Miracle-dz/UniFAR-FTUs huggingface dataset:
PAANet: Visual Perception based Four-stage Framework for Salient Object Detection using High-order Contrast Operator

It is believed that human vision system (HVS) consists of pre-attentive process and attention process when performing salient object detection (SOD). Based on this fact, we propose a four-stage framework for SOD, in which the ﬁrst two stages match the P re- A ttentive process consisting of general feature extraction (GFE) and feature preprocessing (FP), and the last two stages are corresponding to A ttention process containing saliency feature extraction (SFE) and the feature aggregation (FA), namely PAANet . According to the pre-attentive process, the GFE stage applies the fully-trained backbone and needs no further ﬁnetuning for diﬀerent datasets. This modiﬁcation can greatly increase the training speed. The FP stage plays the role of ﬁnetuning but works more eﬃciently because of its simpler structure and fewer parameters. Moreover, in SFE stage we design for saliency feature extraction a novel contrast operator, which works more semantically in contrast with the traditional convolution operator when extracting the interactive information between the foreground and its surroundings. Interestingly, this contrast operator can be cascaded to form a deeper structure and extract higher-order saliency more eﬀective for complex scene. Comparative experiments with the state-of-the-art methods on 5 datasets demonstrate the eﬀectiveness of our framework.
The Future of Hackathon Research and Practice

Hackathons are time-bounded collaborative events which have become a global phenomenon adopted by both researchers and practitioners in a plethora of contexts. Hackathon events are generally used to accelerate the development of, for example, scientific results and collaborations, communities, and innovative prototypes addressing urgent challenges. As hackathons have been adopted into many different contexts, the events have also been adapted in numerous ways corresponding to the unique needs and situations of organizers, participants and other stakeholders. While these interdisciplinary adaptions, in general affords many advantages - such as tailoring the format to specific needs - they also entail certain challenges, specifically: 1) limited exchange of best practices, 2) limited exchange of research findings, and 3) larger overarching questions that require interdisciplinary collaboration are not discovered and remain unaddressed. We call for interdisciplinary collaborations to address these challenges. As a first initiative towards this, we performed an interdisciplinary collaborative analysis in the context of a workshop at the Lorentz Center, Leiden in December 2021. In this paper, we present the results of this analysis in terms of six important areas which we envision to contribute to maturing hackathon research and practice: 1) hackathons for different purposes, 2) socio-technical event design, 3) scaling up, 4) making hackathons equitable, 5) studying hackathons, and 6) hackathon goals and how to reach them. We present these areas in terms of the state of the art and research proposals and conclude the paper by suggesting next steps needed for advancing hackathon research and practice.
Study of the resonance contributions in the 
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The decay process $\Xi_{b}^{-} \rightarrow pK^{-}K^{-}$ is studied with the final state interaction approach by considering the contributions from the $S$-wave meson-baryon interactions, and also the intermediate state $\Lambda(1520)$ in the $D$-wave. The low-lying resonances $\Lambda(1405)$ and $\Lambda(1670)$ have significant contributions, which are both dynamically generated from the $S$-wave final state interactions with isospin $I=0$. Furthermore, the $\Lambda(1520)$ state also has important contributions from $D$-wave. With these resonances contributions, the experimental data of the lower $pK^{-}$ invariant mass distributions are well described. We also discuss the contribution of another resonance in the $S$-wave with isospoin $I=1$, which cannot be ignored. Moreover, some of the branching fractions obtained for the corresponding decay channels are consistent with the experimental measurements.
Three Dimensional Rotations of the Electroweak Interaction

An extension to the standard electroweak model is presented that is different from previous models. This extension involves a three dimensional rotation of a plane defined by two charge-neutral current axes. In this model the plane also contains the three algebraically coupled SU(2) groups quantization axes that comprise the SU(3) group. This extension differentiates leptonic currents from baryonic currents through hypercolor-charge channels and predicts additional symmetries and currents due to different rotational orientations of the interaction projecting onto the plane defining the SU(3) symmetry group and a U(1) symmetry axis.
Symmetric Volume Maps: Order-invariant Volumetric Mesh Correspondence with Free Boundary

Although shape correspondence is a central problem in geometry processing, most methods for this task apply only to two-dimensional surfaces. The neglected task of volumetric correspondence—a natural extension relevant to shapes extracted from simulation, medical imaging, and volume rendering—presents unique challenges that do not appear in the two-dimensional case. In this work, we propose a method for mapping between volumes represented as tetrahedral meshes. Our formulation minimizes a distortion energy designed to extract maps symmetrically, i.e., without dependence on the ordering of the source and target domains. We accompany our method with theoretical discussion describing the consequences of this symmetry assumption, leading us to select a symmetrized ARAP energy that favors isometric correspondences. Our final formulation optimizes for near-isometry while matching the boundary. We demonstrate our method on a diverse geometric dataset, producing low-distortion matchings that align closely to the boundary.
Optomechanics for thermal characterization of suspended graphene

Thermal properties of suspended single-layer graphene membranes are investigated by characterization of their mechanical motion in response to a high-frequency modulated laser. A characteristic delay time $\tau$ between the optical intensity and mechanical motion is observed, which is attributed to the time required to raise the temperature of the membrane. We find, however, that the measured time constants are significantly larger than the predicted ones based on values of the specific heat and thermal conductivity. In order to explain the discrepancy between measured and modeled tau, a model is proposed that takes a thermal boundary resistance at the edge of the graphene drum into account. The measurements provide a noninvasive way to characterize thermal properties of suspended atomically thin membranes, providing information that can be hard to obtain by other means.
Addressing the issue of stochastic environments and local decision-making in multi-objective reinforcement learning

Multi-objective reinforcement learning (MORL) is a relatively new field which builds on conventional Reinforcement Learning (RL) to solve multi-objective problems. One of common algorithm is to extend scalar value Q-learning by using vector Q values in combination with a utility function, which captures the user’s preference for action selection. This study follows on prior works, and focuses on what factors influence the frequency with which value-based MORL Q-learning algorithms learn the optimal policy for an environment with stochastic state transitions in scenarios where the goal is to maximise the Scalarised Expected Return (SER) that is, to maximise the average outcome over multiple runs rather than the outcome within each individual episode. The analysis of the interaction between stochastic environment and MORL Q-learning algorithms run on a simple Multi-objective Markov decision process (MOMDP) Space Traders problem with different variant versions. The empirical evaluations show that well designed reward signal can improve the performance of the original baseline algorithm, however it is still not enough to address more general environment. A variant of MORL Q-Learning incorporating global statistics is shown to outperform the baseline method in original Space Traders problem, but remains below 100% effectiveness in finding the find desired SER-optimal policy at the end of training. On the other hand, Option learning is guarantied to converge to desired SER-optimal policy but it is not able to scale up to solve more complex problem in real-life. The main contribution of this thesis is to identify the extent to which the issue of noisy Q-value estimates impacts on the ability to learn optimal policies under the combination of stochastic environments, non-linear utility and a constant learning rate. In conclusion, this study presents several alternative methods that may be more suitable to overcome noisy Q value estimate issue and also find SER optimal policy in MOMDPs with stochastic transitions.
Guided Waves in Multilayered Plates for Internal Defect Detection

In this paper, stress and displacement fields inside a multilayered plate are analytically computed. For the theoretical modeling, individual layers of the multilayered plate are assumed to be elastic and homogeneous, but anisotropic. These theoretical computations are then experimentally verified in a qualitative manner. Experimental investigations are carried out on a ceramic composite plate that has 12 plies of fibers going in 0° and 90° directions, [0.90]\d3\i\ds. The incident angles and frequencies of the ultrasonic signal for generating different Lamb wave modes in this plate are obtained experimentally as well as from the theory. Displacement and stress fields inside the plate for some of these Lamb modes are then computed. Computation of stress and displacement fields inside the plate is useful for studying the internal damage. Internal stress distribution varies significantly from one Lamb mode to another. Hence, different Lamb modes should detect defects in different layers of the plate. The Lamb mode that produces maximum stress in a specific layer should be most sensitive for detecting defects in that layer. This is observed experimentally as well.
Flux Reconstruction for Goal-Oriented A Posteriori Error Estimation

We propose a new heuristic goal-oriented a posteriori error estimator that connects the dual weighted residual method with equilibrated a posteriori error estimation. Our numerical experiments demonstrate the practical reliability of the error estimator, confirming theoretical predictions, as well as optimally convergent adaptivity even over singular domains and coarse meshes. The central algorithm is a localized flux reconstruction, which has been implemented in the finite element library deal.II. For a solid preparation we assess the performance of the equilibrated a posteriori error estimator of the energy norm in numerical experiments. Moreover, we give what seems to be first rigorous discussion in the numerical literature of localized flux reconstruction over quadrilateral meshes with hanging nodes.
Morse theory study on the evolution of nodal lines in 
PT
-symmetric nodal-line semimetals

A nodal-line semimetal is a topological gapless phase containing one-dimensional degeneracies called nodal lines. The nodal lines are deformed by a continuous change of the system such as pressure and they can even change their topology, but it is not systematically understood what kind of changes of topology of nodal lines are possible. In this paper, we classify the events of topology change of nodal lines by the Morse theory and reveal that only three types of topology changes of nodal lines, i.e., creation, reconnection, and annihilation, are possible in the spinless nodal-line semimetal protected by inversion and time-reversal symmetries. They are characterized by an index having the values 0, 1, and 2 for the above three types in the Morse theory. Moreover, we extend our theory to systems with rotational symmetries and mirror symmetry and disclose the possible events of topology change of nodal lines under each symmetry.
Benchmarking Graph Neural Networks for FMRI analysis

Graph Neural Networks (GNNs) have emerged as a powerful tool to learn from graph-structured data. A paramount example of such data is the brain, which operates as a network, from the micro-scale of neurons, to the macro-scale of regions. This organization deemed GNNs a natural tool of choice to model brain activity, and have consequently attracted a lot of attention in the neuroimaging community. Yet, the advantage of adopting these models over conventional methods has not yet been assessed in a systematic way to gauge if GNNs are capable of leveraging the underlying structure of the data to improve learning. In this work, we study and evaluate the performance of five popular GNN architectures in diagnosing major depression disorder and autism spectrum disorder in two multi-site clinical datasets, and sex classification on the UKBioBank, from functional brain scans under a general uniform framework. Our results show that GNNs fail to outperform kernel-based and structure-agnostic deep learning models, in which 1D CNNs outperform the other methods in all scenarios. We highlight that creating optimal graph structures for functional brain data is a major bottleneck hindering the performance of GNNs, where existing works use arbitrary measures to define the edges resulting in noisy graphs. We therefore propose to integrate graph diffusion into existing architectures and show that it can alleviate this problem and improve their performance. Our results call for increased moderation and rigorous validation when evaluating graph methods and advocate for more data-centeric approaches in developing GNNs for functional neuroimaging applications.
Pinning of Diffusional Instabilities by Non-Uniform Curvature

Turing patterns emerge from a spatially uniform state following a linear instability driven by diffusion. Features of the eventual pattern (stabilized by non-linearities) are already present in the initial unstable modes. On a uniform flat surface or perfect sphere, the unstable modes and final patterns are degenerate, reflecting translational/rotational symmetry. This symmetry can be broken, e.g. by a bump on a flat substrate or by deforming a sphere. As the diffusion operator on a two dimensional manifold depends on the underlying curvature, the degeneracy of the initial unstable mode is similarly reduced. Different shapes can pin different modes. We adapt methods of conformal mapping and perturbation theory to analytically examine how bumps and ripples entrain modes of the diffusion operator on cylinders and spheres. We confirm these results numerically, and provide closed form expressions that describe how non-uniformities in curvature pin diffusion-driven instabilities and the resulting patterns.
Global Optimization with Parametric Function Approximation

We consider the problem of global optimization with noisy zeroth order oracles - a well-motivated problem useful for various applications ranging from hyper-parameter tuning for deep learning to new material design. Existing work relies on Gaussian processes or other non-parametric family, which suffers from the curse of dimensionality. In this paper, we propose a new algorithm GO-UCB that leverages a parametric family of functions (e.g., neural networks) instead. Under a realizable assumption and a few other mild geometric conditions, we show that GO-UCB achieves a cumulative regret of \~O$(\sqrt{T})$ where $T$ is the time horizon. At the core of GO-UCB is a carefully designed uncertainty set over parameters based on gradients that allows optimistic exploration. Synthetic and real-world experiments illustrate GO-UCB works better than popular Bayesian optimization approaches, even if the model is misspecified.
Casimir effect between dissimilar materials: a test for the proximity theorem

We calculate exactly the Casimir force between a spherical particle and a plane, both with arbitrary dielectric properties, in the non-retarded limit. Using a Spectral Representation formalism, we show that the Casimir force of a sphere made of a material A and a plane made of a material B, diﬀer from the case when the sphere is made of B, and the plane is made of A. The diﬀerences in energy and force show the importance of the geometry, and make evident the necessity of realistic descriptions of the sphere-plane system beyond the Proximity Theorem approximation.
Yield Evaluation of Citrus Fruits based on the YoloV5 compressed by Knowledge Distillation

—In the ﬁeld of planting fruit trees, pre-harvest estimation of fruit yield is important for fruit storage and price evaluation. However, considering the cost, the yield of each tree cannot be assessed by directly picking the immature fruit. Therefore, the problem is a very difﬁcult task. In this paper, a fruit counting and yield assessment method based on computer vision is proposed for citrus fruit trees as an example. Firstly, images of single fruit trees from different angles are acquired and the number of fruits is detected using a deep Convolutional Neural Network model YOLOv5, and the model is compressed using a knowledge distillation method. Then, a linear regression method is used to model yield-related features and evaluate yield. Experiments show that the proposed method can accurately count fruits and approximate the yield.
Efficiently Answering Quality Constrained Shortest Distance Queries in Large Graphs

One of the fundamental concept in the graph-related problem is the shortest path distance. This problem is widely studied for decades, and has numerous real-life applications. Nevertheless, quality constraints are naturally associated with edges. For instance, finding the distance between two vertices along valid edges (i.e., edges that satisfy a given quality constraint) is also critical. To fill this research gap, we investigate this vital problem, i.e., the quality constraint shortest distance problems. An efficient index structure is proposed based on 2-hop labeling approaches. By using a path dominance relationship into both quality and length information, it is demonstrated that the new index could ensure the minimal property. To further speed up the performance, we present an efficient query processing algorithm. Extensive experimental studies over real-life datasets demonstrates efficiency and effectiveness of our techniques.
Holographic Integrated Sensing and Communications: Principles, Technology, and Implementation

Integrated sensing and communication (ISAC) has attracted much attention as a promising approach to alleviate spectrum congestion. However, traditional ISAC systems rely on phased arrays to provide high spatial diversity, where enormous power-consuming components such as phase shifters are used, leading to the high power consumption. In this article, we introduce the holographic ISAC, a new paradigm to enable high spatial diversity with low power consumption by using reconfigurable holographic surfaces (RHSs), which is an innovative type of planar antenna with densely deployed metamaterial elements. We first introduce the hardware structure and working principle of the RHS and then propose a novel holographic beamforming scheme for ISAC. Moreover, we build an RHS-enabled hardware prototype for ISAC and evaluate its performance. Simulation and experimental results verify the feasibility of the holographic ISAC and reveal the great potential of the RHS for reducing power consumption. Furthermore, future research directions and key challenges related to the holographic ISAC are discussed.
Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction

The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions. In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA). In this work, we propose a novel transfer learning algorithm called Renewing Iterative Self-labeling Domain Adaptation (Re-ISDA).
Energy Smart Buildings: Parallel Uniform Cost-Search with Energy Storage and Generation

—The amalgamation of Internet of Things and the smart grid enables the energy optimal scheduling of appliances based on user needs and dynamic energy prices. Additionally, progress in local storage technology calls for exploiting additional sources of ﬂexibility. In this paper, we propose a scheduling approach for building operation management, considering factors such as energy storage, local energy generation, and dynamic energy prices. In addition, we propose a new optimization strategy to discover the optimal scheduling of devices. Our approach utilizes parallel uniform cost-search to explore the complex search space and to ﬁnd the optimal schedule within a user-acceptable amount of time. The evaluation utilizes real-world data for the devices, and the price signals, while the architecture is designed following a micro-service approach, enabling modularity and loose-coupling. The evaluation shows that including local energy storage as part of the optimization problem further reduces overall costs by up to 22.64% when compared to schedules without energy storage. Parallel uniform cost-search decreases the time to ﬁnd the optimal schedule by a factor of 4.7 with respect to the traditional uniform cost-search algorithm.
Quantifying Quantum Entanglement in Two-Qubit Mixed State from Connected Correlator

Our study employs a connected correlation matrix to quantify Quantum Entanglement. The matrix encompasses all necessary measures for assessing the degree of entanglement between particles. We begin with a three-qubit state and involve obtaining a mixed state by performing partial tracing over one qubit. Our goal is to exclude the non-connected sector by focusing on the connected correlation. This suggests that the connected correlation is deemed crucial for capturing relevant entanglement degrees. The study classifies mixed states and observes that separable states exhibit the lowest correlation within each class. We demonstrate that the entanglement measure monotonically increases concerning the correlation measure. This implies that connected correlation serves as an effective measure of Quantum Entanglement. Finally, our proposal suggests that interpreting Quantum Entanglement from a local perspective is possible. The observable is described as a vector with locality but violates freedom of choice.
Efficiently Finding Adversarial Examples with DNN Preprocessing

Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly complex task that used to be unimaginable for ma-chines to carry out. In doing so, they do a lot of decision making which, depending on the application, may be disastrous if gone wrong. This necessitates a formal argument that the underlying neural networks sat-isfy certain desirable properties. Robustness is one such key property for DNNs, particularly if they are being deployed in safety- or business-critical applications. Informally speaking, a DNN is not robust if very small changes to its input may aﬀect the output in a considerable way (e.g. changes the classiﬁcation for that input). The task of ﬁnding an adversarial example is to demonstrate this lack of robustness, whenever applicable. While this is doable with the help of constrained optimization techniques, scalability becomes a challenge due to large-sized networks. This paper proposes the use of information gathered by preprocessing the DNN to heavily simplify the optimization problem. Our experiments substantiate that this is eﬀective, and does signiﬁcantly better than the state-of-the-art.