Miracle-dz/UniFAR-FTUs huggingface dataset:
Preferences Evolve And So Should Your Bandits: Bandits with Evolving States for Online Platforms

We propose a model for learning with bandit feedback while accounting for deterministically evolving and unobservable states that we call Bandits with Deterministically Evolving States ($B$-$DES$). The workhorse applications of our model are learning for recommendation systems and learning for online ads. In both cases, the reward that the algorithm obtains at each round is a function of the short-term reward of the action chosen and how"healthy"the system is (i.e., as measured by its state). For example, in recommendation systems, the reward that the platform obtains from a user's engagement with a particular type of content depends not only on the inherent features of the specific content, but also on how the user's preferences have evolved as a result of interacting with other types of content on the platform. Our general model accounts for the different rate $\lambda \in [0,1]$ at which the state evolves (e.g., how fast a user's preferences shift as a result of previous content consumption) and encompasses standard multi-armed bandits as a special case. The goal of the algorithm is to minimize a notion of regret against the best-fixed sequence of arms pulled, which is significantly harder to attain compared to standard benchmark of the best-fixed action in hindsight. We present online learning algorithms for any possible value of the evolution rate $\lambda$ and we show the robustness of our results to various model misspecifications.
Mass Accretion, Spectral, and Photometric Properties of T Tauri Stars in Taurus Based on TESS and LAMOST

We present the analysis of 16 classical T Tauri stars (CTTSs) using LAMOST and TESS data, investigating spectral properties, photometric variations, and mass accretion rates. All 16 stars exhibit emissions in Hα lines, from which the average mass accretion rate of 1.76 × 10−9 M ⊙ yr−1 is derived. Two of the stars, DL Tau and Haro 6-13, show mass accretion bursts simultaneously in TESS, ASAS-SN, and/or the ZTF survey. Based on these observations, we find that the mass accretion rates of DL Tau and Haro 6-13 reach their maxima of 2.5 × 10−8 M ⊙ yr−1 and 2 × 10−10 M ⊙ yr−1, respectively, during the TESS observation. We detect 13 flares among these stars. The flare frequency distribution shows that the CTTSs’ flare activity is not only dominated by strong flares with high energy but also much more active than those of solar-type and young low-mass stars. By comparing the variability classes reported in the literature, we find that the transition timescale between different classes of variability in CTTSs, such as from stochastic (S) to bursting (B) or from quasi-periodic symmetric to quasi-periodic dipping, may range from 1.6 to 4 yr. We observe no significant correlation between inclination and mass accretion rates derived from the emission indicators. This suggests that inner disk properties may be more important than those of outer disks. Finally, we find a relatively significant positive correlation between the asymmetric metric M and the cold disk inclination compared to the literature. A weak negative correlation between the periodicity metric Q value and inclination has also been found.
$L^{p}$ boundedness for singular integrals with rough kernels on product domains

In this paper we study a class of singular integral operators defined by polynomial mappings on product domains. We prove the L p boundedness of such singular integral operators under very weak conditions on the singular kernels. For the corresponding maximal truncated singular integral operators we also obtained appropriate L p bounds which were previously unavailable even in relateviely simple settings.
OUTFOX: LLM-generated Essay Detection through In-context Learning with Adversarially Generated Examples

Large Language Models (LLMs) have achieved human-level fluency in text generation, making it difficult to distinguish between human-written and LLM-generated texts. This poses a growing risk of misuse of LLMs and demands the development of detectors to identify LLM-generated texts. However, existing detectors lack robustness against attacks: they degrade detection accuracy by simply paraphrasing LLM-generated texts. Furthermore, a malicious user might attempt to deliberately evade the detectors based on detection results, but this has not been assumed in previous studies. In this paper, we propose OUTFOX, a framework that improves the robustness of LLM-generated-text detectors by allowing both the detector and the attacker to consider each other's output. In this framework, the attacker uses the detector's prediction labels as examples for in-context learning and adversarially generates essays that are harder to detect, while the detector uses the adversarially generated essays as examples for in-context learning to learn to detect essays from a strong attacker. Experiments in the domain of student essays show that the proposed detector improves the detection performance on the attacker-generated texts by up to +41.3 points F1-score. Furthermore, the proposed detector shows a state-of-the-art detection performance: up to 96.9 points F1-score, beating existing detectors on non-attacked texts. Finally, the proposed attacker drastically degrades the performance of detectors by up to -57.0 points F1-score, massively outperforming the baseline paraphrasing method for evading detection.
About the Blaschke-Santalo diagram of area, perimeter and moment of inertia

We study the Blaschke-Santal\'o diagram associated to the area, the perimeter, and the moment of inertia. We work in dimension 2, under two assumptions on the shapes: convexity and the presence of two orthogonal axis of symmetry. We discuss topological and geometrical properties of the diagram. As a by-product we address a conjecture by P\'olya, in the simplified setting of double symmetry.
Stabilization of switched DAEs via fast switching

Switched differential algebraic equations (switched DAEs) can model dynamical systems with state constraints together with sudden structural changes (switches). These switches may lead to induced jumps and can destabilize the system even in the case that each mode is stable. However, the opposite effect is also possible; in particular, the question of finding a stabilizing switching signal is of interest. Two approaches are presented how to stabilize a switched DAE via fast switching. (© 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Assessing Tuning Parameter Selection Variability in Penalized Regression

ABSTRACT Penalized regression methods that perform simultaneous model selection and estimation are ubiquitous in statistical modeling. The use of such methods is often unavoidable as manual inspection of all possible models quickly becomes intractable when there are more than a handful of predictors. However, automated methods usually fail to incorporate domain-knowledge, exploratory analyses, or other factors that might guide a more interactive model-building approach. A hybrid approach is to use penalized regression to identify a set of candidate models and then to use interactive model-building to examine this candidate set more closely. To identify a set of candidate models, we derive point and interval estimators of the probability that each model along a solution path will minimize a given model selection criterion, for example, Akaike information criterion, Bayesian information criterion (AIC, BIC), etc., conditional on the observed solution path. Then models with a high probability of selection are considered for further examination. Thus, the proposed methodology attempts to strike a balance between algorithmic modeling approaches that are computationally efficient but fail to incorporate expert knowledge, and interactive modeling approaches that are labor intensive but informed by experience, intuition, and domain knowledge. Supplementary materials for this article are available online.
EndoSurf: Neural Surface Reconstruction of Deformable Tissues with Stereo Endoscope Videos

Reconstructing soft tissues from stereo endoscope videos is an essential prerequisite for many medical applications. Previous methods struggle to produce high-quality geometry and appearance due to their inadequate representations of 3D scenes. To address this issue, we propose a novel neural-field-based method, called EndoSurf, which effectively learns to represent a deforming surface from an RGBD sequence. In EndoSurf, we model surface dynamics, shape, and texture with three neural fields. First, 3D points are transformed from the observed space to the canonical space using the deformation field. The signed distance function (SDF) field and radiance field then predict their SDFs and colors, respectively, with which RGBD images can be synthesized via differentiable volume rendering. We constrain the learned shape by tailoring multiple regularization strategies and disentangling geometry and appearance. Experiments on public endoscope datasets demonstrate that EndoSurf significantly outperforms existing solutions, particularly in reconstructing high-fidelity shapes. Code is available at https://github.com/Ruyi-Zha/endosurf.git.
The connection between the spread of misinformation, time of day, and individual user activity patterns

Social media manipulation poses a significant threat to cognitive autonomy and unbiased opinion formation. Prior literature explored the relationship between online activity and emotional state, cognitive resources, sunlight and weather. However, a limited understanding exists regarding the role of time of day in content spread and the impact of user activity patterns on susceptibility to mis- and disinformation. This work uncovers a strong correlation between user activity patterns and the tendency to spread manipulated content. Through quantitative analysis of Twitter data, we examine how user activity throughout the day aligns with chronotypical archetypes. Evening types exhibit a significantly higher inclination towards spreading potentially manipulated content, which is generally more likely between 2:30 AM and 4:15 AM. This knowledge can become crucial for developing targeted interventions and strategies that mitigate misinformation spread by addressing vulnerable periods and user groups more susceptible to manipulation.
Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs

Knowledge graphs (KGs) have become the standard technology for the representation of factual information in applications such as recommendation engines, search, and question-answering systems. However, the continual updating of KGs, as well as the integration of KGs from different domains and KGs in different languages, remains to be a major challenge. What we suggest here is that by a reification of abstract objects and by acknowledging the ontological distinction between concepts and types, we arrive at an ontologically grounded and language-agnostic representation that can alleviate the difficulties in KG integration.
Robust ground-state energy estimation under depolarizing noise

We present a novel ground-state energy estimation algorithm that is robust under global depolarizing error channels. Building upon the recently developed Quantum Exponential Least Squares (QCELS) algorithm, our new approach incorporates significant advancements to ensure robust estimation while maintaining a polynomial cost in precision. By leveraging the spectral gap of the Hamiltonian effectively, our algorithm overcomes limitations observed in previous methods like quantum phase estimation (QPE) and robust phase estimation (RPE). Going beyond global depolarizing error channels, our work underscores the significance and practical advantages of utilizing randomized compiling techniques to tailor quantum noise towards depolarizing error channels. Our research demonstrates the feasibility of ground-state energy estimation in the presence of depolarizing noise, offering potential advancements in error correction and algorithmic-level error mitigation for quantum algorithms.
Scalable Multi-agent Skill Discovery based on Kronecker Graphs

Covering skill (a.k.a., option) discovery has been developed to improve the exploration of RL in single-agent scenarios with sparse reward signals, through connecting the most distant states in the embedding space provided by the Fiedler vector of the state transition graph. Given that joint state space grows exponentially with the number of agents in multi-agent systems, existing researches still relying on single-agent option discovery either become prohibitive or fail to directly discover joint options that improve the connectivity of the joint state space. In this paper, we show how to directly compute multi-agent options with collaborative exploratory behaviors while still enjoying the ease of decomposition. Our key idea is to approximate the joint state space as a Kronecker graph, based on which we can directly estimate its Fiedler vector using the Laplacian spectrum of individual agents’ transition graphs. Further, considering that directly computing the Laplacian spectrum is intractable for tasks with inﬁnite-scale state spaces, we further propose a deep learning extension of our method by estimating eigenfunctions through NN-based representation learning techniques. The evaluation on multi-agent tasks built with simulators like Mujoco, shows that the proposed algorithm can successfully identify multi-agent options, and signiﬁcantly outperforms the state-of-the-art. Codes are available at: https://github.itap. purdue.edu/Clan-labs/Scalable_MAOD_via_KP.
Photon mediated energy, linear and angular momentum transport in fullerene and graphene systems beyond local equilibrium

Based on a tight-binding model for the electron system, we investigate the transfer of energy, momentum, and angular momentum mediated by electromagnetic fields among buckminsterfullerene (C$_{60}$) and graphene nano-strips. Our nonequilibrium Green's function approach enables calculations away from local thermal equilibrium where the fluctuation-dissipation theorem breaks down. For example, the forces between C$_{60}$ and current-carrying nano-strips are predicted. It is found that the presence of current usually enhances the van der Waals attractive forces. For two current-carrying graphene strips rotated at some angle, the fluctuational force and torque are much stronger at the nanoscale compared to that of the static Biot-Savart law.
Homogenisation of nonlinear Dirichlet problems in randomly perforated domains under minimal assumptions on the size of perforations

In this paper we study the convergence of integral functionals with $q$-growth in a randomly perforated domain of $\mathbb R^n$, with $1<q<n$. Under the assumption that the perforations are small balls whose centres and radii are generated by a \emph{stationary short-range marked point process}, we obtain in the critical-scaling limit an averaged analogue of the nonlinear capacitary term obtained by Ansini and Braides in the deterministic periodic case \cite{Ansini-Braides}. In analogy to the random setting introduced by Giunti, H\"ofer, and Vel\'azquez \cite{Giunti-Hofer-Velasquez} to study the Poisson equation, we only require that the random radii have finite $(n-q)$-moment. This assumption on the one hand ensures that the expectation of the nonlinear $q$-capacity of the spherical holes is finite, and hence that the limit problem is well defined. On the other hand, it does not exclude the presence of balls with large radii, that can cluster up. We show however that the critical rescaling of the perforations is sufficient to ensure that no percolating-like structures appear in the limit.
Bar-Natan Homology for null homologous links in \mathbb{RP}^3

In this paper, we introduce Bar-Natan homology for null homologous links in \mathbb{RP}^3 over the field of two elements. It is a deformation of the Khovanov homology in \mathbb{RP}^3 defined by Asaeda, Przytycki and Sikora. We also define an s-invariant from this deformation using the same recipe as for links in S^3, and prove some genus bound using it. The key ingredient is the notion of twisted orientation for null homologous links and cobordisms in \mathbb{RP}^3.
The fructose survival hypothesis for obesity

The fructose survival hypothesis proposes that obesity and metabolic disorders may have developed from over-stimulation of an evolutionary-based biologic response (survival switch) that aims to protect animals in advance of crisis. The response is characterized by hunger, thirst, foraging, weight gain, fat accumulation, insulin resistance, systemic inflammation and increased blood pressure. The process is initiated by the ingestion of fructose or by stimulating endogenous fructose production via the polyol pathway. Unlike other nutrients, fructose reduces the active energy (adenosine triphosphate) in the cell, while blocking its regeneration from fat stores. This is mediated by intracellular uric acid, mitochondrial oxidative stress, the inhibition of AMP kinase and stimulation of vasopressin. Mitochondrial oxidative phosphorylation is suppressed, and glycolysis stimulated. While this response is aimed to be modest and short-lived, the response in humans is exaggerated due to gain of ‘thrifty genes’ coupled with a western diet rich in foods that contain or generate fructose. We propose excessive fructose metabolism not only explains obesity but the epidemics of diabetes, hypertension, non-alcoholic fatty liver disease, obesity-associated cancers, vascular and Alzheimer's dementia, and even ageing. Moreover, the hypothesis unites current hypotheses on obesity. Reducing activation and/or blocking this pathway and stimulating mitochondrial regeneration may benefit health-span. This article is part of a discussion meeting issue ‘Causes of obesity: theories, conjectures and evidence (Part I)’.
Natural selection and human adiposity: crafty genotype, thrifty phenotype

Evolutionary perspectives on obesity have aimed to understand how the genetic constitution of individuals has been shaped by selective pressures such as famine, predation or infectious disease. The dual intervention model assumes strong selection on lower and upper limits of adiposity, but negligible fitness implications for intermediate adiposity. These frameworks are agnostic to age, sex and condition. I argue that selection has favoured a ‘crafty genotype’—a genetic basis for accommodating variability in the ‘fitness value’ of fat through phenotypic plasticity, depending on the endogenous and exogenous characteristics of each individual. Hominin evolution occurred in volatile environments. I argue that the polygenetic basis of adiposity stabilizes phenotype in such environments, while also coordinating phenotypic variance across traits. This stability underpins reaction norms through which adiposity can respond sensitively to ecological factors. I consider how the fitness value of fat changes with age, sex and developmental experience. Fat is also differentially distributed between peripheral and abdominal depots, reflecting variable prioritization of survival versus reproduction. Where longevity has been compromised by undernutrition, abdominal fat may promote immediate survival and fitness, while long-term cardiometabolic risks may never materialize. This approach helps understand the sensitivity of adiposity to diverse environmental factors, and why the health impacts of obesity are variable. This article is part of a discussion meeting issue ‘Causes of obesity: theories, conjectures and evidence (Part I)’.
Data on Inelastic Processes in Low-energy Calcium–Hydrogen Ionic Collisions

The nonadiabatic nuclear dynamics for the 17 low-lying molecular states of the collisional system is studied by the probabilistic version of the hopping probability current method based on the accurate ab initio adiabatic potentials. Inelastic Ca+ + H, Ca + H+, and Ca2+ + H− collisions are treated, and partial cross sections and rate coefficients for all transitions between the considered scattering channels are calculated for excitation, de-excitation, charge exchange, ion-pair formation, and neutralization processes. The cross sections and the rate coefficients for the 272 partial inelastic processes are computed. It is found that the reaction mechanism for the partial processes with high-valued rates is due to the long-range ionic–covalent interaction, while for some processes with moderate-valued rates it is due to short-range nonadiabatic regions. It is shown that the largest rate coefficients correspond to the neutralization and also charge exchange processes from the optimal window. The largest rate coefficient exceeds the value . It is also found that some two-electron-transition charge exchange processes have rate coefficients as large as one-electron-transition processes. The processes with large and moderate values of rate coefficients are likely to be important for stellar spectra modeling.
Determination of cortisone and cortisol in human scalp hair using an improved LC-MS/MS-based method

Abstract Objectives Human scalp hair is an easily available but complex matrix for determination of cortisone and cortisol, and has been shown to reflect long-term glucocorticoid exposure. Hair glucocorticoid analysis has been used to detect hypo- and hypercortisolism. In this study, we describe the development and validation of a LC-MS/MS method for quantification of cortisone and cortisol in human scalp hair, and provide a novel approach for analysis and interpretation of the results. Methods Improved sample preparation using pulverization and solid phase extraction allowed for low sample volumes (10 mg). Baseline chromatographic separation without matrix interference was achieved by reversed phase chromatography and MRM measurement in negative ion mode. Run-to-run time was 8 min. Mixed model analyses were performed to create individual patterns of cortisone and cortisol concentrations. Results Matrix matched calibration curves showed excellent linearity up to 100 pg (analyte)/mg (hair) for both cortisone and cortisol (R2>0.995). LLOQ was 1.5 and 1.0 pg/mg for cortisone and cortisol, respectively. Matrix effect was negligible for hair color (recoveries 95–105 %). Cortisone and cortisol concentrations decreased from proximal to distal hair segments, following a predictable, but subject-specific pattern, with less individual variation for cortisone than for cortisol. Conclusions This improved LC-MS/MS method is able to accurately quantify cortisone and cortisol in human hair with minimum matrix interference. This new way of data analysis and interpretation including individual patterns of cortisone and cortisol will be of help with detection of pathological concentrations in both the high – and the low ranges of glucocorticoids.
Iterative Methods for Large Scale Convex Optimization

This thesis presents a detailed description and analysis of Bregman’s iterative method for convex programming with linear constraints. Row and block action methods for large scale problems are adopted for convex feasibility problems. This motivates Bregman type methods for optimization. A new simultaneous version of the Bregman’s method for the optimization of Bregman function subject to linear constraints is presented and an extension of the method and its application to solving convex optimization problems is also made. Closed-form formulae are known for Bregman’s method for the particular cases of entropy maximization like Shannon and Burg’s entropies. The algorithms such as the Multiplicative Algebraic Reconstruction Technique (MART) and the related methods use closed-form formulae in their iterations. We present a generalization of these closed-form formulae of Bregman’s method when the objective function variables are separated and analyze its convergence. We also analyze the algorithm MART when the problem is inconsistent and give some convergence results. ii University of Ghana http://ugspace.ug. edu.gh