Miracle-dz/UniFAR-FTUs huggingface dataset:
A Benchmark for Temporal Color Constancy

Temporal Color Constancy (CC) is a recently proposed approach that challenges the conventional single-frame color constancy. The conventional approach is to use a single frame - shot frame - to estimate the scene illumination color. In temporal CC, multiple frames from the view finder sequence are used to estimate the color. However, there are no realistic large scale temporal color constancy datasets for method evaluation. In this work, a new temporal CC benchmark is introduced. The benchmark comprises of (1) 600 real-world sequences recorded with a high-resolution mobile phone camera, (2) a fixed train-test split which ensures consistent evaluation, and (3) a baseline method which achieves high accuracy in the new benchmark and the dataset used in previous works. Results for more than 20 well-known color constancy methods including the recent state-of-the-arts are reported in our experiments.
PCA Based English Handwritten Digit Recognition

In this paper, a digit Recognition system is designed using the Principal Component Analysis (PCA), a method of extraction of characteristics based on the digit forms, combined with k-Nearest Neighbor to recognize the numeral digits, this approach is tested on the MNIST handwritten isolated digit database. This proposed method shows an excellent performance with higher accuracy, Achieved approximately 86.5%.
Geometry of First-Order Methods and Adaptive Acceleration.

First-order operator splitting methods are ubiquitous among many fields through science and engineering, such as inverse problems, signal/image processing, statistics, data science and machine learning, to name a few. In this paper, we study a geometric property of first-order methods when applying to solve non-smooth optimization problems. With the tool of "partial smoothness", we design a framework to analyze the trajectory of the fixed-point sequence generated by first-order methods and show that locally, the fixed-point sequence settles onto a regular trajectory such as a straight line or a spiral. Based on this finding, we discuss the limitation of current widely used "inertial acceleration" technique, and propose a trajectory following adaptive acceleration algorithm. Global convergence is established for the proposed acceleration scheme based on the perturbation of fixed-point iteration. Locally, we first build connections between the acceleration scheme and the well-studied "vector extrapolation technique" in the field of numerical analysis, and then discuss local acceleration guarantees of the proposed acceleration scheme. Moreover, our result provides a geometric interpretation of these vector extrapolation techniques. Numerical experiments on various first-order methods are provided to demonstrate the advantage of the proposed adaptive acceleration scheme.
Unifying Specialist Image Embedding into Universal Image Embedding

Deep image embedding provides a way to measure the semantic similarity of two images. It plays a central role in many applications such as image search, face verification, and zero-shot learning. It is desirable to have a universal deep embedding model applicable to various domains of images. However, existing methods mainly rely on training specialist embedding models each of which is applicable to images from a single domain. In this paper, we study an important but unexplored task: how to train a single universal image embedding model to match the performance of several specialists on each specialist's domain. Simply fusing the training data from multiple domains cannot solve this problem because some domains become overfitted sooner when trained together using existing methods. Therefore, we propose to distill the knowledge in multiple specialists into a universal embedding to solve this problem. In contrast to existing embedding distillation methods that distill the absolute distances between images, we transform the absolute distances between images into a probabilistic distribution and minimize the KL-divergence between the distributions of the specialists and the universal embedding. Using several public datasets, we validate that our proposed method accomplishes the goal of universal image embedding.
A Tracking System For Baseball Game Reconstruction

The baseball game is often seen as many contests that are performed between individuals. The duel between the pitcher and the batter, for example, is considered the engine that drives the sport. The pitchers use a variety of strategies to gain competitive advantage against the batter, who does his best to figure out the ball trajectory and react in time for a hit. In this work, we propose a system that captures the movements of the pitcher, the batter, and the ball in a high level of detail, and discuss several ways how this information may be processed to compute interesting statistics. We demonstrate on a large database of videos that our methods achieve comparable results as previous systems, while operating solely on video material. In addition, state-of-the-art AI techniques are incorporated to augment the amount of information that is made available for players, coaches, teams, and fans.
ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification

Emotion cause analysis such as emotion cause extraction (ECE) and emotion-cause pair extraction (ECPE) have gradually attracted the attention of many researchers. However, there are still two shortcomings in the existing research: 1) In most cases, emotion expression and cause are not the whole clause, but the span in the clause, so extracting the clause-pair rather than the span-pair greatly limits its applications in real-world scenarios; 2) It is not enough to extract the emotion expression clause without identifying the emotion categories, the presence of emotion clause does not necessarily convey emotional information explicitly due to different possible causes. In this paper, we propose a new task: Emotion-Cause Span-Pair extraction and classification (ECSP), which aims to extract the potential span-pair of emotion and corresponding causes in a document, and make emotion classification for each pair. In the new ECSP task, ECE and ECPE can be regarded as two special cases at the clause-level. We propose a span-based extract-then-classify (ETC) model, where emotion and cause are directly extracted and paired from the document under the supervision of target span boundaries, and corresponding categories are then classified using their pair representations and localized context. Experiments show that our proposed ETC model outperforms the SOTA model of ECE and ECPE task respectively and gets a fair-enough results on ECSP task.
Quantum tunneling rate of dilute axion stars close to the maximum mass

We compute the quantum tunneling rate of dilute axion stars close to the maximum mass [P.H. Chavanis, Phys. Rev. D {\bf 84}, 043531 (2011)] using the theory of instantons. We confirm that the lifetime of metastable states is extremely long, scaling as $t_{\rm life}\sim e^N\, t_D$ (except close to the critical point), where $N$ is the number of axions in the system and $t_D$ is the dynamical time ($N\sim 10^{57}$ and $t_D\sim 10\, {\rm hrs}$ for typical QCD axion stars; $N\sim 10^{96}$ and $t_D\sim 100\, {\rm Myrs}$ for the quantum core of a dark matter halo made of ultralight axions). Therefore, metastable equilibrium states can be considered as stable equilibrium states in practice. We develop a finite size scaling theory close to the maximum mass and predict that the collapse time at criticality scales as $t_{\rm coll}\sim N^{1/5}t_D$ instead of being infinite as when fluctuations are neglected. The collapse time is smaller than the age of the universe for QCD axion stars and larger than the age of the universe for dark matter cores made of ultralight axions. We also consider the thermal tunneling rate and reach the same conclusions. We compare our results with similar results obtained for Bose-Einstein condensates in laboratory, globular clusters in astrophysics, and quantum field theory in the early Universe.
Manifold lifting: scaling MCMC to the vanishing noise regime

Standard Markov chain Monte Carlo methods struggle to explore distributions that are concentrated in the neighbourhood of low-dimensional structures. These pathologies naturally occur in a number of situations. For example, they are common to Bayesian inverse problem modelling and Bayesian neural networks, when observational data are highly informative, or when a subset of the statistical parameters of interest are non-identifiable. In this paper, we propose a strategy that transforms the original sampling problem into the task of exploring a distribution supported on a manifold embedded in a higher dimensional space; in contrast to the original posterior this lifted distribution remains diffuse in the vanishing noise limit. We employ a constrained Hamiltonian Monte Carlo method which exploits the manifold geometry of this lifted distribution, to perform efficient approximate inference. We demonstrate in several numerical experiments that, contrarily to competing approaches, the sampling efficiency of our proposed methodology does not degenerate as the target distribution to be explored concentrates near low dimensional structures.
Pseudo-Convolutional Policy Gradient for Sequence-to-Sequence Lip-Reading

Lip-reading aims to infer the speech content from the lip movement sequence and can be seen as a typical sequence-to-sequence (seq2seq) problem which translates the input image sequence of lip movements to the text sequence of the speech content. However, the traditional learning process of seq2seq models always suffers from two problems: the exposure bias resulted from the strategy of “teacher-forcing”, and the inconsistency between the discriminative optimization target (usually the cross-entropy loss) and the final evaluation metric (usually the character/word error rate). In this paper, we propose a novel pseudo-convolutional policy gradient (PCPG) based method to address these two problems. On the one hand, we introduce the evaluation metric (refers to the character error rate in this paper) as a form of reward to optimize the model together with the original discriminative target. On the other hand, inspired by the local perception property of convolutional operation, we perform a pseudo-convolutional operation on the reward and loss dimension, so as to take more context around each time step into account to generate a robust reward and loss for the whole optimization. Finally, we perform a thorough comparison and evaluation on both the word-level and sentence-level benchmarks. The results show a significant improvement over other related methods, and report either a new state-of-the-art performance or a competitive accuracy on all these challenging benchmarks, which clearly proves the advantages of our approach.
SpellBound: Defending Against Package Typosquatting

Package managers for software repositories based on a single programming language are very common. Examples include npm (JavaScript), and PyPI (Python). These tools encourage code reuse, making it trivial for developers to import external packages. Unfortunately, repositories' size and the ease with which packages can be published facilitates the practice of typosquatting: the uploading of a package with name similar to that of a highly popular package, typically with the aim of capturing some of the popular package's installs. Typosquatting has serious negative implications, resulting in developers importing malicious packages, or -- as we show -- code clones which do not incorporate recent security updates. In order to tackle this problem, we present SpellBound, a tool for identifying and reporting potentially erroneous imports to developers. SpellBound implements a novel typosquatting detection technique, based on an in-depth analysis of npm and PyPI. Our technique leverages a model of lexical similarity between names, and further incorporates the notion of package popularity. This approach flags cases where unknown/scarcely used packages would be installed in place of popular ones with similar names, before installation occurs. We evaluated SpellBound on both npm and PyPI, with encouraging results: SpellBound flags typosquatting cases while generating limited warnings (0.5% of total package installs), and low overhead (only 2.5% of package install time). Furthermore, SpellBound allowed us to confirm known cases of typosquatting and discover one high-profile, unknown case of typosquatting that resulted in a package takedown by the npm security team.
Fabrication and characterization of Fused Deposition Modeling 3D printed mm-scaled metasurface units.

We present a cost-effective, eco-friendly and accessible method for fabricating three-dimensional, ultralight and flexible millimeter-scale metasurfaces using a household 3D printer. In particular, we fabricate conductive Spilt Ring Resonators (SRRs) in a free-standing form, employing the so-called Fused Deposition Modeling 3D printing technique. We experimentally characterize the samples through transmission measurements in standard rectangular waveguide configurations. The structures exhibit well defined resonant features dependent on the geometrical parameters and the infiltrating dielectric materials. The demonstrated 3D printed components are suitable for practical real-life applications while the method holds the additional advantage of the ecological approach, the low cost, the flexibility and the small weight of the components. Thus, the flexible and light 3D printed metasurfaces may serve as electromagnetic components and fabrics for coating a plethora of devices and infrastructure units of different shapes and size. \end{abstract}
Ultrafast dynamical Lifshitz transition

We demonstate a non-equilibrium route for ultrafast modification of Fermi surface topology in quantum materials. Fermi surface is at the heart of our understanding of metals and strongly correlated many-body systems. An abrupt change in the Fermi surface topology, also called Lifshitz transition, can lead to the emergence of fascinating phenomena like colossal magnetoresistance and superconductivity. While Lifshitz transitions have been demonstrated for a broad range of materials by equilibrium tuning of macroscopic parameters such as strain, doping, pressure, and temperature, a nonequilibrium dynamical route toward ultrafast modification of the Fermi surface topology has not been experimentally demonstrated. Combining time-resolved multidimensional photoemission spectroscopy with state-of-the-art TDDFT+U simulations, we introduce a scheme for driving an ultrafast Lifshitz transition in the correlated type-II Weyl semimetal Td-MoTe2. We demonstrate that this nonequilibrium topological electronic transition finds its microscopic origin in the dynamical modification of the effective electronic correlations. These results shed light on a previously unexplored ultrafast scheme for controlling the Fermi surface topology in correlated quantum materials.
Zooming for Efficient Model-Free Reinforcement Learning in Metric Spaces

Despite the wealth of research into provably efficient reinforcement learning algorithms, most works focus on tabular representation and thus struggle to handle exponentially or infinitely large state-action spaces. In this paper, we consider episodic reinforcement learning with a continuous state-action space which is assumed to be equipped with a natural metric that characterizes the proximity between different states and actions. We propose ZoomRL, an online algorithm that leverages ideas from continuous bandits to learn an adaptive discretization of the joint space by zooming in more promising and frequently visited regions while carefully balancing the exploitation-exploration trade-off. We show that ZoomRL achieves a worst-case regret $\tilde{O}(H^{\frac{5}{2}} K^{\frac{d+1}{d+2}})$ where $H$ is the planning horizon, $K$ is the number of episodes and $d$ is the covering dimension of the space with respect to the metric. Moreover, our algorithm enjoys improved metric-dependent guarantees that reflect the geometry of the underlying space. Finally, we show that our algorithm is robust to small misspecification errors.
Accurate mass estimates from the proper motions of dispersion-supported galaxies

We derive a new mass estimator that relies on internal proper motion measurements of dispersion-supported stellar systems, one that is distinct and complementary to existing estimators for line-of-sight velocities. Starting with the spherical Jeans equation, we show that there exists a radius where the mass enclosed depends only on the projected tangential velocity dispersion, assuming that the anisotropy profile slowly varies. This is well-approximated at the radius where the log-slope of the stellar tracer profile is −2: r−2. The associated mass is $M(r_{-2}) = 2 G^{-1} \langle \sigma _{\mathcal {T}}^{2}\rangle ^{*} r_{-2}$ and the circular velocity is $V^{2}({r_{-2}}) = 2\langle \sigma _{\mathcal {T}}^{2}\rangle ^{*}$. For a Plummer profile r−2 ≃ 4Re/5. Importantly, r−2 is smaller than the characteristic radius for line-of-sight velocities derived by Wolf et al. Together, the two estimators can constrain the mass profiles of dispersion-supported galaxies. We illustrate its applicability using published proper motion measurements of dwarf galaxies Draco and Sculptor, and find that they are consistent with inhabiting cuspy NFW subhaloes of the kind predicted in CDM but we cannot rule out a core. We test our combined mass estimators against previously published, non-spherical cosmological dwarf galaxy simulations done in both cold dark matter (CDM; naturally cuspy profile) and self-interacting dark matter (SIDM; cored profile). For CDM, the estimates for the dynamic rotation curves are found to be accurate to $10\rm { per\, cent}$ while SIDM are accurate to $15\rm { per\, cent}$. Unfortunately, this level of accuracy is not good enough to measure slopes at the level required to distinguish between cusps and cores of the type predicted in viable SIDM models without stronger priors. However, we find that this provides good enough accuracy to distinguish between the normalization differences predicted at small radii (r ≃ r−2 < rcore) for interesting SIDM models. As the number of galaxies with internal proper motions increases, mass estimators of this kind will enable valuable constraints on SIDM and CDM models.
General Conditions to Realize Exceptional Points of Degeneracy in Two Uniform Coupled Transmission Lines

We present the general conditions to realize a fourth-order exceptional point of degeneracy (EPD) in two <italic>uniform</italic> (i.e., invariant along <inline-formula> <tex-math notation="LaTeX">$z$ </tex-math></inline-formula>) lossless and gainless coupled transmission lines (CTLs), namely, a degenerate band edge (DBE). Until now the DBE has been shown only in periodic structures. In contrast, the CTLs considered here are uniform and subdivided into four cases where the two TLs support combinations of forward propagation, backward propagation, and evanescent modes (when neglecting the mutual coupling). We demonstrate, for the first time, that a DBE is supported in <italic>uniform</italic> CTLs when there is proper coupling between: 1) propagating modes and evanescent modes, 2) forward and backward propagating modes, or 3) four evanescent modes (two in each direction). We also show that the loaded quality factor of <italic>uniform</italic> CTLs exhibiting a fourth-order EPD at <inline-formula> <tex-math notation="LaTeX">$k=0$ </tex-math></inline-formula> is robust to series losses due to the fact that the degenerate modes do not advance in phase. We also provide a microstrip possible implementation of a uniform CTL exhibiting a DBE using periodic series capacitors with very subwavelength unit-cell length. Finally, we show an experimental verification of the existence DBE for a microstrip implementation of a CTL supporting coupled propagating and evanescent modes.
Non-contact Monitoring of Respiration and Heart Activity of Infants Using UWB Signals

The main purpose of this contribution is to introduce a new system which is able to simultaneously to monitor the breathing and heart activity of an infant. The working frequency band of the system is 1—8 GHz. Three bow-tie antennas (1 transmitter Tx and 2 receivers Rx) are placed over the neonatal. The antennas are placed 60 cm above the infant. In case of measurement the Tx antenna is emitting the UWB signal, which is reflected from the body and detected by the Rx antennas. The static background is subtracted by the signal mean value. The detection principle is based on the knowledge that the respiratory frequency of an infant (1—3 years old) is around 1 Hz and the movement of the chest is about 0.2 mm. This can be detected via UWB radar as peak detection and its movement caused by breathing. In case of infants' apnea, we propose a dynamic threshold. From the signal it is also possible to detect the heart activity.
Predictive Guidance Scheme with NAR Neural Network for Autonomous Aerial Refueling

In order to improve the passive tracking situation of receiver aircraft for drogue during the docking phase of AAR (autonomous aerial refueling), a strategy of AAR predictive guidance scheme based on NAR (Nonlinear Auto Regressive) neural network model is proposed. The NAR neural network model is used to predict the future position of the drogue as the target point of receiver aircraft. The simulation is carried out by Monte Carlo target test. The simulation results indicate that the proposed algorithm possesses high prediction precision and significantly enhances the success rate of AAR, which is of crucial importance in achievement of AAR technique.
An Improved Particle Filter Algorithm for Automated Aerial Refueling Based on Meanshift Clustering

Traditional particle filter algorithm requires a number of particle samples for the accuracy of tracking with high computational complexity, which does not satisfy the real-time requirements. In addition, the target is easily to lose under the occlusion condition when the particle tracing algorithm is applied. A target tracking algorithm of anti-blocking particle filter is proposed in this paper. On the basis of clustering, a target probability density estimation method is proposed to describe the target model, and the target can be tracked with less particles. Kalman filter is chosen to determine the target location when the target is blocked seriously. Experiments show that when the algorithm is employed the robustness and tracking accuracy are much better and the real-time requirement is met.
Fiber-optic Temperature Sensing Scheme by Using Stimulated Brillouin Scattering Effect in a Recirculating Fiber Ring

We proposed and experimentally demonstrated a novel sensing scheme by using high order stimulated Brillouin scattering effect in a recirculating fiber ring to improve the sensitivity Temperature sensing using this scheme has been implemented in the experiment for demonstration. By measuring the frequency shift of the beat signal, temperature variation can be monitored. The sensitivity of monitoring the second-order beat signal is estimated to ~2.2 MHz/° in the experiment, which is twice of that of first order, and by using higher-order beat signal, higher sensitivity can be achieved.