Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of the ratio of variances in distance‐based and classical multivariate analysis

In multivariate analysis, the measure of variance accounted for plays a central role. In this paper, we show that an alternative approach, distance‐based multivariate analysis, also yields solutions that can be summarized by a ratio of variances. For classical multivariate analysis, this ratio is equal to the variance accounted for (VAF) and in distance‐based multivariate analysis it equals distance accounted for (DAF). We show that DAF in distance‐based multivariate analysis can always be made higher than VAF in classical multivariate analysis. This property is illustrated for principal components analysis, multiple correspondence analysis, multiple regression, and analysis of variance.
DAOPHOT: A COMPUTER PROGRAM FOR CROWDED-FIELD STELLAR PHOTOMETRY

The tasks of the DAOPHOT program, developed to exploit the capability of photometrically linear image detectors to perform stellar photometry in crowded fields, are discussed. Raw CCD images are prepared prior to analysis, and following the obtaining of an initial star list with the FIND program, synthetic aperture photometry is performed on the detected objects with the PHOT routine. A local sky brightness and a magnitude are computed for each star in each of the specified stellar apertures, and for crowded fields, the empirical point-spread function must then be obtained for each data frame. The GROUP routine divides the star list for a given frame into optimum subgroups, and then the NSTAR routine is used to obtain photometry for all the stars in the frame by means of least- squares profile fits. The process is illustrated with images of stars in a crowded field, and shortcomings and possible improvements of the program are considered.
Dissimilar control of momentum and heat transfer in a fully developed turbulent channel flow

Abstract A wide range of applicability of the Reynolds analogy between turbulent momentum and heat transport implies inherent difficulty in diminishing or enhancing skin friction and heat transfer independently. In the present study, we introduce suboptimal control theory for achieving a dissimilar control of enhancing heat transfer, while keeping the skin friction not increased considerably in a fully developed channel flow. The Fréchet differentials clearly show that the responses of velocity and temperature fields to wall blowing/suction are quite different, due to the fact that the velocity is a divergence-free vector field while the temperature is a conservative scalar field. This essential difference allows us to achieve dissimilar control even in flows where the averaged momentum and energy transport equations have an identical form. It is also found that the resultant optimized mode of control input exhibits a streamwise travelling-wave-like property. By exploring the phase relationship between the travelling-wave-like control input and the velocity and thermal fields, we reveal that such control input contributes to dissimilar heat transfer enhancement via two different mechanisms, i.e. direct modification of the coherent components of the Reynolds shear stress and the turbulent heat flux, and indirect effects on the incoherent components, through modification of the mean velocity and temperature profiles. Based on these results, a simple open-loop strategy for dissimilar control is proposed and assessed.
Strain relaxation of GaAs/Ge crystals on patterned Si substrates

We report on the mask-less integration of GaAs crystals several microns in size on patterned Si substrates by metal organic vapor phase epitaxy. The lattice parameter mismatch is bridged by first growing 2-μm-tall intermediate Ge mesas on 8-μm-tall Si pillars by low-energy plasma enhanced chemical vapor deposition. We investigate the morphological evolution of the GaAs crystals towards full pyramids exhibiting energetically stable {111} facets with decreasing Si pillar size. The release of the strain induced by the mismatch of thermal expansion coefficients in the GaAs crystals has been studied by X-ray diffraction and photoluminescence measurements. The strain release mechanism is discussed within the framework of linear elasticity theory by Finite Element Method simulations, based on realistic geometries extracted from scanning electron microscopy images.
Mechanistic Explanation in Systems Biology: Cellular Networks

It is argued that once biological systems reach a certain level of complexity, mechanistic explanations provide an inadequate account of many relevant phenomena. In this article, I evaluate such claims with respect to a representative programme in systems biological research: the study of regulatory networks within single-celled organisms. I argue that these networks are amenable to mechanistic philosophy without need to appeal to some alternate form of explanation. In particular, I claim that we can understand the mathematical modelling techniques of systems biologists as part of a broader practice of constructing and evaluating mechanism schemas. This argument is elaborated by considering the case of bacterial chemotactic networks, where some research has been interpreted as explaining phenomena by means of abstract design principles. 1 Introduction 2 The Strategies of Mechanistic Science in the Study of Cellular Networks 3 Are Mechanisms Insufficient? 4 Design Explanations and Bacterial Chemotaxis 5 Conclusion 1 Introduction 2 The Strategies of Mechanistic Science in the Study of Cellular Networks 3 Are Mechanisms Insufficient? 4 Design Explanations and Bacterial Chemotaxis 5 Conclusion
Experimental Investigation of Solar-Driven Hollow Fiber Membrane Liquid Dehumidification System

The hollow fiber membrane modules act as dehumidifiers and regenerators to avoid gas–liquid entrainment problems in direct-contact dehumidification systems. A solar-driven hollow fiber membrane dehumidification experimental rig was designed to investigate its performance from July to September in Guilin, China. The dehumidification, regeneration, and cooling performance of the system between 8:30 and 17:30 are analyzed. The energy utilization of the solar collector and system is investigated. The results show that solar radiation has a significant influence on the system. The hourly regeneration of the system has the same trend as the temperature of solar hot water, which ranges from 0.13 g/s to 0.36 g/s. The regeneration capacity of the dehumidification system is always larger than the dehumidification capacity after 10:30, which increases the solution concentration and the dehumidification performance. Further, it ensures stable system operation when the solar radiation is lower (15:30–17:50). In addition, the hourly dehumidification capacity and efficiency of the system ranges from 0.15 g/s to 0.23 g/s and 52.4 to 71.3%, respectively, with good dehumidification performance. The COP of the system and solar collector have the same trend, in which their maximum values are 0.874 and 0.634, respectively, with high energy utilization efficiency. The solar-driven hollow fiber membrane liquid dehumidification system performs better in regions with larger solar radiation.
Switching Pushing Skill Combined MPC and Deep Reinforcement Learning for Planar Non-prehensile Manipulation

In this paper, a novel switching pushing skill algorithm is proposed to improve the efficiency of planar non-prehensile manipulation, which draws inspiration from human pushing actions and comprises two sub-problems, i.e., discrete decision-making of pushing point and continuous feedback control of pushing action. In order to solve the sub-problems above, a combination of Model Predictive Control (MPC) and Deep Reinforcement Learning (DRL) method is employed. Firstly, the selection of pushing point is modeled as a Markov decision process,and an off-policy DRL method is used by reshaping the reward function to train the decision-making model for selecting pushing point from a pre-constructed set based on the current state. Secondly, a motion constraint region (MCR) is constructed for the specific pushing point based on the distance from the target, followed by utilizing the MPC controller to regulate the motion of the object within the MCR towards the target pose. The trigger condition for switching the pushing point occurs when the object reaches the boundary of the MCR under the pushing action. Subsequently, the pushing point and the controller are updated iteratively until the target pose is reached. We conducted pushing experiments on four distinct object shapes in both simulated and physical environments to evaluate our method. The results indicate that our method achieves a significantly higher training efficiency, with a training time that is only about 20% of the baseline method while maintaining around the same success rate. Moreover, our method outperforms the baseline method in terms of both training and execution efficiency of pushing operations, allowing for rapid learning of robot pushing skills.
Audio-Visual Grouping Network for Sound Localization from Mixtures

Sound source localization is a typical and challenging task that predicts the location of sound sources in a video. Previous single-source methods mainly used the audio-visual association as clues to localize sounding objects in each image. Due to the mixed property of multiple sound sources in the original space, there exist rare multi-source approaches to localizing multiple sources simultaneously, except for one recent work using a contrastive random walk in the graph with images and separated sound as nodes. Despite their promising performance, they can only handle a fixed number of sources, and they cannot learn compact class-aware representations for individual sources. To alleviate this shortcoming, in this paper, we propose a novel audio-visual grouping network, namely AVGN, that can directly learn category-wise semantic features for each source from the input audio mixture and image to localize multiple sources simultaneously. Specifically, our AVGN leverages learnable audio-visual class tokens to aggregate class-aware source features. Then, the aggregated semantic features for each source can be used as guidance to localize the corresponding visual regions. Compared to existing multi-source methods, our new framework can localize a flexible number of sources and disentangle category-aware audio-visual representations for individual sound sources. We conduct extensive experiments on MUSIC, VGGSound-Instruments, and VGG-Sound Sources benchmarks. The results demonstrate that the proposed AVGN can achieve state-of-the-art sounding object localization performance on both single-source and multi-source scenarios. Code is available at https://github.com/stoneMo/AVGN.
Stability investigations of isotropic and anisotropic exponential inflation in the Starobinsky-Bel-Robinson gravity

In this paper, we would like to examine whether a novel Starobinsky-Bel-Robinson gravity model admits stable exponential inflationary solutions with or without spatial anisotropies. As a result, we are able to derive an exact de Sitter inflationary to this Starobinsky-Bel-Robinson model. Furthermore, we observe that an exact Bianchi type I inflationary solution does not exist in the Starobinsky-Bel-Robinson model. However, we find that a modified Starobinsky-Bel-Robinson model, in which the sign of coefficient of $R^2$ term is flipped from positive to negative, can admit the corresponding Bianchi type I inflationary solution. Unfortunately, stability analysis using the dynamical system approach indicates that both of these inflationary solutions turn out to be unstable. Interestingly, we show that a stable de Sitter inflationary solution can be obtained in the modified Starobinsky-Bel-Robinson gravity.
DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving

In the realm of autonomous driving, real-time perception or streaming perception remains under-explored. This research introduces DAMO-StreamNet, a novel framework that merges the cutting-edge elements of the YOLO series with a detailed examination of spatial and temporal perception techniques. DAMO-StreamNet's main inventions include: (1) a robust neck structure employing deformable convolution, bolstering receptive field and feature alignment capabilities; (2) a dual-branch structure synthesizing short-path semantic features and long-path temporal features, enhancing the accuracy of motion state prediction; (3) logits-level distillation facilitating efficient optimization, which aligns the logits of teacher and student networks in semantic space; and (4) a real-time prediction mechanism that updates the features of support frames with the current frame, providing smooth streaming perception during inference. Our testing shows that DAMO-StreamNet surpasses current state-of-the-art methodologies, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200, 1920)) sAP without requiring additional data. This study not only establishes a new standard for real-time perception but also offers valuable insights for future research. The source code is at https://github.com/zhiqic/DAMO-StreamNet.
Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud Normal Estimation

Most existing approaches for point cloud normal estimation aim to locally fit a geometric surface and calculate the normal from the fitted surface. Recently, learning-based methods have adopted a routine of predicting pointwise weights to solve the weighted least-squares surface fitting problem. Despite achieving remarkable progress, these methods overlook the approximation error of the fitting problem, resulting in a less accurate fitted surface. In this paper, we first carry out in-depth analysis of the approximation error in the surface fitting problem. Then, in order to bridge the gap between estimated and precise surface normals, we present two basic design principles: 1) applies the Z-direction Transform to rotate local patches for a better surface fitting with a lower approximation error; 2) models the error of the normal estimation as a learnable term. We implement these two principles using deep neural networks, and integrate them with the state-of-the-art (SOTA) normal estimation methods in a plug-and-play manner. Extensive experiments verify our approaches bring benefits to point cloud normal estimation and push the frontier of state-of-the-art performance on both synthetic and real-world datasets. The code is available at https://github.com/hikvision-research/3DVision.
TorKameleon: Improving Tor's Censorship Resistance With K-anonimization and Media-based Covert Channels

Anonymity networks like Tor significantly enhance online privacy but are vulnerable to correlation attacks by state-level adversaries. While covert channels encapsulated in media protocols, particularly WebRTC-based encapsulation, have demonstrated effectiveness against passive traffic correlation attacks, their resilience against active correlation attacks remains unexplored, and their compatibility with Tor has been limited. This paper introduces TorKameleon, a censorship evasion solution designed to protect Tor users from both passive and active correlation attacks. TorKameleon employs K-anonymization techniques to fragment and reroute traffic through multiple TorKameleon proxies, while also utilizing covert WebRTC-based channels or TLS tunnels to encapsulate user traffic.
Hydrodynamics of superfluid helium below 0.6 $sup 0$K. II. Velocity and attenuation of ultrasonic waves

The attenuation and velocity of an ultrasonic wave in superfluid helium- 4 have been calculated for temperatures below 0.6 deg K. The calculation is based on the kinetic equations given by Khalatnikov. The phonon Boltzmann equation is solved numerically without making simplifying approximations about the form of the collision integral. The theory predicts that the velocity of sound increseses with frequency for very low frequencies, passes through a maximum, decreases to a minimum, and finally incresses towards a constant value at high frequencies. A simple explanation for this peculiar behavior is given. The theory is in good agreement with the experimental results of Abrashams et al., and Waters et al. (auth)
Spin and orbital angular momentum of coherent photons in a waveguide

Spin angular momentum of a photon corresponds to a polarisation degree of freedom of lights, and such that various polarisation properties are coming from macroscopic manifestation of quantum-mechanical properties of lights. An orbital degree of freedom of lights is also manipulated to form a vortex of lights with orbital angular momentum, which is also quantised. However, it is considered that spin and orbital angular momentum of a photon cannot be split from the total orbital angular momentum in a gauge-invariant way. Here, we revisit this issue for a coherent monochromatic ray from a laser source, propagating in a waveguide. We obtained the helical components of spin and orbital angular momentum by the correspondence with the classical Ponyting vector. By applying a standard quantum field theory using a coherent state, we obtained the gauge-independent expressions of spin and orbital angular momentum operators. During the derivations, it was essential to take a finite cross-sectional area into account, which leads the finite longitudinal component along the direction of the propagation, which allows the splitting. Therefore, the finite mode profile was responsible to justify the splitting, which was not possible as far as we were using plane-wave expansions in a standard theory of quantum-electrodynamics (QED). Our results suggest spin and orbital angular momentum are well-defined quantum-mechanical freedoms at least for coherent photons propagating in a waveguide and in a vacuum with a finite mode profile.
KOO approach for scalable variable selection problem in large-dimensional regression

An important issue in many multivariate regression problems is to eliminate candidate predictors with null predictor vectors. In large-dimensional (LD) setting where the numbers of responses and predictors are large, model selection encounters the scalability challenge. Knock-one-out (KOO) statistics hold promise to meet this challenge. In this paper, the almost sure limits and the central limit theorem of the KOO statistics are derived under the LD setting and mild distributional assumptions (finite fourth moments) of the errors. These theoretical results guarantee the strong consistency of a subset selection rule based on the KOO statistics with a general threshold. For enhancing the robustness of the selection rule, we also propose a bootstrap threshold for the KOO approach. Simulation results support our conclusions and demonstrate the selection probabilities by the KOO approach with the bootstrap threshold outperform the methods using Akaike information threshold, Bayesian information threshold and Mallow's C$_p$ threshold. We compare the proposed KOO approach with those based on information threshold to a chemometrics dataset and a yeast cell-cycle dataset, which suggests our proposed method identifies useful models.
Complementary Random Masking for RGB-Thermal Semantic Segmentation

RGB-thermal semantic segmentation is one potential solution to achieve reliable semantic scene understanding in adverse weather and lighting conditions. However, the previous studies mostly focus on designing a multi-modal fusion module without consideration of the nature of multi-modality inputs. Therefore, the networks easily become over-reliant on a single modality, making it difficult to learn complementary and meaningful representations for each modality. This paper proposes 1) a complementary random masking strategy of RGB-T images and 2) self-distillation loss between clean and masked input modalities. The proposed masking strategy prevents over-reliance on a single modality. It also improves the accuracy and robustness of the neural network by forcing the network to segment and classify objects even when one modality is partially available. Also, the proposed self-distillation loss encourages the network to extract complementary and meaningful representations from a single modality or complementary masked modalities. Based on the proposed method, we achieve state-of-the-art performance over three RGB-T semantic segmentation benchmarks. Our source code is available at https://github.com/UkcheolShin/CRM_RGBTSeg.
Lateral quantum confinement effect on monolayer high-Tc superconductors

Despite decades of research in spatially confined superconducting systems to understand the modification of superconductivity from reduced length scales, the investigation of the quantum confinement effect on high-temperature superconductors remains an outstanding challenge. Here, we report scanning tunneling spectroscopy measurements on laterally confined FeSe monolayers on SrTiO3 substrates, which are formed by epitaxially growing FeSe films with a coverage less than one unit cell. Comparing to the uniform regions of FeSe monolayers, the peninsula regions at the monolayer boundary exhibit reduced Fermi energy and undiminished superconductivity, leading to a putative crossover from a Bardeen-Cooper-Schrieffer state to a Bose-Einstein condensate state. In isolated FeSe monolayer islands, superconductivity is shown to exist in samples of smaller volume in contrast to conventional superconductors, while the validity of Anderson's criterion remains fulfilled. Our work reveals lateral quantum confinement effects in unconventional superconductors, to enrich the understanding of high-temperature superconductivity in low-dimensional systems.
Regression and Classification Methods for Learning Sound Wave Amplitude Modulation in Soft Tactile Sensing

—This paper presents a novel soft tactile skin (STS) technology operating with sound waves. In this innovative approach, the sound waves generated by a speaker travel in channels embedded in a soft membrane and get modulated due to a deformation of the channel when pressed by an external force and received by a microphone at the end of the channel. The sensor leverages regression and classification methods for estimating the normal force and its contact location. Our sensor can be affixed to any robot part, e.g., end effectors or arm. We tested several regression and classifier methods to learn the relation between sound wave modulation, the applied force, and its location, respectively and picked the best-performing models for force and location predictions. Our novel tactile sensor yields 93% of the force estimation within ±1.5 N tolerances for a range of 0-30 +1 N and estimates contact locations with over 96% accuracy. We also demonstrated the performance of STS technology for a real-time gripping force control application.
Online Learning and Disambiguations of Partial Concept Classes

In a recent article, Alon, Hanneke, Holzman, and Moran (FOCS '21) introduced a unifying framework to study the learnability of classes of partial concepts. One of the central questions studied in their work is whether the learnability of a partial concept class is always inherited from the learnability of some ``extension'' of it to a total concept class. They showed this is not the case for PAC learning but left the problem open for the stronger notion of online learnability. We resolve this problem by constructing a class of partial concepts that is online learnable, but no extension of it to a class of total concepts is online learnable (or even PAC learnable).