Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospects for Heavy Neutral Lepton Searches at Short and Medium Baseline Reactor Experiments

Heavy neutrinos with masses in the MeV range can in principle simultaneously explain the light neutrino masses and the origin of baryonic matter in the universe. The strongest constraints on their properties come from their potential impact on the formation of light elements in the early universe. Since these constraints rely on assumptions about the cosmic history, independent checks in the laboratory are highly desirable. In this paper, we discuss the opportunity to search for heavy neutrinos within the MeV mass range in short and medium baseline reactor neutrino experiments, using the SoLid, JUNO and TAO experiments as examples. This kind of experiments can give the currently strongest upper bound on the mixing between the light electron neutrinos and the heavy neutrino in the 2-9 MeV mass range.
Upper bound of heat flux in an anelastic model for Rayleigh-B\'enard convection

Bounds on heat transfer have been the subject of previous studies concerning convection in the Boussinesq approximation: in the Rayleigh-B\'enard configuration, the first result obtained by \cite{howard63} states that $Nu<(3/64 \ Ra)^{1/2}$ for large values of the Rayleigh number $Ra$, independently of the Prandtl number $Pr$. This is still the best known upper bound, only with the prefactor improved to $Nu<1/6 \ Ra^{1/2}$ by \cite{DoeringConstantin96}. In the present paper, this result is extended to compressible convection. An upper bound is obtained for the anelastic liquid approximation, which is similar to the anelastic model used in astrophysics based on a turbulent diffusivity for entropy. The anelastic bound is still scaling as $Ra^{1/2}$, independently of $Pr$, but depends on the dissipation number $\mathcal{D}$ and on the equation of state. For monatomic gases and large Rayleigh numbers, the bound is $Nu<146\, Ra^{\frac{1}{2}} / (2-\mathcal{D} )^{\frac{5}{2}}$.
Anisotropic Hall effects in Bi$_2$Se$_3$/EuS interfaces

Proximity coupling of ferromagnetic insulator EuS to the topological insulator Bi$_2$Se$_3$ has been proposed to break time-reversal symmetry near the surface of Bi$_2$Se$_3$, introducing an energy gap or a tilt in the surface Dirac cone. As an inverse proximity effect, strong spin-orbit coupling available in the topological surface states can enhance the Curie temperature of ferromagnetism in EuS largely beyond its bulk value, and also generate a magnetic anisotropy. This can result in a canting of the magnetic moment of Eu ions in a plane perpendicular to the interface. Here, we investigate theoretically electronic transport properties arising from the Bi$_2$Se$_3$/EuS interfaces in the planar Hall geometry. Our analysis, based on a realistic model Hamiltonian and a semi-classical formalism for the Boltzmann transport equation, reveals distinct intriguing features of anisotropic planar Hall conductivity, depending on different scenarios for the canting of the Eu moments: fixed Eu moment canting, and freely-orientable Eu moment in response to the external in-plane magnetic field. The anisotropy in the planar Hall conductivity arises from the asymmetric Berry curvature of the gapped topological surface states. We also explore topological Hall effect of the Dirac surface states, coupled to a skyrmion crystal which can emerge in the EuS due to the interplay of ferromagnetic Heisenberg exchange, interfacial Dzyaloshinskii-Moriya interaction, and perpendicular alignment of the Eu moment. Our study provides new impetus for probing complex interplay between magnetic exchange interactions and topological surface states via anisotropic planar Hall effects.
Subsampling for Big Data Linear Models with Measurement Errors

Subsampling algorithms for various parametric regression models with massive data have been extensively investigated in recent years. However, all existing studies on subsampling heavily rely on clean massive data. In practical applications, the observed covariates may suffer from inaccuracies due to measurement errors. To address the challenge of large datasets with measurement errors, this study explores two subsampling algorithms based on the corrected likelihood approach: the optimal subsampling algorithm utilizing inverse probability weighting and the perturbation subsampling algorithm employing random weighting assuming a perfectly known distribution. Theoretical properties for both algorithms are provided. Numerical simulations and two real-world examples demonstrate the effectiveness of these proposed methods compared to other uncorrected algorithms.
Sociocultural sustainability of rural community-based tourism : case study of local participation in fair trade coffee trail, Nicaragua

Rural community-based tourism (turismo rural y comunitario) is smallscale tourism in poor rural areas, where the local people are active actors in tourism development. It is often perceived as a sustainable and responsible form of tourism. However, in practice the planning and evaluation of the tourism development is often concentrated only upon rapid economic and environmental impacts and the sociocultural signifi cance is left without proper attention. The issues about sociocultural development have not been considered seriously enough in alternative tourism contexts. This study aims to fi ll this gap by bringing together cultural studies of tourism and studies of development. The starting point of this study is that sustainable tourism development cannot take place without social justice and active local participation. The purpose of this research is to analyze the sociocultural possibilities and challenges of rural community-based tourism development in the host communities. This research has an ethnographic approach and it includes a fi eld study in three communities of Fair Trade Coffee Trail in San Ramón, Northern Nicaragua. The idea for the tourism development was born after the coffee price crisis in 2001, given that the main objectives of tourism were to bring complementary income to the poor coffee farmers and to promote equal participation inside the communities. Data for the sociocultural impact analysis was collected through 23 semi-structured interviews and participatory observation during four week period of fi eldwork in 2008. The results of this study support the assumption that the real essence of rural community-based tourism is its potential to promote people’s control over factors that affect their lives – in other words to support empowerment. In San Ramón tourism development had especially brought new opportunities to the young people working as guides and to the women responsible for the tourism accommodation. Women and young guides had been able to enhance their self-esteem and freedom of choice through new contacts, knowledge, new skills and cultural pride. The encounters with visitors had been mainly positive. Alternatively, the weakness in the local broker’s coordination had threatened the whole tourism development. The sociocultural benefi ts of tourism had not reached the community level and therefore had not promoted the social empowerment of communities. The results of this study can encourage and help development agencies to support community-based tourism initiatives as a tool of sustainable development. Developing a holistic guideline for rural community-based tourism development presents a challenge for further studies.
Cafe-Mpc: A Cascaded-Fidelity Model Predictive Control Framework with Tuning-Free Whole-Body Control

This work introduces an optimization-based locomotion control framework for on-the-fly synthesis of complex dynamic maneuvers. At the core of the proposed framework is a cascaded-fidelity model predictive controller (Cafe-Mpc). Cafe-Mpc strategically relaxes the planning problem along the prediction horizon (i.e., with descending model fidelity, increasingly coarse time steps, and relaxed constraints) for computational and performance gains. This problem is numerically solved with an efficient customized multiple-shooting iLQR (MS-iLQR) solver that is tailored for hybrid systems. The action-value function from Cafe-Mpc is then used as the basis for a new value-function-based whole-body control (VWBC) technique that avoids additional tuning for the WBC. In this respect, the proposed framework unifies whole-body MPC and more conventional whole-body quadratic programming (QP), which have been treated as separate components in previous works. We study the effects of the cascaded relaxations in Cafe-Mpc on the tracking performance and required computation time. We also show that the Cafe-Mpc, if configured appropriately, advances the performance of whole-body MPC without necessarily increasing computational cost. Further, we show the superior performance of the proposed VWBC over the Riccati feedback controller in terms of constraint handling. The proposed framework enables accomplishing for the first time gymnastic-style running barrel rolls on the MIT Mini Cheetah. Video: https://youtu.be/YiNqrgj9mb8.
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy

Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific applications for specialized areas or industries, due to unique terminologies, incomplete contexts of user queries, and specialized search intents. To capture the theme-specific information and improve retrieval, we propose to use a corpus topical taxonomy, which outlines the latent topic structure of the corpus while reflecting user-interested aspects. We introduce ToTER (Topical Taxonomy Enhanced Retrieval) framework, which identifies the central topics of queries and documents with the guidance of the taxonomy, and exploits their topical relatedness to supplement missing contexts. As a plug-and-play framework, ToTER can be flexibly employed to enhance various PLM-based retrievers. Through extensive quantitative, ablative, and exploratory experiments on two real-world datasets, we ascertain the benefits of using topical taxonomy for retrieval in theme-specific applications and demonstrate the effectiveness of ToTER.
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability

The existence of adversarial attacks on machine learning models imperceptible to a human is still quite a mystery from a theoretical perspective. In this work, we introduce two notions of adversarial attacks: natural or on-manifold attacks, which are perceptible by a human/oracle, and unnatural or off-manifold attacks, which are not. We argue that the existence of the off-manifold attacks is a natural consequence of the dimension gap between the intrinsic and ambient dimensions of the data. For 2-layer ReLU networks, we prove that even though the dimension gap does not affect generalization performance on samples drawn from the observed data space, it makes the clean-trained model more vulnerable to adversarial perturbations in the off-manifold direction of the data space. Our main results provide an explicit relationship between the $\ell_2,\ell_{\infty}$ attack strength of the on/off-manifold attack and the dimension gap.
SAM-PD: How Far Can SAM Take Us in Tracking and Segmenting Anything in Videos by Prompt Denoising

Recently, promptable segmentation models, such as the Segment Anything Model (SAM), have demonstrated robust zero-shot generalization capabilities on static images. These promptable models exhibit denoising abilities for imprecise prompt inputs, such as imprecise bounding boxes. In this paper, we explore the potential of applying SAM to track and segment objects in videos where we recognize the tracking task as a prompt denoising task. Specifically, we iteratively propagate the bounding box of each object's mask in the preceding frame as the prompt for the next frame. Furthermore, to enhance SAM's denoising capability against position and size variations, we propose a multi-prompt strategy where we provide multiple jittered and scaled box prompts for each object and preserve the mask prediction with the highest semantic similarity to the template mask. We also introduce a point-based refinement stage to handle occlusions and reduce cumulative errors. Without involving tracking modules, our approach demonstrates comparable performance in video object/instance segmentation tasks on three datasets: DAVIS2017, YouTubeVOS2018, and UVO, serving as a concise baseline and endowing SAM-based downstream applications with tracking capabilities.
Wondertopes

Positive geometries were introduced by Arkani-Hamed--Bai--Lam as a method of computing scattering amplitudes in theoretical physics. We show that a positive geometry from a polytope admits a log resolution of singularities to another positive geometry. Our result states that the regions in a wonderful compactification of a hyperplane arrangement complement, which we call wondertopes, are positive geometries. A familiar wondertope is the curvy associahedron, which tiles the moduli space of pointed stable rational curves. Thus our work generalizes the known positive geometry structure on this moduli space.
Teaching Large Language Models to Reason with Reinforcement Learning

Reinforcement Learning from Human Feedback (\textbf{RLHF}) has emerged as a dominant approach for aligning LLM outputs with human preferences. Inspired by the success of RLHF, we study the performance of multiple algorithms that learn from feedback (Expert Iteration, Proximal Policy Optimization (\textbf{PPO}), Return-Conditioned RL) on improving LLM reasoning capabilities. We investigate both sparse and dense rewards provided to the LLM both heuristically and via a learned reward model. We additionally start from multiple model sizes and initializations both with and without supervised fine-tuning (\textbf{SFT}) data. Overall, we find all algorithms perform comparably, with Expert Iteration performing best in most cases. Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of $10^6$ samples to converge from a pretrained checkpoint. We investigate why this is the case, concluding that during RL training models fail to explore significantly beyond solutions already produced by SFT models. Additionally, we discuss a trade off between maj@1 and pass@96 metric performance during SFT training and how conversely RL training improves both simultaneously. We then conclude by discussing the implications of our findings for RLHF and the future role of RL in LLM fine-tuning.
Unsupervised Learning of Harmonic Analysis Based on Neural HSMM with Code Quality Templates

This paper presents a method of unsupervised learning of harmonic analysis based on a hidden semi-Markov model (HSMM). We introduce the chord quality templates, which specify the probability of pitch class emissions given a root note and a chord quality. Other probability distributions that comprise the HSMM are automatically learned via unsupervised learning, which has been a challenge in existing research. The results of the harmonic analysis of the proposed model were evaluated using existing labeled data. While our proposed method has yet to perform as well as existing models that used supervised learning and complex rule design, it has the advantage of not requiring expensive labeled data or rule elaboration. Furthermore, we also show how to recognize the tonic without prior knowledge, based on the transition probabilities of the Markov model.
Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition

We study reinforcement learning with linear function approximation, unknown transition, and adversarial losses in the bandit feedback setting. Specifically, we focus on linear mixture MDPs whose transition kernel is a linear mixture model. We propose a new algorithm that attains an $\widetilde{O}(d\sqrt{HS^3K} + \sqrt{HSAK})$ regret with high probability, where $d$ is the dimension of feature mappings, $S$ is the size of state space, $A$ is the size of action space, $H$ is the episode length and $K$ is the number of episodes. Our result strictly improves the previous best-known $\widetilde{O}(dS^2 \sqrt{K} + \sqrt{HSAK})$ result in Zhao et al. (2023a) since $H \leq S$ holds by the layered MDP structure. Our advancements are primarily attributed to (i) a new least square estimator for the transition parameter that leverages the visit information of all states, as opposed to only one state in prior work, and (ii) a new self-normalized concentration tailored specifically to handle non-independent noises, originally proposed in the dynamic assortment area and firstly applied in reinforcement learning to handle correlations between different states.
The West Philippine Basin and the initiation of subduction, revisited

New swath bathymetry plus existing geophysical data reveal that the direction of West Philippine Basin (WPB) seafloor spreading rotated 100° counter‐clockwise between 49 Ma and 33 Ma. The curvilinear and multi‐stranded Mindanao Fracture Zone separates the WPB from the Palau Basin to the south. WPB opening was contemporaneous with, and behind, early Izu‐Bonin‐Mariana (IBM) subduction, whose arc volcanism began by 50 Ma. This produced over 1000 km of arc parallel spreading (or stretching plus magmatism) in the Mariana segment of the Eocene IBM arc/forearc. The initial IBM subduction cut across, rather than followed, pre‐existing structures (remnant arcs, fracture zones and spreading fabric). New models of subduction initiation are required to reproduce such characteristics.
HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning

Federated Learning (FL) is a practical approach to train deep learning models collaboratively across user-end devices, protecting user privacy by retaining raw data on-device. In FL, participating user-end devices are highly fragmented in terms of hardware and software configurations. Such fragmentation introduces a new type of data heterogeneity in FL, namely \textit{system-induced data heterogeneity}, as each device generates distinct data depending on its hardware and software configurations. In this paper, we first characterize the impact of system-induced data heterogeneity on FL model performance. We collect a dataset using heterogeneous devices with variations across vendors and performance tiers. By using this dataset, we demonstrate that \textit{system-induced data heterogeneity} negatively impacts accuracy, and deteriorates fairness and domain generalization problems in FL. To address these challenges, we propose HeteroSwitch, which adaptively adopts generalization techniques (i.e., ISP transformation and SWAD) depending on the level of bias caused by varying HW and SW configurations. In our evaluation with a realistic FL dataset (FLAIR), HeteroSwitch reduces the variance of averaged precision by 6.3\% across device types.
Topology Change from Pointlike Sources

In this paper we study topology-changing spacetimes occurring from pointlike sources. Following an old idea of Penrose, we will opt for a non-Hausdorff model of topology change in which an initial pointlike source is"doubled"and allowed to propagate along null rays into an eventual cobordism. By appealing to recent developments in non-Hausdorff differential geometry, we will describe and evaluate gravitational actions on these pointlike topology-changing spacetimes. Motivated by analogous results for the Trousers space, we describe a sign convention for Lorentzian angles that will ensure the dampening of our non-Hausdorff topology-changing spacetimes within a two-dimensional path integral for gravity.
A Simple and Near-Optimal Algorithm for Directed Expander Decompositions

In this work, we present the first algorithm to compute expander decompositions in an $m$-edge directed graph with near-optimal time $\tilde{O}(m)$. Further, our algorithm can maintain such a decomposition in a dynamic graph and again obtains near-optimal update times. Our result improves over previous algorithms of Bernstein-Probst Gutenberg-Saranurak (FOCS 2020), Hua-Kyng-Probst Gutenberg-Wu (SODA 2023) that only obtained algorithms optimal up to subpolynomial factors. At the same time, our algorithm is much simpler and more accessible than previous work. In order to obtain our new algorithm, we present a new push-pull-relabel flow framework that generalizes the classic push-relabel flow algorithm of Goldberg-Tarjan (JACM 1988), which was later dynamized for computing expander decompositions in undirected graphs by Henzinger-Rao-Wang (SIAM J. Comput. 2020), Saranurak-Wang (SODA 2019). We then show that the flow problems formulated in recent work of Hua-Kyng-Probst Gutenberg-Wu (SODA 2023) to decompose directed graphs can be solved much more efficiently in the push-pull-relabel flow framework.
Emergent impervious band crossing in the bulk in topological nodal line semimetal ZrAs$_2$

Topological nodal line semimetals (TSMs) represent a unique class of materials with intriguing electronic structures and rich of symmetries, hosting electronic states with non-trivial topological properties. Among these, ZrAs$_2$ stands out, characterized by its nodal lines forming continuous loops in momentum space, governed by non-symmorphic symmetries. This study integrates angle-resolved photoemission spectroscopy (ARPES) with density functional theory (DFT) calculations to explore the electronic states of ZrAs$_2$. In ARPES scans, we observed a distinctive nodal loop structure observed at lower excitation energies of 30 and 50 eV. Our results, supported by calculations based on DFT, unveil symmetry-enforced Dirac-like band crossings anchored at specific points in the Brillouin zone, with particular emphasis on the S point. Surface bands and bulk states near the crossing are elucidated through slab calculations, corroborating experimental findings. DFT calculations also show the existence of several spin-orbit coupling (SOC) resilient semi-Dirac crossings pinned at Z point. This comprehensive investigation sheds light on the intricate electronic behaviors of ZrAs$_2$ with the involved symmetries, important for fundamental understanding of topological nodal line semimetals.
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift

Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based device classification when testing and training data is collected in different domains. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem. Contrastive learning, a state-of-the-art self-supervised learning approach from deep learning, learns a distance metric such that positive pairs are closer (i.e. more similar) in the learned metric space than negative pairs. When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs. Through experiments on wireless and wired RF datasets collected over several days, we demonstrate that our contrastive learning approach captures domain-invariant features, diminishing the effects of domain-specific variations. Our results show large and consistent improvements in accuracy (10.8\% to 27.8\%) over baseline models, thus underscoring the effectiveness of contrastive learning in improving device classification under domain shift.