Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Neighborhood Construction for Structured Large Discrete Action Spaces

Large discrete action spaces (LDAS) remain a central challenge in reinforcement learning. Existing solution approaches can handle unstructured LDAS with up to a few million actions. However, many real-world applications in logistics, production, and transportation systems have combinatorial action spaces, whose size grows well beyond millions of actions, even on small instances. Fortunately, such action spaces exhibit structure, e.g., equally spaced discrete resource units. With this work, we focus on handling structured LDAS (SLDAS) with sizes that cannot be handled by current benchmarks: we propose Dynamic Neighborhood Construction (DNC), a novel exploitation paradigm for SLDAS. We present a scalable neighborhood exploration heuristic that utilizes this paradigm and efficiently explores the discrete neighborhood around the continuous proxy action in structured action spaces with up to $10^{73}$ actions. We demonstrate the performance of our method by benchmarking it against three state-of-the-art approaches designed for large discrete action spaces across two distinct environments. Our results show that DNC matches or outperforms state-of-the-art approaches while being computationally more efficient. Furthermore, our method scales to action spaces that so far remained computationally intractable for existing methodologies.
Power-law exponent in the transition period of decay in grid turbulence

Hot-wire measurements are carried out in grid-generated turbulence at moderate to low Taylor microscale Reynolds number $Re_{{\it\lambda}}$ to assess the appropriateness of the commonly used power-law decay for the mean turbulent kinetic energy (e.g. $k\sim x^{n}$ , with $n\leqslant -1$ ). It is found that in the region outside the initial and final periods of decay, which we designate a transition region, a power law with a constant exponent $n$ cannot describe adequately the decay of turbulence from its initial to final stages. One is forced to use a family of power laws of the form $x^{n_{i}}$ , where $n_{i}$ is a different constant over a portion $i$ of the decay time during the decay period. Accordingly, it is currently not possible to determine whether any grid-generated turbulence reported in the literature decays according to Saffman or Batchelor because the reported data fall in the transition period where $n$ differs from its initial and final values. It is suggested that a power law of the form $k\sim x^{n_{init}+m(x)}$ , where $m(x)$ is a continuous function of $x$ , could be used to describe the decay from the initial period to the final stage. The present results, which corroborate the numerical simulations of decaying homogeneous isotropic turbulence of Orlandi & Antonia (J. Turbul., vol. 5, 2004, doi:10.1088/1468-5248/5/1/009) and Meldi & Sagaut (J. Turbul., vol. 14, 2013, pp. 24–53), show that the values of $n$ reported in the literature, and which fall in the transition region, have been mistakenly assigned to the initial stage of decay.
IT/OT Integration by Design

The four Industry 4.0 design principles information transparency, technical assistance, interconnection, and decentralized decisions pose challenges in integrating information technology (IT) and operational technology (OT) solutions in industrial systems. These different solutions have conflicting requirements, making interfaces between them problematic for both systems and organizations. An Industrial Business Process Twin (IBPT) entity, acting as an intermediary between the realms of IT and OT, has been proposed in a previous work, to effectively reduce the amount of required IT/OT interfaces in an attempt of overcoming this situation. In this work, we investigate the effects of this approach during the design phase. We argue that, by eliminating interfaces between IT and OT components in the system design, this approach is therefore eliminating conflicting communication channels within the organization's communication structure. In order to verify our argument, we develop a model of our IBPT concept according to the Reference Architecture Model Industrie 4.0 (RAMI4.0) using an Industry 4.0 scenario addressing the four essential Industry 4.0 design principles. Results show that the IBPT approach indeed eliminates potentially conflicting IT/OT interfaces during the system design phase.
Optimal Decision Trees for Separable Objectives: Pushing the Limits of Dynamic Programming

Global optimization of decision trees has shown to be promising in terms of accuracy, size, and consequently human comprehensibility. However, many of the methods used rely on general-purpose solvers for which scalability remains an issue. Dynamic programming methods have been shown to scale much better because they exploit the tree structure by solving subtrees as independent subproblems. However, this only works when an objective can be optimized separately for subtrees. We explore this relationship in detail and show necessary and sufficient conditions for such separability and generalize previous dynamic programming approaches into a framework that can optimize any combination of separable objectives and constraints. Experiments on four application domains show the general applicability of this framework, while outperforming the scalability of general-purpose solvers by a large margin.
SVVAD: Personal Voice Activity Detection for Speaker Verification

Voice activity detection (VAD) improves the performance of speaker verification (SV) by preserving speech segments and attenuating the effects of non-speech. However, this scheme is not ideal: (1) it fails in noisy environments or multi-speaker conversations; (2) it is trained based on inaccurate non-SV sensitive labels. To address this, we propose a speaker verification-based voice activity detection (SVVAD) framework that can adapt the speech features according to which are most informative for SV. To achieve this, we introduce a label-free training method with triplet-like losses that completely avoids the performance degradation of SV due to incorrect labeling. Extensive experiments show that SVVAD significantly outperforms the baseline in terms of equal error rate (EER) under conditions where other speakers are mixed at different ratios. Moreover, the decision boundaries reveal the importance of the different parts of speech, which are largely consistent with human judgments.
Effects of Partial Ionization on Magnetic Flux Emergence in the Sun

We have performed 3D numerical simulations to investigate the effect of partial ionization on the process of magnetic flux emergence. In our study, we have modified the single-fluid MHD equations to include the presence of neutrals and have performed two basic experiments: one that assumes a fully ionized plasma (FI case) and one that assumes a partially ionized plasma (PI case). We find that the PI case brings less dense plasma to and above the solar surface. Furthermore, we find that partial ionization alters the emerging magnetic field structure, leading to a different shape of the polarities in the emerged bipolar regions compared to the FI case. The amount of emerging flux into the solar atmosphere is larger in the PI case, which has the same initial plasma beta as the FI case, but a larger initial magnetic field strength. The expansion of the field above the photosphere occurs relatively earlier in the PI case, and we confirm that the inclusion of partial ionization reduces cooling due to adiabatic expansion. However, it does not appear to work as a heating mechanism for the atmospheric plasma. The performance of these experiments in three dimensions shows that PI does not prevent the formation of unstable magnetic structures, which erupt into the outer solar atmosphere.
Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound’s poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson’s ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and <inline-formula> <tex-math notation="LaTeX">${L}{1}$ </tex-math></inline-formula>-MechSOUL (<inline-formula> <tex-math notation="LaTeX">${L}{1}$ </tex-math></inline-formula>-norm-based MechSOUL), which optimize <inline-formula> <tex-math notation="LaTeX">${L}{2}$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">${L}{1}$ </tex-math></inline-formula>-norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and <inline-formula> <tex-math notation="LaTeX">${L}{1}$ </tex-math></inline-formula>-MechSOUL’s lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and <inline-formula> <tex-math notation="LaTeX">${L}{1}$ </tex-math></inline-formula>-MechSOUL at <uri>https://code.sonography. ai</uri>.
Quantum Brachistochrone for the Majorana Particle

This paper covers some new results from the theory of time optimal quantum control, with particular application to relativistic particles including Majorana fermions. We give a brief review of the state of affairs regarding experimental results, and a concise overview of the methodology of time optimal control and unitary transformation. This technique is then applied to the Majorana particle and used to derive four dimensional conservation laws. We then discuss the nature of time transformation in these systems and some simple scattering problems.
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play

Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning. One characteristic of such experience is that the learner sees the same object from several different viewpoints. In this paper, we study how learning signals that equate different viewpoints -- e.g., assigning similar representations to different views of a single object -- can support robust visual learning. We use the Toybox dataset, which contains egocentric videos of humans manipulating different objects, and conduct experiments using a computer vision framework for self-supervised contrastive learning. We find that representations learned by equating different physical viewpoints of an object benefit downstream image classification accuracy. Further experiments show that this performance improvement is robust to variations in the gaps between viewpoints, and that the benefits transfer to several different image classification tasks.
Active causal structure learning with advice

We introduce the problem of active causal structure learning with advice. In the typical well-studied setting, the learning algorithm is given the essential graph for the observational distribution and is asked to recover the underlying causal directed acyclic graph (DAG) $G^*$ while minimizing the number of interventions made. In our setting, we are additionally given side information about $G^*$ as advice, e.g. a DAG $G$ purported to be $G^*$. We ask whether the learning algorithm can benefit from the advice when it is close to being correct, while still having worst-case guarantees even when the advice is arbitrarily bad. Our work is in the same space as the growing body of research on algorithms with predictions. When the advice is a DAG $G$, we design an adaptive search algorithm to recover $G^*$ whose intervention cost is at most $O(\max\{1, \log \psi\})$ times the cost for verifying $G^*$; here, $\psi$ is a distance measure between $G$ and $G^*$ that is upper bounded by the number of variables $n$, and is exactly 0 when $G=G^*$. Our approximation factor matches the state-of-the-art for the advice-less setting.
Fibration symmetries and cluster synchronization in the Caenorhabditis elegans connectome

Capturing how the Caenorhabditis elegans connectome structure gives rise to its neuron functionality remains unclear. It is through fiber symmetries found in its neuronal connectivity that synchronization of a group of neurons can be determined. To understand these we investigate graph symmetries and search for such in the symmetrized versions of the forward and backward locomotive sub-networks of the Caenorhabditi elegans worm neuron network. The use of ordinarily differential equations simulations admissible to these graphs are used to validate the predictions of these fiber symmetries and are compared to the more restrictive orbit symmetries. Additionally fibration symmetries are used to decompose these graphs into their fundamental building blocks which reveal units formed by nested loops or multilayered fibers. It is found that fiber symmetries of the connectome can accurately predict neuronal synchronization even under not idealized connectivity as long as the dynamics are within stable regimes of simulations.
Humans in 4D: Reconstructing and Tracking Humans with Transformers

We present an approach to reconstruct humans and track them over time. At the core of our approach, we propose a fully "transformerized" version of a network for human mesh recovery. This network, HMR 2.0, advances the state of the art and shows the capability to analyze unusual poses that have in the past been difficult to reconstruct from single images. To analyze video, we use 3D reconstructions from HMR 2.0 as input to a tracking system that operates in 3D. This enables us to deal with multiple people and maintain identities through occlusion events. Our complete approach, 4DHumans, achieves state-of-the-art results for tracking people from monocular video. Furthermore, we demonstrate the effectiveness of HMR 2.0 on the downstream task of action recognition, achieving significant improvements over previous pose-based action recognition approaches. Our code and models are available on the project website: https://shubham-goel.github. io/4dhumans/.
Convection rolls and three-dimensional particle dynamics in merging solute streams

Microparticles migrate in response to gradients in solute concentration through diffusiophoresis and diffusioosmosis. Merging streams of fluid with distinct solute concentrations is a common strategy for producing a steady concentration gradient with continuous flow in microfluidic devices; the solute concentration gradient and consequent diffusiophoresis are primarily normal to the background flow. This is particularly useful in separation and filtration processes, as it results in regions of particle accrual and depletion in continuous flows. Such systems have been examined in several classic papers on diffusiophoresis, with a focus on the particle dynamics far from boundaries. We show, through experiments, simulations, and theory, that diffusioosmotic flow along certain boundaries can result in significant changes in particle dynamics and particle focusing in near-wall regions. The nonzero velocity at charged surfaces draws solute and particles along the boundary until the flow ultimately recirculates. These"convection rolls,"which result in the spanwise migration of polystyrene particles close to boundaries, are apparent near a glass surface but vanish when the surface is coated with gold. The three-dimensional nature of the dynamics could have implications for the design of microfluidic devices: Channel materials can be selected to enhance or suppress near-wall flows. Additionally, we demonstrate the importance of considering solute concentration-dependent models for diffusiophoretic and diffusioosmotic mobility in capturing the dynamics of particles, particularly in regions of low solute concentration.
Are Large Kernels Better Teachers than Transformers for ConvNets?

This paper reveals a new appeal of the recently emerged large-kernel Convolutional Neural Networks (ConvNets): as the teacher in Knowledge Distillation (KD) for small-kernel ConvNets. While Transformers have led state-of-the-art (SOTA) performance in various fields with ever-larger models and labeled data, small-kernel ConvNets are considered more suitable for resource-limited applications due to the efficient convolution operation and compact weight sharing. KD is widely used to boost the performance of small-kernel ConvNets. However, previous research shows that it is not quite effective to distill knowledge (e.g., global information) from Transformers to small-kernel ConvNets, presumably due to their disparate architectures. We hereby carry out a first-of-its-kind study unveiling that modern large-kernel ConvNets, a compelling competitor to Vision Transformers, are remarkably more effective teachers for small-kernel ConvNets, due to more similar architectures. Our findings are backed up by extensive experiments on both logit-level and feature-level KD ``out of the box", with no dedicated architectural nor training recipe modifications. Notably, we obtain the \textbf{best-ever pure ConvNet} under 30M parameters with \textbf{83.1\%} top-1 accuracy on ImageNet, outperforming current SOTA methods including ConvNeXt V2 and Swin V2. We also find that beneficial characteristics of large-kernel ConvNets, e.g., larger effective receptive fields, can be seamlessly transferred to students through this large-to-small kernel distillation. Code is available at: \url{https://github.com/VITA-Group/SLaK}.
Too Large; Data Reduction for Vision-Language Pre-Training

This paper examines the problems of severe image-text misalignment and high redundancy in the widely-used large-scale Vision-Language Pre-Training (VLP) datasets. To address these issues, we propose an efficient and straightforward Vision-Language learning algorithm called ${\color {Purple}{TL;DR}}$, which aims to compress the existing large VLP data into a small, high-quality set. Our approach consists of two major steps. First, a codebook-based encoder-decoder captioner is developed to select representative samples. Second, a new caption is generated to complement the original captions for selected samples, mitigating the text-image misalignment problem while maintaining uniqueness. As the result, ${\color {Purple}{TL;DR}}$ enables us to reduce the large dataset into a small set of high-quality data, which can serve as an alternative pre-training dataset. This algorithm significantly speeds up the time-consuming pretraining process. Specifically, ${\color {Purple}{TL;DR}}$ can compress the mainstream VLP datasets at a high ratio, e.g., reduce well-cleaned CC3M dataset from 2.82M to 0.67M (~24%) and noisy YFCC15M from 15M to 2.5M (~16.7%). Extensive experiments with three popular VLP models over seven downstream tasks show that VLP model trained on the compressed dataset provided by ${\color {Purple}{TL;DR}}$ can perform similar or even better results compared with training on the full-scale dataset1.
Probing post-measurement entanglement without post-selection

We study the problem of observing quantum collective phenomena emerging from large numbers of measurements. These phenomena are difficult to observe in conventional experiments because, in order to distinguish the effects of measurement from dephasing, it is necessary to post-select on sets of measurement outcomes whose Born probabilities are exponentially small in the number of measurements performed. An unconventional approach, which avoids this exponential `post-selection problem', is to construct cross-correlations between experimental data and the results of simulations on classical computers. However, these cross-correlations generally have no definite relation to physical quantities. We first show how to incorporate shadow tomography into this framework, thereby allowing for the construction of quantum information-theoretic cross-correlations. We then identify cross-correlations which both upper and lower bound the measurement-averaged von Neumann entanglement entropy, and additional cross-correlations which lower bound the measurement-averaged purity and entanglement negativity. These bounds show that experiments can be performed to constrain post-measurement entanglement without the need for post-selection. To illustrate our technique we consider how it could be used to observe the measurement-induced entanglement transition in Haar-random quantum circuits. We use exact numerical calculations as proxies for quantum simulations and, to highlight the fundamental limitations of classical memory, we construct cross-correlations with tensor-network calculations at finite bond dimension. Our results reveal a signature of measurement-induced criticality that can be observed using a quantum simulator in polynomial time and with polynomial classical memory.
How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases

Accurate syntactic representations are essential for robust generalization in natural language. Recent work has found that pre-training can teach language models to rely on hierarchical syntactic features—as opposed to incorrect linear features—when performing tasks after fine-tuning. We test what aspects of pre-training are important for endowing encoder-decoder Transformers with an inductive bias that favors hierarchical syntactic generalizations. We focus on architectural features (depth, width, and number of parameters), as well as the genre and size of the pre-training corpus, diagnosing inductive biases using two syntactic transformation tasks: question formation and passivization, both in English. We find that the number of parameters alone does not explain hierarchical generalization: model depth plays greater role than model width. We also find that pre-training on simpler language, such as child-directed speech, induces a hierarchical bias using an order-of-magnitude less data than pre-training on more typical datasets based on web text or Wikipedia; this suggests that in cognitively plausible language acquisition settings, neural language models may be more data-efficient than previously thought.
A High-resolution Spectroscopic Analysis of Aminoacrylonitrile and an Interstellar Search toward G+0.693

Cyanides, ranging from three carbon atoms to polycyclic aromatic hydrocarbons, and alkenyl compounds are abundant in the interstellar medium. Aminoacrylonitrile (3-Amino-2-propenenitrile, H2N–CH=CH–C≡N), an alkenyl cyanide, thus represents a promising candidate for new interstellar detection. A comprehensive spectroscopic laboratory investigation of aminoacrylonitrile in its rotational ground vibrational state has been herein performed. The measurements carried out up to the THz regime made it possible to generate a precise set of reliable rest frequencies for its search in space up to submillimeter wavelengths. The Z-aminoacrylonitrile (Z-apn) isomer spectrum has been recorded employing a source-modulated submillimeter spectrometer, from 80 GHz to 1 THz. A combination of Doppler and sub-Doppler measurement regimes allowed one to record 600 new lines. The collected data have enabled the characterization of a set of spectroscopic parameters up to decic centrifugal distortion constants. The catalog generated from the improved spectral data has been used for the search of Z-apn in the spectral survey of the G+0.693-0.027 molecular cloud located in the central molecular zone, in the proximity of the Galactic center.