiple benchmarks show that our method can effectively deal with the class imbalance problem, and our method improves the accuracy by 3% compared to the traditional loss function.
Laplace Matching for fast Approximate Inference in Latent Gaussian Models

Bayesian inference on non-Gaussian data is often non-analytic and requires computationally expensive approximations such as sampling or variational inference. We propose an approximate inference framework primarily designed to be computationally cheap while still achieving high approximation quality. The concept, which we call Laplace Matching, involves closed-form, approximate, bi-directional transformations between the parameter spaces of exponential families. These are constructed from Laplace approximations under custom-designed basis transformations. The mappings can then be leveraged to effectively turn a latent Gaussian distribution into an approximate conjugate prior to a rich class of observable variables. This allows us to train latent Gaussian models such as Gaussian Processes on non-Gaussian data at nearly no additional cost. The method can be thought of as a pre-processing step which can be implemented in <5 lines of code and runs in less than a second. Furthermore, Laplace Matching yields a simple way to group similar data points together, e.g. to produce inducing points for GPs. We empirically evaluate the method with experiments for four different exponential distributions, namely the Beta, Gamma, Dirichlet and inverse Wishart, showing approximation quality comparable to state-of-theart approximate inference techniques at a drastic reduction in computational cost.
Real-time frequency estimation of a qubit without single-shot-readout

Quantum sensors can potentially achieve the Heisenberg limit of sensitivity over a large dynamic range using quantum algorithms. The adaptive phase estimation algorithm (PEA) is one example that was proven to achieve such high sensitivities with single-shot readout (SSR) sensors. However, using the adaptive PEA on a non-SSR sensor is not trivial due to the low contrast nature of the measurement. The standard approach to account for the averaged nature of the measurement in this PEA algorithm is to use a method based on ‘majority voting’. Although it is easy to implement, this method is more prone to mistakes due to noise in the measurement. To reduce these mistakes, a binomial distribution technique from a batch selection was recently shown theoretically to be superior, as all ranges of outcomes from an averaged measurement are considered. Here we apply, for the first time, real-time non-adaptive PEA on a non-SSR sensor with the binomial distribution approach. We compare the mean square error of the binomial distribution method to the majority-voting approach using the nitrogen-vacancy center in diamond at ambient conditions as a non-SSR sensor. Our results suggest that the binomial distribution approach achieves better accuracy with the same sensing times. To further shorten the sensing time, we propose an adaptive algorithm that controls the readout phase and, therefore, the measurement basis set. We show by numerical simulation that adding the adaptive protocol can further improve the accuracy in a future real-time experiment.
How to talk so AI will learn: Instructions, descriptions, and autonomy

From the earliest years of our lives, humans use language to express our beliefs and desires. Being able to talk to artificial agents about our preferences would thus fulfill a central goal of value alignment. Yet today, we lack computational models explaining such language use. To address this challenge, we formalize learning from language in a contextual bandit setting and ask how a human might communicate preferences over behaviors. We study two distinct types of language: $\textit{instructions}$, which provide information about the desired policy, and $\textit{descriptions}$, which provide information about the reward function. We show that the agent's degree of autonomy determines which form of language is optimal: instructions are better in low-autonomy settings, but descriptions are better when the agent will need to act independently. We then define a pragmatic listener agent that robustly infers the speaker's reward function by reasoning about $\textit{how}$ the speaker expresses themselves. We validate our models with a behavioral experiment, demonstrating that (1) our speaker model predicts human behavior, and (2) our pragmatic listener successfully recovers humans' reward functions. Finally, we show that this form of social learning can integrate with and reduce regret in traditional reinforcement learning. We hope these insights facilitate a shift from developing agents that $\textit{obey}$ language to agents that $\textit{learn}$ from it.
Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees

Misinformation spread in online social networks is an urgent-to-solve problem having harmful consequences that threaten human health, public safety, economics, and so on. In this study, we construct a novel dataset, called MiDe-22, having 5,284 English and 5,064 Turkish tweets with their misinformation labels under several recent events, including the Russia-Ukraine war, COVID-19 pandemic, and Refugees. Moreover, we provide the user engagements to the tweets in terms of likes, replies, retweets, and quotes. We present a detailed data analysis with descriptive statistics and temporal analysis, and provide the experimental results of a benchmark evaluation for misinformation detection on our novel dataset.
Classification based credit risk analysis: The case of Lending Club

In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit derivative based on the idea of a Credit Default Swap, to hedge against an event of default. The results on the test set are presented using various performance measures.
Controlling unfolding in type theory

We present a novel mechanism for controlling the unfolding of deﬁnitions in dependent type theory. Traditionally, proof assistants let users specify whether each deﬁnition can or cannot be unfolded in the remainder of a development; unfolding deﬁnitions is often necessary in order to reason about them, but an excess of unfolding can result in brittle proofs and intractably large proof goals. In our system, deﬁnitions are by default not unfolded, but users can selectively unfold them in a local manner. We justify our mechanism by means of elaboration to a core type theory with extension types , a connective ﬁrst introduced in the context of homotopy type theory. We prove a normalization theorem for our core calculus and have implemented our system in the cooltt proof assistant, providing both theoretical and practical evidence for it.
Global Estimates of Spatially Distributed Surface Energy Fluxes using Thermodynamic Principles

: Limited surface observations of turbulent heat fluxes result in incomplete knowledge about the surface energy balance that drives the climate system. Here, we developed a novel, purely physics-based analytical method grounded on the thermodynamic principle of maximum power. The approach derives the turbulent heat flux only from the four inputs of incoming and outgoing radiations at the land surface. The proposed approach does not use any parameterization, unlike the existing surface energy balance models, and hence does not suffer from uncertainty due to the same.We validated our methodology with 102 eddy covariance observation stations around the globe with different land use land covers. Using the satellite observations from CERES at a spatial resolution of 1 0 , we have obtained spatially distributed global analytical estimates of Sensible (𝐻) , latent heat ( 𝐿𝐸 ), and land surface heat storage ( ∆𝑄 (cid:3046) ) fluxes for the first time. For a global observed land Net radiation ( 𝑄 ∗ ) of 84 Wm -2 from the satellite, we found 𝐻 , 𝐿𝐸 and ∆𝑄 (cid:3046) to be 42 Wm -2 , 40 Wm -2 and 2 Wm -2 , respectively. The theoretical and precise estimates of all surface energy balance components will improve our understanding of surface warming for different land use land covers across the globe.
Superresolution of Green's functions on noisy quantum computers

Quantum computers, using efficient Hamiltonian evolution routines, have the potential to simulate Green's functions of classically-intractable quantum systems. However, the decoherence errors of near-term quantum processors prohibit large evolution times, posing limits to the spectrum resolution. In this work, we show that Atomic Norm Minimization, a well-known super-resolution technique, can significantly reduce the minimum circuit depth for accurate spectrum reconstruction. We demonstrate this technique by recovering the spectral function of an impurity model from measurements of its Green's function on an IBM quantum computer. The reconstruction error with the Atomic Norm Minimization is one order of magnitude smaller than with more standard signal processing methods. Super-resolution methods can facilitate the simulation of large and previously unexplored quantum systems, and may constitute a useful non-variational tool to establish a quantum advantage in a nearer future.
Social Influence Dialogue Systems: A Scoping Survey of the Efforts Towards Influence Capabilities of Dialogue Systems

Dialogue systems capable of social inﬂuence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. However, existing research primarily focuses on either task-oriented or open-domain scenarios, a categorization that has been inadequate for capturing inﬂuence skills systematically. There exists no formal deﬁnition or category for dialogue systems with these skills and data-driven efforts in this direction are highly limited. In this work, we formally deﬁne and introduce the category of social inﬂuence dialogue systems that inﬂuence users’ cognitive and emotional responses, leading to changes in thoughts, opinions, and behaviors through natural conversations. We present a survey of various tasks, datasets, and methods, compiling the progress across seven diverse domains. We discuss the commonalities and differences between the examined systems, identify limitations, and recommend future directions. This study serves as a comprehensive reference for social inﬂuence dialogue systems to inspire more dedicated research and discussion in this emerging area.
Dependency-based anomaly detection

Anomaly (or outlier) detection techniques can be used to find occurrences in data that are surprising or unusual, arousing the suspicion of being generated by an aberrant mechanism. A collective anomaly is a collection of data instances of which the individual data instances may not be anomalies by themselves, but their occurrence together is anomalous with respect to the entire data set. An essential part of a collective anomaly detection technique is the manner in which instances are grouped. A dependency is a statistical relation between two events. When two events are dependent, the occurrence of one event affects the probability of occurrence of the other. For multi-dimensional data, dependencies can be used as a method to group instances for collective anomaly detection. When used in this manner, a dependency becomes an abstract object describing a collection of instances that are related in a statistically significant way. This exploratory study demonstrates the potential of using dependencies to find collective anomalies. During the research, anomalies have been found that no other method currently available would have been able to detect.
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios

The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained and instead discarded by the agent after a single use. In this paper, we present a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Results obtained show that the use of broad-persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer.
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption

There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose unbounded variance. This paper proposes two type of robust estimators, the ridge log-truncated M-estimator and the elastic net log-truncated M-estimator. The first estimator is applied to convex regressions such as quantile regression and generalized linear models, while the other one is applied to high dimensional non-convex learning problems such as regressions via deep neural networks. Simulations and real data analysis demonstrate the {robustness} of log-truncated estimations over standard estimations.
A Hierarchical Grouping Algorithm for the Multi-Vehicle Dial-a-Ride Problem

Ride-sharing is an essential aspect of modern urban mobility. In this paper, we consider a classical problem in ride-sharing - the Multi-Vehicle Dial-a-Ride Problem (Multi-Vehicle DaRP). Given a fleet of vehicles with a fixed capacity stationed at various locations and a set of ride requests specified by origins and destinations, the goal is to serve all requests such that no vehicle is assigned more passengers than its capacity at any point in its trip. We give an algorithm HGR, which is the first non-trivial approximation algorithm for the Multi-Vehicle DaRP. The main technical contribution is to reduce Multi-Vehicle DaRP to a certain capacitated partitioning problem, which we solve using a novel hierarchical grouping algorithm. Experimental results show that the vehicle routes produced by our algorithm not only exhibit less total travel distance compared to state-of-the-art baselines, but also enjoy a small in-transit latency, which crucially relates to each individual rider's traveling time. This suggests that HGR enhances rider experience while being energy-efficient.
Complex Relativity: Gravity and Electromagnetic Fields

We present new aspects of the electromagnetic field by introducting the natural potentials. These natural potentials are suitable for constructing the first order distortions of the metric tensor of Complex Relativity - the theory combining the General Relativity with the electromagnetic equations. A transition from antisymmetric tensors to the symmetric ones helps to define the natural potentials; their form fits a system of the Dirac matrices and this representation leads to distortion of the metric tensor. Our considerations have originated from the recent progresses in the asymmetric continuum theories. One version of such theories assumes an existence of the antisymmetric strain and stress fields; these fields originate due to some kind of internal friction in a continuum medium which have elastic bonds related to rotations of the particles.
Patient Knowledge, Risk Perception, and Barriers to Barrett's Esophagus Screening

INTRODUCTION: Most patients with esophageal adenocarcinoma (EAC) do not have a previous diagnosis of Barrett's esophagus (BE), demonstrating a failure of current screening practices. An understanding of patient attitudes and barriers is essential to develop and implement interventions to improve BE screening adherence. METHODS: We conducted a Web-based survey of patients aged >50 years with chronic gastroesophageal reflux disease at 3 academic medical centers and 1 affiliated safety net health systems. Survey domains included patient characteristics, endoscopy history, familiarity with screening practices, perceived BE/EAC risk, and barriers to screening. RESULTS: We obtained a response rate of 22.6% (472/2,084) (74% men, mean age 67.9 years). Self-identified race and ethnicity of participants was 66.5% non-Hispanic White, 20.0% non-Hispanic Black, 13.4% other race, and 7.1% Hispanic. Screening for BE was recommended in only 13.2%, and only 5.3% reported previous screening. Respondents had notable gaps in knowledge about screening indications; only two-thirds correctly identified BE risk factors and only 19.5% believed BE screening was needed for gastroesophageal reflux disease. More than 1 in 5 respondents believed they would get BE (31.9%) or EAC (20.2%) but reported barriers to screening. Compared with White respondents, more Black respondents were concerned about getting BE/EAC and interested in screening but report higher barriers to screening. DISCUSSION: Patients at risk for BE, particularly racial and ethnic minorities, are worried about developing EAC but rarely undergo screening and have poor understanding of screening recommendations.
Testing MOG, Non-Local Gravity and MOND with rotation curves of dwarf galaxies

Modified Gravity (MOG) and Non-Local Gravity (NLG) are two alternative theories to General Relativity. They are able to explain the rotation curves of spiral galaxies and clusters of galaxies without including dark matter (Moffat & Rahvar 2013, 2014; Rahvar & Mashhoon 2014). In the weak-field approximation these two theories have similar forms, with an effective gravitational potential that has two components: (i) Newtonian gravity with the gravitational constant enhanced by a factor $(1+\alpha)$ and (ii) a Yukawa type potential that produces a repulsive force with length scale $1/\mu$. In this work we compare the rotation curves of dwarf galaxies in the LITTLE THINGS catalog with predictions of MOG, NLG and Modified Newtonian Dynamics (MOND). We find that the universal parameters of these theories, can fit the rotation curve of dwarf galaxies with a larger stellar mass to the light ratio compared to the nearby stars in the Milky Way galaxy. Future direct observations of mass function of stars in the dwarf galaxies can examine different modified gravity models.
BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification

Predicting protein thermostability change upon mutation is crucial for understanding diseases and designing therapeutics. However, accurately estimating Gibbs free energy change of the protein remained a challenge. Some methods struggle to generalize on examples with no homology and produce uncalibrated predictions. Here we leverage advances in graph neural networks for protein feature extraction to tackle this structure–property prediction task. Our method, BayeStab, is then tested on four test datasets, including S669, S611, S350, and Myoglobin, showing high generalization and symmetry performance. Meanwhile, we apply concrete dropout enabled Bayesian neural networks to infer plausible models and estimate uncertainty. By decomposing the uncertainty into parts induced by data noise and model, we demonstrate that the probabilistic method allows insights into the inherent noise of the training datasets, which is closely relevant to the upper bound of the task. Finally, the BayeStab web server is created and can be found at: http://www.bayestab. com. The code for this work is available at: https://github.com/HongzhouTang/BayeStab.
Combined pulsed field ablation with ultra‐low temperature cryoablation: A preclinical experience

Combining pulsed field ablation (PFA) with ultra‐low temperature cryoablation (ULTC) represents a novel energy source which may create more transmural cardiac lesions. We sought to assess the feasibility of lesions created by combined cryoablation and pulsed field ablation (PFCA) versus PFA alone.
EspH interacts with the host active Bcr related (ABR) protein to suppress RhoGTPases

ABSTRACT Enteropathogenic Escherichia coli are bacterial pathogens that colonize the gut and cause severe diarrhea in humans. Upon intimate attachment to the intestinal epithelium, these pathogens translocate via a type III secretion system virulent proteins, termed effectors, into the host cells. These effectors manipulate diverse host cell organelles and functions for the pathogen’s benefit. However, the precise mechanisms underlying their activities are not fully understood despite intensive research. EspH, a critical effector protein, has been previously reported to disrupt the host cell actin cytoskeleton by suppressing RhoGTPase guanine exchange factors. However, native host proteins targeted by EspH to mediate these activities remained unknown. Here, we identified the active Bcr related (ABR), a protein previously characterized to possess dual Rho guanine nucleotide exchange factor and GTPase activating protein (GAP) domains, as a native EspH interacting partner. These interactions are mediated by the effector protein’s C-terminal 38 amino acid segment. The effector primarily targets the GAP domain of ABR to suppress Rac1 and Cdc42, host cell cytotoxicity, bacterial invasion, and filopodium formation at infection sites. Knockdown of ABR expression abolished the ability of EspH to suppress Rac1, Cdc42. Our studies unravel a novel mechanism by which host RhoGTPases are hijacked by bacterial effectors.
Individual- and Peer-Level Risk and Protective Factors for Gun Carriage Among Adolescents Living in Low-Income Urban Communities

Carrying a handgun is an established risk factor for firearm violence, with detrimental and too often irreversible consequences for adolescents including injury and mortality. Although researchers identified a number of risk factors for adolescent handgun carriage, little is known regarding the role of strengths or developmental assets in buffering against risk. The goal of this study was to identify both risk and protective factors for handgun carriage among a predominantly African American (88%) community-based sample of adolescents (Mage = 14.3) who resided in urban communities with high rates of poverty and exposure to violence. Consistent with prior work, we found that adolescents with access to a handgun or with friends who had carried a handgun had higher odds of carrying a handgun themselves in the past 3 months. Handgun access, friends’ handgun carriage, and beliefs supporting reactive aggression were identified as risk factors for handgun carriage. Although personal and social assets and positive outlook moderated these relations, the direction was contrary to our hypotheses. We believe that these findings are in part due to structural inequities and social norms impacting adolescents in urban communities characterized by concentrated poverty and high rates of violence. Our findings highlight the value of moving beyond a deficit-oriented framework to gain a more nuanced understanding of the dynamics among both positive and negative factors that alter risk for handgun carriage among African American youth living in low-income urban areas.