Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification under Nuisance Parameters and Generalized Label Shift in Likelihood-Free Inference

An open scientific challenge is how to classify events with reliable measures of uncertainty, when we have a mechanistic model of the data-generating process but the distribution over both labels and latent nuisance parameters is different between train and target data. We refer to this type of distributional shift as generalized label shift (GLS). Direct classification using observed data $\mathbf{X}$ as covariates leads to biased predictions and invalid uncertainty estimates of labels $Y$. We overcome these biases by proposing a new method for robust uncertainty quantification that casts classification as a hypothesis testing problem under nuisance parameters. The key idea is to estimate the classifier's receiver operating characteristic (ROC) across the entire nuisance parameter space, which allows us to devise cutoffs that are invariant under GLS. Our method effectively endows a pre-trained classifier with domain adaptation capabilities and returns valid prediction sets while maintaining high power. We demonstrate its performance on two challenging scientific problems in biology and astroparticle physics with data from realistic mechanistic models.
Assessing the atomic moment picture of spin dynamics: the perspective of \textit{ab initio} magnon wavefunction

Our understanding of collective spin fluctuation in materials relies largely on Heisenberg-type spin Hamiltonians. Implicit in these spin models is the atomic moment picture that in transverse spin dynamics the magnetization around an atom undergoes precessional motion as a rigid moment, which has been challenged by emerging theoretical and experimental advances. To assess the validity of the atomic moment picture in spin dynamics, however, necessitates magnon wavefunctions from \textit{ab initio} methods without \textit{a priori} spin models. To this end, we develop an efficient model-free {\it ab initio} method for computing magnon spectrum and wavefunctions. Niu-Kleinman's adiabatic spin-wave dynamics is reformulated using linear perturbation theory into a generalized eigenvalue problem, which can be solved to produce magnon spectrum and wavefunctions without assuming atomic moments. We have implemented this method in the framework of density functional perturbation theory (DFPT). A dynamical extension of Niu-Kleinman equation of motion is proposed to improve inaccurate predicted magnon energies due to imperfect adiabaticity at higher energies. Based on so-obtained {\it ab initio} magnon wavefunctions, we find the atomic moment picture to be valid in typical ferromagnets and antiferromagnets, but fails in the molecular orbital crystal Na$_2$IrO$_3$. Our results suggest that the usual spin Hamiltonian approach should be taken with a grain of salt, and possible experimental ramification on the issue is discussed.
Near-Optimal Convex Simple Bilevel Optimization with a Bisection Method

This paper studies a class of simple bilevel optimization problems where we minimize a composite convex function at the upper-level subject to a composite convex lower-level problem. Existing methods either provide asymptotic guarantees for the upper-level objective or attain slow sublinear convergence rates. We propose a bisection algorithm to find a solution that is $\epsilon_f$-optimal for the upper-level objective and $\epsilon_g$-optimal for the lower-level objective. In each iteration, the binary search narrows the interval by assessing inequality system feasibility. Under mild conditions, the total operation complexity of our method is ${\tilde {\mathcal{O}}}\left(\max\{\sqrt{L_{f_1}/\epsilon_f},\sqrt{L_{g_1}/\epsilon_g} \} \right)$. Here, a unit operation can be a function evaluation, gradient evaluation, or the invocation of the proximal mapping, $L_{f_1}$ and $L_{g_1}$ are the Lipschitz constants of the upper- and lower-level objectives' smooth components, and ${\tilde {\mathcal{O}}}$ hides logarithmic terms. Our approach achieves a near-optimal rate, matching the optimal rate in unconstrained smooth or composite convex optimization when disregarding logarithmic terms. Numerical experiments demonstrate the effectiveness of our method.
(Almost) Affine Higher-Order Tree Transducers

We investigate the tree-to-tree functions computed by \enquote{affine$\lambda$-transducers}: tree automata whose memory consists of an affine $\lambda$-term instead of a finite state. They can be seen as variations on Gallot, Lemay and Salvati's Linear High-Order Deterministic Tree Transducers. When the memory is almost purely affine (\textit{\`a la} Kanazawa), we show that these machines can be translated to tree-walking transducers (and with a purely affine memory, we get a reversible tree-walking transducer). This leads to a proof of an inexpressivity conjecture of \titocecilia on \enquote{implicit automata} in an affine $\lambda$-calculus. The key technical tool in our proofs is the Interaction Abstract Machine (IAM), an operational avatar of the \enquote{geometry of interaction} semantics of linear logic. We work with ad-hoc specializations to (almost) affine $\lambda$-terms of a tree-generating version of the IAM.
Predicting concentration changes via discrete sampling

To successfully navigate chemical gradients, microorganisms need to predict how the ligand concentration changes in space. Due to their limited size, they do not take a spatial derivative over their body length but rather a temporal derivative, comparing the current signal with that in the recent past, over the so-called adaptation time. This strategy is pervasive in biology, but it remains unclear what determines the accuracy of such measurements. Using a generalized version of the previously established sampling framework, we investigate how resource limitations and the statistics of the input signal set the optimal design of a well-characterized network that measures temporal concentration changes: the Escherichia coli chemotaxis network. Our results show how an optimal adaptation time arises from the trade-off between the sampling error, caused by the stochastic nature of the network, and the dynamical error, caused by uninformative fluctuations in the input. A larger resource availability reduces the sampling error, which allows for a smaller adaptation time, thereby simultaneously decreasing the dynamical error. Similarly, we find that the optimal adaptation time scales inversely with the gradient steepness, because steeper gradients lift the signal above the noise and reduce the sampling error. These findings shed light on the principles that govern the optimal design of the E. coli chemotaxis network specifically, and any system measuring temporal changes more broadly.
Extending 6D Object Pose Estimators for Stereo Vision

Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task. However, recent methods for directly regressing poses from RGB images using dense features have achieved state-of-the-art results. Stereo vision, which provides an additional perspective on the object, can help reduce pose ambiguity and occlusion. Moreover, stereo can directly infer the distance of an object, while mono-vision requires internalized knowledge of the object's size. To extend the state-of-the-art in 6D object pose estimation to stereo, we created a BOP compatible stereo version of the YCB-V dataset. Our method outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo vision and can easily be adopted for other dense feature-based algorithms.
Cyclic sieving on noncrossing (1,2)-configurations

Verifying a suspicion of Propp and Reiner concerning the cyclic sieving phenomenon (CSP), M. Thiel introduced a Catalan object called noncrossing $(1,2)$-configurations (denoted by $X_n$), which is a class of set partitions of $[n-1]$. More precisely, Thiel proved that, with a natural action of the cyclic group $C_{n-1}$ on $X_n$, the triple $\left(X_n,C_{n-1},\text{Cat}_n(q)\right)$ exhibits the CSP, where $\text{Cat}_n(q):=\frac{1}{[n+1]_q}\begin{bmatrix} 2n\\ n \end{bmatrix}_q$ is MacMahon's $q$-Catalan number. Recently, in a study of the fermionic diagonal coinvariant ring $FDR_n$, J. Kim found a combinatorial basis for $FDR_n$ indexed by $X_n$. In this paper, we continue to study $X_n$ and obtain the following results: (1) We define a statistic $cwt$ on $X_n$ whose generating function is $\text{Cat}_n(q)$, which answers a problem of Thiel. (2) We show that $\text{Cat}_n(q)$ is equivalent to $$\sum_{\substack{k,x,y\\2k+x+y=n-1}}\begin{bmatrix} n-1 2k,x,y \end{bmatrix}_q\text{Cat}_k (q)q^{k+\binom{x}{2}+\binom{y}{2}+\binom{n}{2}}$$ modulo $q^{n-1}-1$, which answers a problem of Kim. As mentioned by Kim, this result leads to a representation theoretic proof of the above cyclic sieving result of Thiel. (3) We consider the dihedral sieving, a generalization of the CSP, which was recently introduced by Rao and Suk. Under a natural action of the dihedral group $I_2(n-1)$ (for even $n$), we prove a dihedral sieving result on $X_n$.
Patterns of Distribution and Endemism in Bahamian Tenebrionidae (Coleoptera)

Les coleopteres Tenebrionidae constituent une partie significative de la faune d'insectes de l'archipel des Bahamas. Cependant, malgre des siecles d'occupation europeenne, ils restent mal connus. On trouve une variete d'habitats propices aux Tenebrionidae sur les iles Bahamas. Des travaux recents sur quelques iles selectionnees et l'inventaire de leurs especes de Tenebrionidae, indiquent une richesse etonnante avec un melange d'especes antillaises repandues, des elements adventifs et des endemiques supposes. La plupart de ces derniers appartiennent aux genres Branchus, Diastolinus et Trientoma, tous incapables de voler, apportant beaucoup de renseignements pour des analyses biogeographiques. Les rivages des iles Bahamas qui furent historiquement isoles par l'inondation complete durant le Pliocene, offrent des opportunites pour des etudes de dispersion, de colonisation et de l'origine des especes insulaires.
Rocks Coding, Not Development-A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks

Recently, large language models (LLM) based generative AI has been gaining momentum for their impressive high-quality performances in multiple domains, particularly after the release of the ChatGPT. Many believe that they have the potential to perform general-purpose problem-solving in software development and replace human software developers. Nevertheless, there are in a lack of serious investigation into the capability of these LLM techniques in fulfilling software development tasks. In a controlled 2 x 2 between-subject experiment with 109 participants, we examined whether and to what degree working with ChatGPT was helpful in the coding task and typical software development task and how people work with ChatGPT. We found that while ChatGPT performed well in solving simple coding problems, its performance in supporting typical software development tasks was not that good. We also observed the interactions between participants and ChatGPT and found the relations between the interactions and the outcomes. Our study thus provides first-hand insights into using ChatGPT to fulfill software engineering tasks with real-world developers and motivates the need for novel interaction mechanisms that help developers effectively work with large language models to achieve desired outcomes.
X-ray Polarization from Magnetar Sources

The launch of the IXPE telescope in late 2021 finally made polarization measurements in the 2–8keV band a reality, more than 40 years after the pioneering observations of the OSO-8 satellite. In the first two years of operations, IXPE targeted more than 60 sources, including four magnetars, neutron stars with magnetic fields in the petaGauss range. In this paper we summarize the IXPE main findings and discuss their implications for the physics of ultra-magnetized neutron stars. Polarimetric observations confirmed theoretical predictions, according to which X-ray radiation from magnetar sources is highly polarized, up to ≈80%, the highest value detected so far. This provides an independent confirmation that magnetars are indeed endowed with a super-strong magnetic field and that the twisted magnetosphere scenario is the most likely explanation for their soft X-ray emission. Polarization measurements allowed us to probe the physical conditions of the star’s outermost layers, showing that the cooler surface regions are in a condensed state, with no atmosphere on top. Although no smoking-gun of vacuum QED effects was found, the phase-dependent behavior of the polarization angle strongly hints that vacuum birefringence is indeed at work in magnetar magnetospheres.
Knowledge Distillation for Road Detection based on cross-model Semi-Supervised Learning

The advancement of knowledge distillation has played a crucial role in enabling the transfer of knowledge from larger teacher models to smaller and more efficient student models, and is particularly beneficial for online and resource-constrained applications. The effectiveness of the student model heavily relies on the quality of the distilled knowledge received from the teacher. Given the accessibility of unlabelled remote sensing data, semi-supervised learning has become a prevalent strategy for enhancing model performance. However, relying solely on semi-supervised learning with smaller models may be insufficient due to their limited capacity for feature extraction. This limitation restricts their ability to exploit training data. To address this issue, we propose an integrated approach that combines knowledge distillation and semi-supervised learning methods. This hybrid approach leverages the robust capabilities of large models to effectively utilise large unlabelled data whilst subsequently providing the small student model with rich and informative features for enhancement. The proposed semi-supervised learning-based knowledge distillation (SSLKD) approach demonstrates a notable improvement in the performance of the student model, in the application of road segmentation, surpassing the effectiveness of traditional semi-supervised learning methods.
Joint End-to-End Image Compression and Denoising: Leveraging Contrastive Learning and Multi-Scale Self-ONNs

Noisy images are a challenge to image compression algorithms due to the inherent difficulty of compressing noise. As noise cannot easily be discerned from image details, such as high-frequency signals, its presence leads to extra bits needed for compression. Since the emerging learned image compression paradigm enables end-to-end optimization of codecs, recent efforts were made to integrate denoising into the compression model, relying on clean image features to guide denoising. However, these methods exhibit suboptimal performance under high noise levels, lacking the capability to generalize across diverse noise types. In this paper, we propose a novel method integrating a multi-scale denoiser comprising of Self Organizing Operational Neural Networks, for joint image compression and denoising. We employ contrastive learning to boost the network ability to differentiate noise from high frequency signal components, by emphasizing the correlation between noisy and clean counterparts. Experimental results demonstrate the effectiveness of the proposed method both in rate-distortion performance, and codec speed, outperforming the current state-of-the-art.
Taming Timeout Flakiness: An Empirical Study of SAP HANA

Regression testing aims to prevent code changes from breaking existing features. Flaky tests negatively affect regression testing because they result in test failures that are not necessarily caused by code changes, thus providing an ambiguous signal. Test timeouts are one contributing factor to such flaky test failures. With the goal of reducing test flakiness in SAP HANA, we empirically study the impact of test timeouts on flakiness in system tests. We evaluate different approaches to automatically adjust timeout values, assessing their suitability for reducing execution time costs and improving build turnaround times. We collect metadata on SAP HANA's test executions by repeatedly executing tests on the same code revision over a period of six months. We analyze the test flakiness rate, investigate the evolution of test timeout values, and evaluate different approaches for optimizing timeout values. The test flakiness rate ranges from 49% to 70%, depending on the number of repeated test executions. Test timeouts account for 70% of flaky test failures. Developers typically react to flaky timeouts by manually increasing timeout values or splitting long-running tests. However, manually adjusting timeout values is a tedious task. Our approach for timeout optimization reduces timeout-related flaky failures by 80% and reduces the overall median timeout value by 25%, i.e., blocked tests are identified faster. Test timeouts are a major contributing factor to flakiness in system tests. It is challenging for developers to effectively mitigate this problem manually. Our technique for optimizing timeout values reduces flaky failures while minimizing test costs. Practitioners working on large-scale industrial software systems can use our findings to increase the effectiveness of their system tests while reducing the burden on developers to manually maintain appropriate timeout values.
FuncGrasp: Learning Object-Centric Neural Grasp Functions from Single Annotated Example Object

We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categorical priors. Unlike previous works that only transfer a set of grasp poses, FuncGrasp aims to transfer infinite configurations parameterized by an object-centric continuous grasp function across varying instances. To ease the transfer process, we propose Neural Surface Grasping Fields (NSGF), an effective neural representation defined on the surface to densely encode grasp configurations. Further, we exploit function-to-function transfer using sphere primitives to establish semantically meaningful categorical correspondences, which are learned in an unsupervised fashion without any expert knowledge. We showcase the effectiveness through extensive experiments in both simulators and the real world. Remarkably, our framework significantly outperforms several strong baseline methods in terms of density and reliability for generated grasps.
Exact capacity of the \emph{wide} hidden layer treelike neural networks with generic activations

Recent progress in studying \emph{treelike committee machines} (TCM) neural networks (NN) in \cite{Stojnictcmspnncaprdt23,Stojnictcmspnncapliftedrdt23,Stojnictcmspnncapdiffactrdt23} showed that the Random Duality Theory (RDT) and its a \emph{partially lifted}(pl RDT) variant are powerful tools that can be used for very precise networks capacity analysis. Here, we consider \emph{wide} hidden layer networks and uncover that certain aspects of numerical difficulties faced in \cite{Stojnictcmspnncapdiffactrdt23} miraculously disappear. In particular, we employ recently developed \emph{fully lifted} (fl) RDT to characterize the \emph{wide} ($d\rightarrow \infty$) TCM nets capacity. We obtain explicit, closed form, capacity characterizations for a very generic class of the hidden layer activations. While the utilized approach significantly lowers the amount of the needed numerical evaluations, the ultimate fl RDT usefulness and success still require a solid portion of the residual numerical work. To get the concrete capacity values, we take four very famous activations examples: \emph{\textbf{ReLU}}, \textbf{\emph{quadratic}}, \textbf{\emph{erf}}, and \textbf{\emph{tanh}}. After successfully conducting all the residual numerical work for all of them, we uncover that the whole lifting mechanism exhibits a remarkably rapid convergence with the relative improvements no better than $\sim 0.1\%$ happening already on the 3-rd level of lifting. As a convenient bonus, we also uncover that the capacity characterizations obtained on the first and second level of lifting precisely match those obtained through the statistical physics replica theory methods in \cite{ZavPeh21} for the generic and in \cite{BalMalZech19} for the ReLU activations.
Explaining the $B^+\to K^+ \nu \bar{\nu}$ excess via a massless dark photon

The Belle II collaboration has recently observed the rare decay $B^+\to K^+ \nu \bar{\nu}$, finding an excess with respect to the Standard Model prediction. We explore the possibility that the data entails long-distance interactions induced by a massless dark photon, $\gamma_{\scriptscriptstyle{D}}$. This couples at the tree-level to an invisible, dark sector and to the Standard Model via higher-dimensional operators, such as the chromomagnetic-dipole coupling that we use to explain the excess. As the process $B^+\to K^+ \gamma_{\scriptscriptstyle{D}}$ is forbidden by angular momentum conservation, the transition mediated by the off-shell dark photon yields a three-body final state comprising a pair of dark fermions that show as a missing energy continuum in the detector, faking the neutrino signature. We show that the Belle II data is explained for perturbative values of the parameters of the model. This scenario predicts new contributions to the neutral $B$ meson decays $B^0\to K^* \gamma_{\scriptscriptstyle{D}}$, in which the emission of a on-shell dark photon is allowed, yielding a monochromatic missing energy signature. Analogously, an excess due to the emission of a dark photon is predicted for the $B^0_s\to \phi + E_{\rm miss}$ decay that could be scrutinized next at the LHCb experiments.
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models

The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing. However, the intensive memory requirements for fine-tuning LLMs pose significant challenges, especially when deploying on clients with limited computational resources. To circumvent this, we explore the novel integration of Memory-efficient Zeroth-Order Optimization within a federated setting, a synergy we term as FedMeZO. Our study is the first to examine the theoretical underpinnings of FedMeZO in the context of LLMs, tackling key questions regarding the influence of large parameter spaces on optimization behavior, the establishment of convergence properties, and the identification of critical parameters for convergence to inform personalized federated strategies. Our extensive empirical evidence supports the theory, showing that FedMeZO not only converges faster than traditional first-order methods such as FedAvg but also significantly reduces GPU memory usage during training to levels comparable to those during inference. Moreover, the proposed personalized FL strategy that is built upon the theoretical insights to customize the client-wise learning rate can effectively accelerate loss reduction. We hope our work can help to bridge theoretical and practical aspects of federated fine-tuning for LLMs, thereby stimulating further advancements and research in this area.