Miracle-dz/UniFAR-FTUs huggingface dataset:
Facilitating Contrastive Learning of Discourse Relational Senses by Exploiting the Hierarchy of Sense Relations

Implicit discourse relation recognition is a challenging task that involves identifying the sense or senses that hold between two adjacent spans of text, in the absense of an explicit connective between them. In both PDTB-2 (prasad et al., 2008) and PDTB-3 (Webber et al., 2019), discourse relational senses are organized into a three-level hierarchy ranging from four broad top-level senses, to more specific senses below them. Most previous work on implicitf discourse relation recognition have used the sense hierarchy simply to indicate what sense labels were available. Here we do more — incorporating the sense hierarchy into the recognition process itself and using it to select the negative examples used in contrastive learning. With no additional effort, the approach achieves state-of-the-art performance on the task. Our code is released inhttps://github.com/wanqiulong 0923/Contrastive_IDRR.
The UNCOVER Survey: A first-look HST+JWST catalog of 60,000 galaxies near Abell 2744 and beyond

In November 2022, the James Webb Space Telescope (JWST) returned deep near-infrared images of Abell 2744 -- a powerful lensing cluster capable of magnifying distant, incipient galaxies beyond it. Together with the existing Hubble Space Telescope (HST) imaging, this publicly available dataset opens a fundamentally new discovery space to understand the remaining mysteries of the formation and evolution of galaxies across cosmic time. In this work, we detect and measure some 60,000 objects across the 49 arcmin$^2$ JWST footprint down to a $5\,\sigma$ limiting magnitude of $\sim$30 mag in 0.32"apertures. Photometry is performed using circular apertures on images matched to the point spread function of the reddest NIRCam broad band, F444W, and cleaned of bright cluster galaxies and the related intra-cluster light. To give an impression of the photometric performance, we measure photometric redshifts and achieve a $\sigma_{\rm NMAD}\approx0.03$ based on known, but relatively small, spectroscopic samples. With this paper, we publicly release our HST and JWST PSF-matched photometric catalog with optimally assigned aperture sizes for easy use, along with single aperture catalogs, photometric redshifts, rest-frame colors, and individual magnification estimates. These catalogs will set the stage for efficient and deep spectroscopic follow-up of some of the first JWST-selected samples in Summer 2023.
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition

Recent attempts mainly focus on learning deep representations for each video individually under the episodic meta-learning regime and then performing temporal alignment to match query and support videos. However, they still suffer from two drawbacks: (i) learning individual features without considering the entire task may result in limited representation capability, and (ii) existing alignment strategies are sensitive to noises and misaligned instances. To handle the two limitations, we propose a novel Hybrid Relation guided temporal Set Matching (HyRSM++) approach for few-shot action recognition. The core idea of HyRSM++ is to integrate all videos within the task to learn discriminative representations and involve a robust matching technique. To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric. Given the basic representations from the feature extractor, the hybrid relation module is introduced to fully exploit associated relations within and cross videos in an episodic task and thus can learn task-specific embeddings. Subsequently, in the temporal set matching metric, we carry out the distance measure between query and support videos from a set matching perspective and design a Bi-MHM to improve the resilience to misaligned instances. In addition, we explicitly exploit the temporal coherence in videos to regularize the matching process. Furthermore, we extend the proposed HyRSM++ to deal with the more challenging semi-supervised few-shot action recognition and unsupervised few-shot action recognition tasks. Experimental results on multiple benchmarks demonstrate that our method achieves state-of-the-art performance under various few-shot settings. The source code is available at https://github.com/alibaba-mmai-research/HyRSMPlusPlus.
A Dual-Function Radar-Communication System Empowered by Beyond Diagonal Reconfigurable Intelligent Surface

This work focuses on the use of reconfigurable intelligent surface (RIS) in dual-function radar-communication (DFRC) systems to improve communication capacity and sensing precision, and enhance coverage for both functions. In contrast to most of the existing RIS aided DFRC works where the RIS is modeled as a diagonal phase shift matrix and can only reflect signals to half space, we propose a novel beyond diagonal RIS (BD-RIS) aided DFRC system. Specifically, the proposed BD-RIS supports the hybrid reflecting and transmitting mode, and is compatible with flexible single/group/fully-connected architectures, enabling the system to realize full-space coverage. To achieve the expected benefits, we jointly optimize the transmit waveform, the BD-RIS coefficients, and sensing receive filters, by maximizing the minimum signal-to-clutter-plus-noise ratio for fair target detection, subject to the constraints of the communication quality of service, different BD-RIS architectures and power budget. To solve the non-convex and non-smooth max-min problem, a general solution based on the alternating direction method of multipliers is provided for all considered BD-RIS architectures. Numerical simulations validate the efficacy of the proposed algorithm and show the superiority of the BD-RIS aided DFRC system in terms of both communication and sensing compared to conventional RIS aided DFRC.
Spatio-temporal comparisons of the hydrogen-alpha line width and ALMA 3 mm brightness temperature in the weak solar network

Comparisons between the Atacama Large Millimeter/sub-millimeter Array (ALMA) 3 mm emission and a range of optical and UV solar observations have found the strongest correspondence between the width of the hydrogen alpha line at 656.3 nm and the 3 mm brightness temperature. Previous studies on the oscillatory power of p-modes using ALMA Band 3 and Band 6 data in the 3–5 min period bandpass have found a confusing mix of results, with many reporting a complete lack of the p-mode enhancement typically found in other chromospheric observables. We study these issues using an extensive, publicly available coordinated data set targeting a region of weak network flux near disk center at time SOL 2017-03-17T15:42-16:45. We focus on the Interferometric Bidimensional Spectropolarimeter (IBIS) H-alpha and ALMA 3 mm data series. We confirm the strong correlation between the H-alpha line width and the 3 mm brightness temperature, but find a bimodal relation between the two diagnostics, with a shallower slope of 7.4e-5 Å/K in cooler regions and steeper slope of 1.2e-4 Å/K in hotter regions. The origin of the bimodal distribution is unknown, but does hold for the duration of the observations. Both slopes are steeper than a previously reported value, but this is likely due to systematic differences in the analysis. We then calculate the oscillatory power in the H-alpha and 3 mm data. The IBIS data clearly show the p-mode oscillations in spatially averaged power spectra while the ALMA data do not. However, when we remove IBIS data at times corresponding to the ALMA calibration windows, the spatially averaged power spectra for the two data series are nearly identical, with a Pearson correlation coefficient of 0.9895. Further, the power in the two bands remains strongly correlated when the spatial information is retained but the power is integrated over different temporal frequency bands. We therefore argue that the lack of observed p-modes in the ALMA data may be predominantly due to spectral windowing induced by the timing and duration of the calibration observations. Finally, we find that spatial maps of oscillatory power at 3 mm display the pattern of magnetic shadows and halos typically displayed by other chromospheric diagnostics.
On the squeeze effect of lubricants with additives between rough porous rectangular plates

This paper describes a theoretical analysis of the combined effects of unidirectional surface roughness and the lubricant additives on the performance characteristics of a porous squeeze film lubrication between two rectangular plates of finite dimensions. The lubricant with microstructure additives is modeled as a Stokes couplestress fluid. The stochastic Reynolds type equation is derived to account for the effects due to surface roughness and lubricant additives. The problem is solved analytically using the stochastic method developed by Christensen. It is found that, there exists a critical value of the aspect ratio above which the roughness effects are pronounced due to the size effects of the lubricant additives. Further, this critical value is dependent on the microstructure size of additives. The coarser the microstructure, higher is the critical value of the aspect ratio.
Ab Initio
 Derivation of Lattice-Gauge-Theory Dynamics for Cold Gases in Optical Lattices

We introduce a method for quantum simulation of U$(1)$ lattice gauge theories coupled to matter, utilizing alkaline-earth(-like) atoms in state-dependent optical lattices. The proposal enables the study of both gauge and fermionic-matter fields without integrating out one of them in one and two dimensions. We focus on a realistic and robust implementation that utilizes the long-lived metastable clock state available in alkaline-earth(-like) atomic species. Starting from an $ab\,initio$ modelling of the experimental setting, we systematically carry out a derivation of the target U$(1)$ gauge theory. This approach allows us to identify and address conceptual and practical challenges for the implementation of lattice gauge theories that - while pivotal for a successful implementation - have never been rigorously addressed in the literature: those include the specific engineering of lattice potentials to achieve the desired structure of Wannier functions, and the subtleties involved in realizing the proper separation of energy scales to enable gauge-invariant dynamics. We discuss realistic experiments that can be carried out within such a platform using the fermionic isotope $^{173}$Yb, addressing via simulations all key sources of imperfections, and provide concrete parameter estimates for relevant energy scales in both one- and two-dimensional settings.
Do You Need a Hand? – A Bimanual Robotic Dressing Assistance Scheme

Developing physically assistive robots capable of dressing assistance has the potential to significantly improve the lives of the elderly and disabled population. However, most robotics dressing strategies considered a single robot only, which greatly limited the performance of the dressing assistance. In fact, healthcare professionals perform the task bimanually. Inspired by them, we propose a bimanual cooperative scheme for robotic dressing assistance. In the scheme, an interactive robot joins hands with the human thus supporting/guiding the human in the dressing process while the dressing robot performs the dressing task. We identify a key feature: the elbow angle that affects the dressing action and propose an optimal strategy for the interactive robot using the feature. A dressing coordinate based on the posture of the arm is defined to better encode the dressing policy. We validate the interactive dressing scheme with extensive experiments and also an ablation study.
Origin of the negative thermal expansion in and

The negative thermal expansion recently observed over a wide range of temperatures in may be attributed to the existence of low-frequency phonon modes which can propagate with no distortions of the tetrahedra and octahedra, the so-called `rigid unit modes'. Using methods developed for the study of similar modes in silicates we have located the surfaces of these modes in wave-vector space. The rigid-unit mode interpretation accounts for the weak effect of the 430 K structural phase transition on the negative thermal expansion, provided that the disordered phase does not involve formation of and complexes. On the other hand, the crystal structure of the related material is cross braced by pairs of linked tetrahedra and is therefore significantly less flexible. In this case a qualitatively different mechanism may be responsible for the negative thermal expansion observed in .
FETA: Fair Evaluation of Touch-based Authentication

In this paper, we investigate common pitfalls affecting the evaluation of authentication systems based on touch dynamics. We consider different factors that lead to misrepresented performance, are incompatible with stated system and threat models or impede reproducibility and comparability with previous work. Speciﬁcally, we investigate the effects of (i) small sample sizes (both number of users and recording sessions), (ii) using different phone models in training data, (iii) selecting non-contiguous training data, (iv) inserting attacker samples in training data and (v) swipe aggregation. We perform a systematic review of 30 touch dynamics papers showing that all of them overlook at least one of these pitfalls. To quantify each pitfall’s effect, we design a set of experiments and collect a new longitudinal dataset of touch interactions from 515 users over 31 days comprised of 1,194,451 unique strokes. Part of this data is collected in-lab with Android devices and the rest remotely with iOS devices, allowing us to make in-depth comparisons. We make this dataset and our code available online. Our results show signiﬁcant percentage-point changes in reported mean EER for several pitfalls: including attacker data (2.55%), non-contiguous training data (3.8%) and phone model mixing (3.2%-5.8%). We show that, in a common evaluation setting, the cumulative effects of these evaluation choices result in a combined difference of 8.9% EER. We also largely observe these effects across the entire ROC curve. The pitfalls are evaluated on four distinct classiﬁers - SVM, Random Forest, Neural Network, and kNN. Furthermore, we explore additional considerations for fair evaluation when building touch-based authentication systems and quantify their impacts. Based on these insights, we propose a set of best practices that, if followed, will lead to more realistic and comparable reporting of results in the ﬁeld.
Coherent pair of measures for orthogonal polynomials on lattices

We consider two sequences of orthogonal polynomials $(P_n)_{n\geq 0}$ and $(Q_n)_{n\geq 0}$ with respect regular functionals ${\bf u}$ and ${\bf v}$, respectively. We assume that $$\sum_{j=1} ^{M} a_{j,n}\mathrm{D}_x ^k P_{k+n-j} (z)=\sum_{j=1} ^{N} b_{j,n}\mathrm{D}_x ^{m} Q_{m+n-j} (z)\;,$$ with $k,m,M,N \in \mathbb{N}$, $a_{j,n}$ and $b_{j,n}$ are sequences of complex numbers, $$2\mathrm{S}_xf(x(s))=(\triangle +2\,\mathrm{I})f(z),~~ \mathrm{D}_xf(x(s))=\frac{\triangle}{\triangle x(s-1/2)}f(z),$$ $z=x(s-1/2)$, $\mathrm{I}$ is the identity operator, $x$ defines a lattice, and $\triangle f(s)=f(s+1)-f(s)$. We show that under some natural conditions, the functionals ${\bf u}$ and ${\bf v}$ are connected by a rational factor whenever $m=k$, and for $k>m$, ${\bf u}$ and ${\bf S}_x ^{k-m}{\bf v}$ are semiclassical functionals and in addition ${\bf S}_x{\bf u}$ and ${\bf S}_x ^{k-m+1}{\bf v}$ are connected by a rational factor. This leads to the notion of $(M,N)$-coherent pair of measures of order $(m,k)$ extended to orthogonal polynomials on lattices.
A Brain-inspired Memory Transformation based Differentiable Neural Computer for Reasoning-based Question Answering

Reasoning and question answering as a basic cognitive function for humans, is nevertheless a great challenge for current artificial intelligence. Although the Differentiable Neural Computer (DNC) model could solve such problems to a certain extent, the development is still limited by its high algorithm complexity, slow convergence speed, and poor test robustness. Inspired by the learning and memory mechanism of the brain, this paper proposed a Memory Transformation based Differentiable Neural Computer (MT-DNC) model. MT-DNC incorporates working memory and long-term memory into DNC, and realizes the autonomous transformation of acquired experience between working memory and long-term memory, thereby helping to effectively extract acquired knowledge to improve reasoning ability. Experimental results on bAbI question answering task demonstrated that our proposed method achieves superior performance and faster convergence speed compared to other existing DNN and DNC models. Ablation studies also indicated that the memory transformation from working memory to long-term memory plays essential role in improving the robustness and stability of reasoning. This work explores how brain-inspired memory transformation can be integrated and applied to complex intelligent dialogue and reasoning systems.
Coping with geometric discontinuities in porous shallow water models

Porosity-based models are a viable alternative to classical two-dimensional (2-d) Shallow water Equations (SWE) when the interaction of shallow flows with obstacles is modelled. The exact solution of the Single Porosity (SP) Riemann problem, which is the building block of numerous porosity models solved with the Finite Volume method, exhibits an interesting feature, namely the multiplicity of solutions when a supercritical flow impinges on a sudden porosity reduction. In the present paper, this ambiguity is overcome by systematically comparing the solution of the one-dimensional (1-d) SP Riemann problem with the corresponding 2-d SWE numerical solutions at local porosity discontinuities. An additional result of this comparison is that the SP Riemann problem should incorporate an adequate amount of head loss through porosity discontinuities when strongly supercritical flows are considered. An approximate Riemann solver, able to pick the physically congruent solution among the alternatives and equipped with the required head loss amount, shows promising results when implemented in a 1-d Single Porosity Finite Volume scheme.
Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition

In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the considerable amount of labeled resources. However, most low-resource languages do not have such an abundance of labeled data as high-resource English, leading to poor performance of NER in these low-resource languages. Inspired by knowledge transfer, we propose Converse Attention Network, or CAN in short, to improve the performance of NER in low-resource languages by leveraging the knowledge learned in pretrained high-resource English models. CAN ﬁrst translates low-resource languages into high-resource English using an attention based translation module. In the process of translation, CAN obtain the attention matrices that align the two languages. Furthermore, CAN use the attention matrices to align the high-resource semantic features from a pretrained high-resource English model with the low-resource semantic features. As a result, CAN obtains aligned high-resource semantic features to enrich the representations of low-resource languages. Experiments on four low-resource NER datasets show that CAN achieves consistent and signiﬁcant performance improvements, which indicates the eﬀectiveness of CAN.
Design and Simulation-Based Testing of Connected Traffic Light Guidance Systems

The establishment of fast and reliable communication technologies, such as 5G, is enabling the evolution of a new generation of connected ADAS. This work aims to develop a traffic light advisory system, Multiple Traffic Light Advisor (MTLA), to improve driving efficiency and intersection viability, and reduce urban pollution. The developed system guides the driver on how to modify the vehicle speed to efficiently utilize the current and future states of the traffic lights ahead. Starting from a non-optimal implementation of the overall architecture, MTLA is further improved through a non-linear MPC approach. The developed system is tested in a virtual environment in IPG CarMaker and results show good performances with a high potential and space for future developments.
Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling

We identify and overcome two key obstacles in extending the success of BERT-style pre-training, or the masked image modeling, to convolutional networks (convnets): (i) convolution operation cannot handle irregular, random-masked input images; (ii) the single-scale nature of BERT pre-training is inconsistent with convnet's hierarchical structure. For (i), we treat unmasked pixels as sparse voxels of 3D point clouds and use sparse convolution to encode. This is the first use of sparse convolution for 2D masked modeling. For (ii), we develop a hierarchical decoder to reconstruct images from multi-scale encoded features. Our method called Sparse masKed modeling (SparK) is general: it can be used directly on any convolutional model without backbone modifications. We validate it on both classical (ResNet) and modern (ConvNeXt) models: on three downstream tasks, it surpasses both state-of-the-art contrastive learning and transformer-based masked modeling by similarly large margins (around +1.0%). Improvements on object detection and instance segmentation are more substantial (up to +3.5%), verifying the strong transferability of features learned. We also find its favorable scaling behavior by observing more gains on larger models. All this evidence reveals a promising future of generative pre-training on convnets. Codes and models are released at https://github.com/keyu-tian/SparK.
End-to-end Unsupervised Learning of Long-Term 3D Stable objects

— 3 D point cloud semantic classiﬁcation is an important task in robotics as it enables a better understanding of the mapped environment. This work proposes to learn the long-term stability of the 3 D objects using a neural network based on PointNet++, where the long-term stable object refers to a static object that cannot move on its own (e.g. tree, pole, building). The training data is generated in an unsupervised manner by assigning a continuous label to individual points by exploiting multiple time slices of the same environment. Instead of using discrete labels, i.e. static/dynamic, we propose to use a continuous label value indicating point temporal stability to train a regression PointNet++ network. We evaluated our approach on point cloud data of two parking lots from the NCLT dataset. The experiments’ performance reveals that static vs dynamic object classiﬁcation is best performed by training a regression model, followed by thresholding, compared to directly training a classiﬁcation model.