Miracle-dz/UniFAR-FTUs huggingface dataset:
Writing in the Air: Unconstrained Text Recognition From Finger Movement Using Spatio-Temporal Convolution

In this article, we introduce a new benchmark dataset for the challenging writing in the air (WiTA) task—an elaborate task bridging vision and natural language processing (NLP). WiTA implements an intuitive and natural writing method with finger movement for human–computer interaction (HCI). Our WiTA dataset will facilitate the development of data-driven WiTA systems, which, thus, far have displayed unsatisfactory performance—due to lack of dataset as well as traditional statistical models they have adopted. Our dataset consists of five subdatasets in two languages (Korean and English) and amounts to 209 926 video instances from 122 participants. We capture finger movement for WiTA with red-green-blue (RGB) cameras to ensure wide accessibility and cost-efficiency. Next, we propose spatio-temporal residual network architectures inspired by 3-D ResNet. These models perform unconstrained text recognition from finger movement, guarantee a real-time operation [$>$100 frames per second (FPS)], and will serve as an evaluation standard.
Edge and Corner Detection in Unorganized Point Clouds for Robotic Pick and Place Applications

In this paper, we propose a novel edge and corner detection algorithm for an unorganized point cloud. Our edge detection method classifies a query point as an edge point by evaluating the distribution of local neighboring points around the query point. The proposed technique has been tested on generic items such as dragons, bunnies, and coffee cups from the Stanford 3D scanning repository. The proposed technique can be directly applied to real and unprocessed point cloud data of random clutter of objects. To demonstrate the proposed technique's efficacy, we compare it to the other solutions for 3D edge extractions in an unorganized point cloud data. We observed that the proposed method could handle the raw and noisy data with little variations in parameters compared to other methods. We also extend the algorithm to estimate the 6D pose of known objects in the presence of dense clutter while handling multiple instances of the object. The overall approach is tested for a warehouse application, where an actual UR5 robot manipulator is used for robotic pick and place operations in an autonomous mode.
Activity Stabilization in a Population Model of Working Memory by Sinusoidal and Noisy Inputs

According to mechanistic theories of working memory (WM), information is retained as stimulus-dependent persistent spiking activity of cortical neural networks. Yet, how this activity is related to changes in the oscillatory profile observed during WM tasks remains a largely open issue. We explore joint effects of input gamma-band oscillations and noise on the dynamics of several firing rate models of WM. The considered models have a metastable active regime, i.e., they demonstrate long-lasting transient post-stimulus firing rate elevation. We start from a single excitatory-inhibitory circuit and demonstrate that either gamma-band or noise input could stabilize the active regime, thus supporting WM retention. We then consider a system of two circuits with excitatory intercoupling. We find that fast coupling allows for better stabilization by common noise compared to independent noise and stronger amplification of this effect by in-phase gamma inputs compared to anti-phase inputs. Finally, we consider a multi-circuit system comprised of two clusters, each containing a group of circuits receiving a common noise input and a group of circuits receiving independent noise. Each cluster is associated with its own local gamma generator, so all its circuits receive gamma-band input in the same phase. We find that gamma-band input differentially stabilizes the activity of the “common-noise” groups compared to the “independent-noise” groups. If the inter-cluster connections are fast, this effect is more pronounced when the gamma-band input is delivered to the clusters in the same phase rather than in the anti-phase. Assuming that the common noise comes from a large-scale distributed WM representation, our results demonstrate that local gamma oscillations can stabilize the activity of the corresponding parts of this representation, with stronger effect for fast long-range connections and synchronized gamma oscillations.
Failing Conceptually: Concept-Based Explanations of Dataset Shift

Despite their remarkable performance on a wide range of visual tasks, machine learning technologies often succumb to data distribution shifts. Consequently, a range of recent work explores techniques for detecting these shifts. Unfortunately, current techniques offer no explanations about what triggers the detection of shifts, thus limiting their utility to provide actionable insights. In this work, we present Concept Bottleneck Shift Detection (CBSD): a novel explainable shift detection method. CBSD provides explanations by identifying and ranking the degree to which high-level human-understandable concepts are affected by shifts. Using two case studies (dSprites and 3dshapes), we demonstrate how CBSD can accurately detect underlying concepts that are affected by shifts and achieve higher detection accuracy compared to state-of-the-art shift detection methods.
Monotonicity Marking from Universal Dependency Trees

Dependency parsing is a tool widely used in the field of Natural language processing and computational linguistics. However, there is hardly any work that connects dependency parsing to monotonicity, which is an essential part of logic and linguistic semantics. In this paper, we present a system that automatically annotates monotonicity information based on Universal Dependency parse trees. Our system utilizes surface-level monotonicity facts about quantifiers, lexical items, and token-level polarity information. We compared our system’s performance with existing systems in the literature, including NatLog and ccg2mono, on a small evaluation dataset. Results show that our system outperforms NatLog and ccg2mono.
Sequential Deconfounding for Causal Inference with Unobserved Confounders

Using observational data to estimate the effect of a treatment is a powerful tool for decision-making when randomized experiments are infeasible or costly. However, observational data often yields biased estimates of treatment effects, since treatment assignment can be confounded by unobserved variables. A remedy is offered by deconfounding methods that adjust for such unobserved confounders. In this paper, we develop the Sequential Deconfounder, a method that enables estimating individualized treatment effects over time in presence of unobserved confounders. This is the first deconfounding method that can be used in a general sequential setting (i.e., with one or more treatments assigned at each timestep). The Sequential Deconfounder uses a novel Gaussian process latent variable model to infer substitutes for the unobserved confounders, which are then used in conjunction with an outcome model to estimate treatment effects over time. We prove that using our method yields unbiased estimates of individualized treatment responses over time. Using simulated and real medical data, we demonstrate the efficacy of our method in deconfounding the estimation of treatment responses over time.
Multitask Learning for VVC Quality Enhancement and Super-Resolution

The latest video coding standard, called versatile video coding (VVC), includes several novel and refined coding tools at different levels of the coding chain. These tools bring significant coding gains with respect to the previous standard, high efficiency video coding (HEVC). However, the encoder may still introduce visible coding artifacts, mainly caused by coding decisions applied to adjust the bitrate to the available bandwidth. Hence, pre and post-processing techniques are generally added to the coding pipeline to improve the quality of the decoded video. These methods have recently shown outstanding results compared to traditional approaches, thanks to the recent advances in deep learning. Generally, multiple neural networks are trained independently to perform different tasks, thus omitting to benefit from the redundancy that exists between the models. In this paper, we investigate a learning-based solution as a post-processing step to enhance the decoded VVC video quality. Our method relies on multitask learning to perform both quality enhancement and super-resolution using a single shared network optimized for multiple degradation levels. The proposed solution enables a good performance in both mitigating coding artifacts and super-resolution with fewer network parameters compared to traditional specialized architectures.
Acoustic Metagrating Circulators: Nonreciprocal, Robust, and Tunable Manipulation with Unitary Efficiency

Nonreciprocal signal operation is highly desired for various acoustic applications, where protection from unwanted backscattering can be realized so that transmitting and receiving signals are processed in a full-duplex mode. Here we present the realization of a class of nonreciprocal circulators based on simply structured acoustic metagratings, which consist only of a few solid cylinders and a steady fluid flow with low velocity. These innovative metagratings are intelligently designed via a diffraction analysis of the linearized potential flow equation and a genetic-algorithm-based optimization process. Unitary reflection efficiency between desired ports of the circulators are demonstrated through full-wave numerical simulations, confirming nonreciprocal and robust circulation of the acoustic signal over a broad range of flow velocity magnitude and profile. Our design provides a feasible degree of tunability, including switching from reciprocal to nonreciprocal operation and reversing the handedness of the circulator, presenting a convenient but efficient approach for the realization of nonreciprocal acoustic devices from wavelength-thick metagratings. It may find applications in various scenarios including underwater communication, energy harvesting, and acoustic sensing. ________ *phjunmei@scut.edu. cn
Line-of-sight effects in strong gravitational lensing

While most strong-gravitational-lensing systems may be roughly modelled by a single massive object between the source and the observer, in the details all the structures near the light path contribute to the observed images. These additional contributions, known as line-of-sight effects, are non-negligible in practice. This article proposes a new theoretical framework to model the line-of-sight effects, together with very promising applications at the interface of weak and strong lensing. Our approach relies on the dominant-lens approximation, where one deflector is treated as the main lens while the others are treated as perturbations. The resulting framework is technically simpler to handle than the multi-plane lensing formalism, while allowing one to consistently model any sub-critical perturbation. In particular, it is not limited to the usual external-convergence and external-shear parameterisation. As a first application, we identify a specific notion of line-of-sight shear that is not degenerate with the ellipticity of the main lens, and which could thus be extracted from strong-lensing images. This result supports and improves the recent proposal that Einstein rings might be powerful probes of cosmic shear. As a second application, we investigate the distortions of strong-lensing critical curves under line-of-sight effects, and more particularly their correlations across the sky. We find that such correlations may be used to probe, not only the large-scale structure of the Universe, but also the dark-matter halo profiles of strong lenses. This last possibility would be a key asset to improve the accuracy of the measurement of the Hubble-Lemaître constant via time-delay cosmography.
A novel Time-frequency Transformer and its Application in Fault Diagnosis of Rolling Bearings

The scope of data-driven fault diagnosis models is greatly extended through deep learning (DL). However, the classical convolution and recurrent structure have their defects in computational e ﬃ ciency and feature representation, while the latest Transformer architecture based on attention mechanism has not yet been applied in this ﬁeld. To solve these problems, we propose a novel time-frequency Transformer (TFT) model inspired by the massive success of vanilla Transformer in sequence processing. Specially, we design a fresh tokenizer and encoder module to extract e ﬀ ective abstractions from the time-frequency representation (TFR) of vibration signals. On this basis, a new end-to-end fault diagnosis framework based on time-frequency Transformer is presented in this paper. Through the case studies on bearing experimental datasets, we construct the optimal Transformer structure and verify its fault diagnosis performance. The superiority of the proposed method is demonstrated in comparison with the benchmark models and other state-of-the-art methods.
Mechanisms of producing primordial black holes by breaking the 
SU(2, 1)/SU(2)×U(1)
 symmetry

In this paper we present a class of potentials derived by no-scale supergravity in order to explain the production of primordial black holes (PBHs) in the Universe. By breaking the SU(2,1)/SU(2)$\times$U(1) symmetry we fix one of the two chiral fields and we derive effective scalar potentials which are capable of generating PBHs. Specifically, we modify well-known superpotentials, which reduce to Starobinsky-like effective scalar potentials. Thus, we derive scalar potentials which, on the one hand, explain the production of PBHs and, on the other hand, they conserve the transformation laws, which yield from the parametrization of the coset SU(2,1)/SU(2)$\times$U(1). Moreover, we generate PBHs by modifying the kinetic term of the Langrangian (or the K\"ahler potential) and we keep the superpotentials unmodified. In all cases we evaluate the fractional abundances of PBHs by comparing Press-Schechter approach and peak theory, while focusing on explaining the dark matter in the Universe. All models are in complete consistence with Planck constraints.
Heads in the Clouds? Measuring Universities' Migration to Public Clouds: Implications for Privacy & Academic Freedom

With the emergence of remote education and work in universities due to COVID-19, the 'zoomification' of higher education, i.e., the migration of universities to the clouds, reached the public discourse. Ongoing discussions reason about how this shift will take control over students' data away from universities, and may ultimately harm the privacy of researchers and students alike. However, there has been no comprehensive measurement of universities' use of public clouds and reliance on Software-as-a-Service offerings to assess how far this migration has already progressed. We perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between January 2015 and October 2022. We find that cloud adoption differs between countries, with one cluster (Germany, France, Austria, Switzerland) showing a limited move to clouds, while the other (U.S., U.K., the Netherlands, THE Top100) frequently outsources universities' core functions and services---starting long before the COVID-19 pandemic. We attribute this clustering to several socio-economic factors in the respective countries, including the general culture of higher education and the administrative paradigm taken towards running universities. We then analyze and interpret our results, finding that the implications reach beyond individuals' privacy towards questions of academic independence and integrity.
The phase diagram of carbon dioxide from correlation functions and a many-body potential.

The phase stability and equilibria of carbon dioxide are investigated from 125-325 K and 1-10 000 atm using extensive molecular dynamics (MD) simulations and the Two-Phase Thermodynamics (2PT) method. We devise a direct approach for calculating phase diagrams, in general, by considering the separate chemical potentials of the isolated phase at specific points on the P-T diagram. The unique ability of 2PT to accurately and efficiently approximate the entropy and Gibbs energy of liquids allows for assignment of phase boundaries from relatively short (∼100 ps) MD simulations. We validate our approach by calculating the critical properties of the flexible elementary physical model 2, showing good agreement with previous results. We show, however, that the incorrect description of the short-range Pauli force and the lack of molecular charge polarization lead to deviations from experiments at high pressures. We, thus, develop a many-body, fluctuating charge model for CO2, termed CO2-Fq, from high level quantum mechanics (QM) calculations that accurately capture the condensed phase vibrational properties of the solid (including the Fermi resonance at 1378 cm-1) as well as the diffusional properties of the liquid, leading to overall excellent agreement with experiments over the entire phase diagram. This work provides an efficient computational approach for determining phase diagrams of arbitrary systems and underscores the critical role of QM charge reorganization physics in molecular phase stability.
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds. Existing work has primarily focused on a small number of groups, such as the translation, rotation, and permutation groups. In this work we provide a completely general algorithm for solving for the equivariant layers of matrix groups. In addition to recovering solutions from other works as special cases, we construct multilayer perceptrons equivariant to multiple groups that have never been tackled before, including $\mathrm{O}(1,3)$, $\mathrm{O}(5)$, $\mathrm{Sp}(n)$, and the Rubik's cube group. Our approach outperforms non-equivariant baselines, with applications to particle physics and dynamical systems. We release our software library to enable researchers to construct equivariant layers for arbitrary matrix groups.
Search for Neutrinos in Coincidence with Gravitational Wave Events from the LIGO–Virgo O3a Observing Run with the Super-Kamiokande Detector

The Super-Kamiokande detector can be used to search for neutrinos in time coincidence with gravitational waves detected by the LIGO–Virgo Collaboration (LVC). Both low-energy (7–100 MeV) and high-energy (0.1–105 GeV) samples were analyzed in order to cover a very wide neutrino spectrum. Follow-ups of 36 (out of 39) gravitational waves reported in the GWTC-2 catalog were examined; no significant excess above the background was observed, with 10 (24) observed neutrinos compared with 4.8 (25.0) expected events in the high-energy (low-energy) samples. A statistical approach was used to compute the significance of potential coincidences. For each observation, p-values were estimated using neutrino direction and LVC sky map; the most significant event (GW190602_175927) is associated with a post-trial p-value of 7.8% (1.4σ). Additionally, flux limits were computed independently for each sample and by combining the samples. The energy emitted as neutrinos by the identified gravitational wave sources was constrained, both for given flavors and for all flavors assuming equipartition between the different flavors, independently for each trigger and by combining sources of the same nature.
Two-phase image segmentation by the Allen-Cahn equation and a nonlocal edge detection operator

Based on a nonlocal Laplacian operator, a novel edge detection method of the grayscale image is proposed in this paper. This operator utilizes the information of neighbor pixels for a given pixel to obtain effective and delicate edge detection. The nonlocal edge detection method is used as an initialization for solving the Allen-Cahn equation to achieve two-phase segmentation of the grayscale image. Efficient exponential time differencing (ETD) solvers are employed in the time integration, and finite difference method is adopted in space discretization. The maximum bound principle and energy stability of the proposed numerical schemes are proved. The capability of our segmentation method has been verified in numerical experiments for different types of grayscale images.
On the existence of the Brillouin peaks in a simple dilute dissipative gas

Light scattering due to the interaction of photons and acoustic waves present in a dilute inert gas is analyzed through the use of irreversible thermodynamics. The dispersion relation, which governs the dynamics of the density uctuation of the gas allows the establishment of a simple criterion for the corresponding Rayleigh-Brillouin spectrum to be observed. The criterion here proposed allows a clear physical interpretation and suggests generalizations for other interesting physical scenarios.
Drowned out by the noise: Evidence for Tracking-free Motion Prediction

Autonomous driving consists of a multitude of interacting modules, where each module must contend with errors from the others. Typically, the motion prediction module depends on a robust tracking system to capture each agent’s past movement. In this work, we systematically explore the importance of the tracking module for the motion prediction task and ultimately conclude that the tracking module is detrimental to overall motion prediction performance when the module is imperfect (with as low as 1% error). We explicitly compare models that use tracking information to models that do not across multiple scenarios and conditions. We ﬁnd that the tracking information only improves performance in noise-free conditions. A noise-free tracker is unlikely to remain noise-free in real-world scenarios, and the inevitable noise will subsequently negatively affect performance. We thus argue future work should be mindful of noise when developing and testing motion/tracking modules, or that they should do away with the tracking component entirely.