Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting the Speed of Light from Matrix Product States.

We provide evidence that the spectrum of the local effective Hamiltonian and the transfer operator in infinite-system matrix product state simulations are identical up to a global rescaling factor, i.e., the speed of light of the system, when the underlying system is described by a 1+1 dimensional CFT. We provide arguments for this correspondence based on a path integral point of view. This observation turns out to yield very precise estimates for the speed of light in practice, confirming exact results to high precision where available, but also allowing us to finally determine the speed of light of the non-integrable, critical SU(2) Heisenberg chains with half-integer spin S>1/2 with unprecedented accuracy. We also show that the same technology applied to doped Hubbard ladders provides highly accurate velocities for a range of dopings. Combined with measurements of compressibilities we present new results for the Luttinger liquid parameter in the Luther-Emery regime of doped Hubbard ladders, outperforming earlier approaches based on the fitting of real-space correlation functions.
CLIPER: A Unified Vision-Language Framework for In-the-Wild Facial Expression Recognition

Facial expression recognition (FER) is an essential task for understanding human behaviors. As one of the most informative behaviors of humans, facial expressions are often compound and variable, which is manifested by the fact that different people may express the same expression in very different ways. However, most FER methods still use one-hot or soft labels as the supervision, which lack sufficient semantic descriptions of facial expressions and are less interpretable. Recently, contrastive vision-language pre-training (VLP) models (e.g., CLIP) use text as supervision and have injected new vitality into various computer vision tasks, benefiting from the rich semantics in text. Therefore, in this work, we propose CLIPER, a unified framework for both static and dynamic facial Expression Recognition based on CLIP. Besides, we introduce multiple expression text descriptors (METD) to learn fine-grained expression representations that make CLIPER more interpretable. We conduct extensive experiments on several popular FER benchmarks and achieve state-of-the-art performance, which demonstrates the effectiveness of CLIPER.
Ab initio symmetry-adapted emulator for studying emergent collectivity and clustering in nuclei

We discuss emulators from the ab initio symmetry-adapted no-core shell-model framework for studying the formation of alpha clustering and collective properties without effective charges. We present a new type of an emulator, one that utilizes the eigenvector continuation technique but is based on the use of symplectic symmetry considerations. This is achieved by using physically relevant degrees of freedom, namely, the symmetry-adapted basis, which exploits the almost perfect symplectic symmetry in nuclei. Specifically, we study excitation energies, point-proton root-mean-square radii, along with electric quadrupole moments and transitions for 6Li and 12C. We show that the set of parameterizations of the chiral potential used to train the emulators has no significant effect on predictions of dominant nuclear features, such as shape and the associated symplectic symmetry, along with cluster formation, but slightly varies details that affect collective quadrupole moments, asymptotic normalization coefficients, and alpha partial widths up to a factor of two. This makes these types of emulators important for further constraining the nuclear force for high-precision nuclear structure and reaction observables.
Prospective Implications of Extreme-ultraviolet Coronal Plumes for Magnetic-network Genesis of Coronal Heating, Coronal-hole Solar Wind, and Solar-wind Magnetic Field Switchbacks

We propose that coronal heating in EUV coronal plumes is weaker, not stronger, than in adjacent nonplume coronal magnetic funnels. This expectation stems from (i) the observation that an EUV plume is born as the magnetic flux at the foot of the plume’s magnetic funnel becomes tightly packed, and (ii) the observation that coronal heating in quiet regions increases in proportion to the coastline length of the underlying magnetic network. We do not rule out the possibility that coronal heating in EUV plumes might be stronger, not weaker, but we point out how the opposite is plausible. We reason that increasing coronal heating during plume birth would cause co-temporal increasing net upward mass flux in the plume, whereas decreasing coronal heating during plume birth would cause co-temporal net downward mass flux in quiet-region plumes and co-temporal decrease in net upward mass flux or even net downward mass flux in coronal-hole plumes. We further reason that conclusive evidence of weaker coronal heating in EUV plumes would strengthen the possibility that magnetic-twist waves from fine-scale magnetic explosions at the edges of the magnetic network (1) power much of the coronal heating in quiet regions, and (2) power most of the coronal heating and solar-wind acceleration in coronal holes, with many twist waves surviving to become magnetic field switchbacks in the solar wind from coronal holes.
Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models

The image deepfake detection task has been greatly addressed by the scientific community to discriminate real images from those generated by Artificial Intelligence (AI) models: a binary classification task. In this work, the deepfake detection and recognition task was investigated by collecting a dedicated dataset of pristine images and fake ones generated by 9 different Generative Adversarial Network (GAN) architectures and by 4 additional Diffusion Models (DM). A hierarchical multi-level approach was then introduced to solve three different deepfake detection and recognition tasks: (i) Real Vs AI generated; (ii) GANs Vs DMs; (iii) AI specific architecture recognition. Experimental results demonstrated, in each case, more than 97% classification accuracy, outperforming state-of-the-art methods.
Finding the right XAI method - A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science

Explainable artificial intelligence (XAI) methods shed light on the predictions of machine learning algorithms. Several different approaches exist and have already been applied in climate science. However, usually missing ground truth explanations complicate their evaluation and comparison, subsequently impeding the choice of the XAI method. Therefore, in this work, we introduce XAI evaluation in the climate context and discuss different desired explanation properties, namely robustness, faithfulness, randomization, complexity, and localization. To this end, we chose previous work as a case study where the decade of annual-mean temperature maps is predicted. After training both a multi-layer perceptron (MLP) and a convolutional neural network (CNN), multiple XAI methods are applied and their skill scores in reference to a random uniform explanation are calculated for each property. Independent of the network, we find that XAI methods Integrated Gradients, layer-wise relevance propagation, and input times gradients exhibit considerable robustness, faithfulness, and complexity while sacrificing randomization performance. Sensitivity methods – gradient, SmoothGrad, NoiseGrad, and FusionGrad, match the robustness skill but sacrifice faithfulness and complexity for randomization skill. We find architecture-dependent performance differences regarding robustness, complexity and localization skills of different XAI methods, highlighting the necessity for research task-specific evaluation. Overall, our work offers an overview of different evaluation properties in the climate science context and shows how to compare and benchmark different explanation methods, assessing their suitability based on strengths and weaknesses, for the specific research problem at hand. By that, we aim to support climate researchers in the selection of a suitable XAI method.
This paper is included in the Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation.

– We explore a new design point for traffic engineering on wide-area networks (WANs): directly optimizing traffic flow on the WAN using only historical data about traffic demands. Doing so obviates the need to explicitly estimate, or predict, future demands. Our method, which utilizes stochastic optimization, provably converges to the global optimum in well-studied theoretical models. We employ deep learning to scale to large WANs and real-world traffic. Our extensive empirical evaluation on real-world traffic and network topologies establishes that our approach’s TE quality almost matches that of an (infeasible) omniscient oracle, outperforming previously proposed approaches, and also substantially lowers runtimes.
Initial experiences with Direct Imaging of Neuronal Activity (DIANA) in humans

Abstract Functional MRI (fMRI) has been widely used to study activity patterns in the human brain. It infers neuronal activity from the associated hemodynamic response, which fundamentally limits its spatiotemporal specificity. In mice, the Direct Imaging of Neuronal Activity (DIANA) method revealed MRI signals that correlated with extracellular electric activity, showing high spatiotemporal specificity. In this work, we attempted DIANA in humans. Five experimental paradigms were tested, exploring different stimulus types (flickering noise patterns, and naturalistic images), stimulus durations (50–200 ms), and imaging resolution (2 × 2 × 5 mm3 and 1 × 1 × 5 mm3). Regions of interest (ROI) were derived from Blood Oxygen Level Dependent (BOLD) fMRI acquisitions (both EPI and FLASH based) and T1-weighted anatomical scans. In Paradigm I (n = 1), using flickering noise patterns, signals were detected that resembled possible functional activity from a small ROI. However, changes in stimulus duration did not lead to corresponding signal changes (Paradigm II; n = 1). Therefore, care should be taken not to mistake artifacts for neuronal activity. In Paradigm III (n = 3), when averaged across multiple subjects, a ~200 ms long 0.02% signal increase was observed ~100 ms after the stimulus onset (10x smaller than the expected signal). However, white matter control ROIs showed similarly large signal fluctuations. In Paradigm IV (n = 3), naturalistic image stimuli were used, but did not reveal signs of a potential functional signal. To reduce partial voluming effects and improve ROI definition, in Paradigm V (n = 3), we acquired data with higher resolution (1 × 1 × 5 mm3) using naturalistic images. However, no sign of activation was found. It is important to note that repetitive experiments with short interstimulus intervals were found to be strenuous for the subjects, which likely impacted data quality. To obtain better data, improvements in sequence and stimulus designs are needed to maximize the DIANA signal and minimize confounds. However, without a clear understanding of DIANA’s biophysical underpinnings it is difficult to do so. Therefore, it may be more effective to first investigate DIANA signals with simultaneously recorded electrophysiological signals in more controlled settings, e.g., in anesthetized mice.
Competence-Based Analysis of Language Models

Despite the recent success of large pretrained language models (LMs) on a variety of prompting tasks, these models can be alarmingly brittle to small changes in inputs or application contexts. To better understand such behavior and motivate the design of more robust LMs, we propose a general experimental framework, CALM (Competence-based Analysis of Language Models), where targeted causal interventions are utilized to damage an LM's internal representation of various linguistic properties in order to evaluate its use of each representation in performing a given task. We implement these interventions as gradient-based adversarial attacks, which (in contrast to prior causal probing methodologies) are able to target arbitrarily-encoded representations of relational properties, and carry out a case study of this approach to analyze how BERT-like LMs use representations of several relational properties in performing associated relation prompting tasks. We find that, while the representations LMs leverage in performing each task are highly entangled, they may be meaningfully interpreted in terms of the tasks where they are most utilized; and more broadly, that CALM enables an expanded scope of inquiry in LM analysis that may be useful in predicting and explaining weaknesses of existing LMs.
Tailed skyrmions—An obscure branch of magnetic solitons

We report tailed skyrmions—a new class of stable soliton solutions of the 2D chiral magnet model. Tailed skyrmions have elongated shapes and emerge in a narrow range of fields near the transition between the spin spirals and the saturated state. We analyze the stability range of these solutions in terms of external magnetic field and magnetocrystalline anisotropy. We calculate minimum energy paths and homotopies (continuous transitions) between tailed skyrmions of the same topological charge. The discovery of tailed skyrmions extends the diversity of already known solutions. This is illustrated by solitons with complex morphology, such as tailed skyrmion bags with and without chiral kinks.
Differentiable conjugacies for one-dimensional maps

Differentiable conjugacies link dynamical systems that share properties such as the stability multipliers of corresponding orbits. It provides a stronger classification than topological conjugacy, which only requires qualitative similarity. We describe some of the techniques and recent results that allow differentiable conjugacies to be defined for standard bifurcations, and explain how this leads to a new class of normal forms. Closed-form expressions for differentiable conjugacies exist between some chaotic maps, and we describe some of the constraints that make it possible to recognise when such conjugacies arise. This paper focuses on the consequences of the existence of differentiable conjugacies rather than the conjugacy classes themselves.
Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools

AI-based design tools are proliferating in professional software to assist engineering and industrial designers in complex manufacturing and design tasks. These tools take on more agentic roles than traditional computer-aided design tools and are often portrayed as “co-creators. ” Yet, working effectively with such systems requires different skills than working with complex CAD tools alone. To date, we know little about how engineering designers learn to work with AI-based design tools. In this study, we observed trained designers as they learned to work with two AI-based tools on a realistic design task. We find that designers face many challenges in learning to effectively co-create with current systems, including challenges in understanding and adjusting AI outputs and in communicating their design goals. Based on our findings, we highlight several design opportunities to better support designer-AI co-creation.
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition

To achieve robust far-field automatic speech recognition (ASR), existing techniques typically employ an acoustic front end (AFE) cascaded with a neural transducer (NT) ASR model. The AFE output, however, could be unreliable, as the beamforming output in AFE is steered to a wrong direction. A promising way to address this issue is to exploit the microphone signals before the beamforming stage and after the acoustic echo cancellation (post-AEC) in AFE. We argue that both, post-AEC and AFE outputs, are complementary and it is possible to leverage the redundancy between these signals to compensate for potential AFE processing errors. We present two fusion networks to explore this redundancy and aggregate these multi-channel (MC) signals: (1) Frequency-LSTM based, and (2) Convolutional Neural Network based fusion networks. We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion. Our experimental results on commercial virtual assistant tasks demonstrate that using the AFE output and two post-AEC signals with fusion networks offers up to 25.9% word error rate (WER) relative improvement over the model using the AFE output only, at the cost of<= 2% parameter increase.
Multi-Armed Bandits with Generalized Temporally-Partitioned Rewards

Decision-making problems of sequential nature, where decisions made in the past may have an impact on the future, are used to model many practically important applications. In some real-world applications, feedback about a decision is delayed and may arrive via partial rewards that are observed with different delays. Motivated by such scenarios, we propose a novel problem formulation called multi-armed bandits with generalized temporally-partitioned rewards. To formalize how feedback about a decision is partitioned across several time steps, we introduce $\beta$-spread property. We derive a lower bound on the performance of any uniformly efficient algorithm for the considered problem. Moreover, we provide an algorithm called TP-UCB-FR-G and prove an upper bound on its performance measure. In some scenarios, our upper bound improves upon the state of the art. We provide experimental results validating the proposed algorithm and our theoretical results.
Observing the two-dimensional Bose glass in an optical quasicrystal

The combined effect of disorder and interactions is central to the richness of condensed matter physics and can lead to novel quantum states such as the Bose glass phase in disordered bosonic systems. Here, we report on the experimental realisation of the two-dimensional Bose glass using ultra-cold atoms in an eight-fold symmetric quasicrystalline optical lattice. By probing the coherence properties of the system, we observe a Bose glass to superfluid transition and map out the phase diagram in the weakly interacting regime. Moreover, we reveal the non-ergodic nature of the Bose glass by probing the capability to restore coherence. Our observations are in good agreement with recent quantum Monte Carlo predictions and pave the way for experimentally testing the connection between the Bose glass, many-body localisation, and glassy dynamics more generally.
On discrete ground states of rotating Bose-Einstein condensates

The ground states of Bose-Einstein condensates in a rotating frame can be described as constrained minimizers of the Gross-Pitaevskii energy functional with an angular momentum term. In this paper we consider the corresponding discrete minimization problem in Lagrange finite element spaces of arbitrary polynomial order and we investigate the approximation properties of discrete ground states. In particular, we prove a priori error estimates of optimal order in the $L^2$- and $H^1$-norm, as well as for the ground state energy and the corresponding chemical potential. A central issue in the analysis of the problem is the missing uniqueness of ground states, which is mainly caused by the invariance of the energy functional under complex phase shifts. Our error analysis is therefore based on an Euler-Lagrange functional that we restrict to certain tangent spaces in which we have local uniqueness of ground states. This gives rise to an error decomposition that is ultimately used to derive the desired a priori error estimates. We also present numerical experiments to illustrate various aspects of the problem structure.
Lifting to truncated Brown-Peterson spectra and Hodge-de Rham degeneration in characteristic $p>0$

The goal of this note is to prove that Hodge-de Rham degeneration holds for smooth and proper $\mathbf{F}_p$-schemes $X$ with $\dim(X)<p^n$ as soon as its category of quasicoherent sheaves admits a lift to the truncated Brown-Peterson spectrum $\mathrm{BP}\langle n-1\rangle$, and the Hochschild-Kostant-Rosenberg spectral sequence for $X$ degenerates at the $E_2$-page. This is obtained from a noncommutative version, whose proof is essentially the same as Mathew's argument in arXiv:1710.09045.
What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy

Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. With DP, privacy protections are probabilistic: they are bounded by the privacy budget parameter, $\epsilon$. Prior work in health and computational science finds that people struggle to reason about probabilistic risks. Yet, communicating the implications of $\epsilon$ to people contributing their data is vital to avoiding privacy theater -- presenting meaningless privacy protection as meaningful -- and empowering more informed data-sharing decisions. Drawing on best practices in risk communication and usability, we develop three methods to convey probabilistic DP guarantees to end users: two that communicate odds and one offering concrete examples of DP outputs. We quantitatively evaluate these explanation methods in a vignette survey study ($n=963$) via three metrics: objective risk comprehension, subjective privacy understanding of DP guarantees, and self-efficacy. We find that odds-based explanation methods are more effective than (1) output-based methods and (2) state-of-the-art approaches that gloss over information about $\epsilon$. Further, when offered information about $\epsilon$, respondents are more willing to share their data than when presented with a state-of-the-art DP explanation; this willingness to share is sensitive to $\epsilon$ values: as privacy protections weaken, respondents are less likely to share data.
Onboard Dynamic-Object Detection and Tracking for Autonomous Robot Navigation With RGB-D Camera

Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving field have investigated the 3D object detection problem, the usage of dense point clouds from a heavy Light Detection and Ranging (LiDAR) sensor and their high computation cost for learning-based data processing make those methods not applicable to small robots, such as vision-based UAVs with small onboard computers. To address this issue, we propose a lightweight 3D dynamic obstacle detection and tracking (DODT) method based on an RGB-D camera, which is designed for low-power robots with limited computing power. Our method adopts a novel ensemble detection strategy, combining multiple computationally efficient but low-accuracy detectors to achieve real-time high-accuracy obstacle detection. Besides, we introduce a new feature-based data association and tracking method to prevent mismatches utilizing point clouds' statistical features. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identification. The proposed method is implemented in a small quadcopter, and the results show that our method can achieve the lowest position error (0.11 m) and a comparable velocity error (0.23 m/s) across the benchmarking algorithms running on the robot's onboard computer. The flight experiments prove that the tracking results from the proposed method can make the robot efficiently alter its trajectory for navigating dynamic environments. Our software is available on GitHub1 as an open-source ROS package.