Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Attention-based Network for Fast Salient Object Detection

In the current salient object detection network, the most popular method is using U-shape structure. However, the massive number of parameters leads to more consumption of computing and storage resources which are not feasible to deploy on the limited memory device. Some others shallow layer network will not maintain the same accuracy compared with U-shape structure and the deep network structure with more parameters will not converge to a global minimum loss with great speed. To overcome all of these disadvantages, we propose a new deep convolution network architecture with three contributions: (1) using smaller convolution neural networks (CNNs) to compress the model in our improved salient object features compression and reinforcement extraction module (ISFCREM) to reduce parameters of the model. (2) introducing channel attention mechanism to weigh different channels for improving the ability of feature representation. (3) applying a new optimizer to accumulate the long-term gradient information during training to adaptively tune the learning rate. The results demonstrate that the proposed method can compress the model to 1/3 of the original size nearly without losing the accuracy and converging faster and more smoothly on six widely used datasets of salient object detection compared with the others models. Our code is published in https://gitee.com/binzhangbinzhangbin/code-a-novel-attention-based-network-for-fast-salientobject-detection.git
A Multi-Layout Approach to Immersive Visualization of Network Data

Network data plays a vital role in much of today’s visualization research. However, there is no optimal graph layout for all possible tasks. The choice of layout heavily depends on the type of network and the task at hand and is often limited by the nature of traditional desktop setups. Recent studies have shown the potential of extended reality (XR) in the immersive exploration of network data. We introduce a multi-layout approach that allows users to effectively explore hierarchical network data in immersive space. The system leverages different layout techniques and interactions to provide an optimal view of the data depending on the task and the level of detail required to solve it. To evaluate our approach, we have conducted a user study comparing our design against the state of the art for immersive network visualization. Participants performed tasks at varying spatial scopes. The results show that our approach outperforms the baseline in spatially focused scenarios as well as when the whole network needs to be considered.
Enhancing fiber atom interferometer by in-fiber laser cooling

We demonstrate an inertia sensitive atom interferometer optically guided inside a 22-cm-long negative curvature hollow-core photonic crystal fiber with an interferometer time of 20 ms. The result prolongs the previous fiber guided atom interferometer time by three orders of magnitude. The improvement arises from the realization of in-fiber {\Lambda}-enhanced gray molasses and delta-kick cooling to cool atoms from 32 {\mu}K to below 1 {\mu}K in 4 ms. The in-fiber cooling overcomes the inevitable heating during the atom loading process and allows a shallow guiding optical potential to minimize decoherence. Our results permit bringing atoms close to source fields for sensing and could lead to compact inertial quantum sensors with a sub-millimeter resolution.
Future Prospects on Constraining Neutrino Cosmology with the Ali CMB Polarization Telescope

We forecast the constraints on the parameters of neutrino physics with the constructions of Cosmic Microwave Background (CMB) temperature, E-mode polarization, and lensing spectra for the ground-based Ali Cosmic Microwave Background Polarization Telescope (AliCPT). To implement the forecast calculations, we perform numerical simulations which show that AliCPT would yield the result σ(N eff) = 0.56 and M ν < 1.10 eV (95% CL.) with TT, TE, EE, and CMB lensing power spectra for the first year of observation. Moreover, we investigate how the instrumental parameters, such as noise level, FWHM, and sky coverage can affect the constraints on these two parameters. Our forecasting results find that a larger aperture telescope with a larger sky survey strategy would significantly improve the current constraints for AliCPT.
Three-phase rectifier using a Sepic DC-DC converter in continuous conduction mode for power factor correction

This paper presents an analysis of a three-phase rectifier with high power factor using a Sepic DC-DC converter operating in continuous conduction mode (CCM). The structure is particularly simple and robust. Its main features are: one power processing stage, which can operate as step-down or step-up voltage; lower harmonic distortion in the line current; and natural isolation. The converter works with constant frequency and PWM modulation. A study for steady-state conditions and a design procedure are presented, and experimental results obtained from a laboratory prototype are also presented.
An Architecture for Exploiting Native User-Land Checkpoint-Restart to Improve Fuzzing

Fuzzing is one of the most popular and widely used techniques to find vulnerabilities in any application. Fuzzers are fast enough, but they still spend a good portion of time to restart a crashed application and then fuzz it from the beginning. Fuzzing an application from a point deeper in the execution is also important. To do this, a user needs to take a snapshot of the program while fuzzing it on top of an emulator, virtual machine, or by utilizing a special kernel module to enable checkpointing. Even with this ability, it can be difficult to attach a fuzzer after restoring a checkpoint. As a result, most fuzzers leverage a form of fork-server design. We propose a novel testing architecture that allows users to attach a fuzzer after the program has started running. We do this by natively checkpointing the target application at a point of interest, and attaching the fuzzer after restoring the checkpoint. A fork-server may even be engaged at the point of restoration. This not only improves the throughput of the fuzzing campaign by minimizing startup time, but opens up a new way to fuzz applications. With this architecture, a user can take a series of checkpoints at points of interest, and run parallel tests to reduce the overall state-complexity of an individual test. Checkpoints allow us to begin fuzzing from a deeper point in the execution path, omitting prior execution from the required coverage path. This and other checkpointing techniques are described in the paper to help improve fuzzing.
Journal of the Mechanics and Physics of Solids

This article introduces a manifold embedding data-driven paradigm to solve small-and finite-strain elasticity problems without a conventional constitutive law. This formulation follows the classical data-driven paradigm by seeking the solution that obeys the balance of linear momentum and compatibility conditions while remaining consistent with the material data by minimizing a distance measure. Our key point of departure is the introduction of a global manifold embedding as a means to learn the geometrical trend of the constitutive data mathematically represented by a smooth manifold. By training an invertible neural network to embed the data of an underlying constitutive manifold onto a Euclidean space, we reformulate the local distance-minimization problem that requires a computationally intensive combinatorial search to identify the optimal data points closest to the conservation law with a cost-efficient projection step. Meanwhile, numerical experiments performed on path-independent elastic materials of different material symmetries suggest that the geometrical inductive bias learned by the neural network is helpful to ensure more consistent predictions when dealing with data sets of limited sizes or those with missing data.
Exact Schur index in closed form

The Schur limit of the superconformal index of a four-dimensional N = 2 superconformal field theory encodes rich physical information about the protected spectrum of the theory. For a Lagrangian model, this limit of the index can be computed by a contour integral of a multivariate elliptic function. However, surprisingly, so far it has eluded exact evaluation in closed, analytical form. In this paper we propose an elementary approach to bring to heel a large class of these integrals by exploiting the ellipticity of their integrand. Our results take the form of a finite sum of (products of) the well-studied flavored Eisenstein series. In particular, we derive a compact formula for the fully flavored Schur index of all theories of class S of type a1, we put forward a conjecture for the unflavored Schur indices of all N = 4 super Yang-Mills theories with gauge group SU(N), and we present closed-form expressions for the index of various other gauge theories of low ranks. We also discuss applications to non-Lagrangian theories, modular properties, and defect indices. December 21, 2021 ar X iv :2 11 2. 09 70 5v 1 [ he pth ] 1 7 D ec 2 02 1
Transplanting Trees: Chromatic Symmetric Function Results through the Group Algebra of $S_n$

One of the major outstanding conjectures in the study of chromatic symmetric functions (CSF's) states that trees are uniquely determined by their CSF's. Though verified on graphs of order up to twenty-nine, this result has been proved only for certain subclasses of trees. Using the definition of the CSF that emerges via the Frobenius character map applied to $\mathbb{C}[S_n]$, we offer new algebraic proofs of several results about the CSF's of trees. Additionally, we prove that a"parent function"of the CSF defined in the group ring of $S_n$ can uniquely determine trees, providing further support for Stanley's conjecture.
A Systematic Comparison of Two Refactoring-aware Merging Techniques

—Dealing with merge conﬂicts in version control systems is a challenging task for software developers. Resolving merge conﬂicts is a time-consuming and error-prone process, which distracts developers from important tasks. Recent work shows that refactorings are often involved in merge conﬂicts and that refactoring-related conﬂicts tend to be larger, making them harder to resolve. In the literature, there are two refactoring-aware merging techniques that claim to automatically resolve refactoring-related conﬂicts; however, these two techniques have never been empirically compared. In this paper, we present RefMerge , a Java re-implementation of the ﬁrst technique, which is an operation-based refactoring-aware merging algorithm. We compare RefMerge to Git and the state-of-the-art graph-based refactoring-aware merging tool, IntelliMerge , on 2,001 merge scenarios with refactoring-related conﬂicts from 20 open-source projects. We ﬁnd that RefMerge completely resolves 143 (7%) merge scenarios while IntelliMerge resolves only 78 (4%). We additionally conduct a qualitative analysis of the differences between the three merging algorithms and provide insights of the strengths and weaknesses of each tool.
Impact of the Ligand Shell on Structural Changes and Decomposition of All-Inorganic Mixed-Halide Perovskite (CsPbX3) Nanocrystals under X-Ray Illumination

Jan Wahl, Philipp Haizmann, Christopher Kirsch, Rene Frecot, Nastasia Mukharamova, , Dameli Assalauova, Young Yong Kim, Ivan Zaluzhnyy, Thomas Chassé, Ivan A. Vartanyants, Heiko Peisert*, Marcus Scheele* $ Institut für physikalische und theoretische Chemie, Universität Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany # Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany + Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany ß Center for Light-Matter Interaction, Sensors & Analytics LISA, Universität Tübingen, Auf der Morgenstelle 15, 72076 Tübingen, Germany & National Research Nuclear University MEPhI (Moscow Engineering Physics Institute), Kashirskoe shosse 31, 115409 Moscow, Russia ‡ These authors contributed equally
Context-Based Music Recommendation Algorithm Evaluation

Artificial Intelligence (AI ) has been very successful in creating and predicting music playlists for online users based on their data; data received from users experience using the app such as searching the songs they like. There are lots of current technological advancements in AI due to the competition between music platform owners such as Spotify, Pandora, and more. In this paper, 6 machine learning algorithms and their individual accuracy for predicting whether a user will like a song are explored across 3 different platforms including Weka, SKLearn, and Orange. The algorithms explored include Logistic Regression, Naive Bayes, Sequential Minimal Optimization (SMO), Multilayer Perceptron (Neural Network), Nearest Neighbor, and Random Forest. With the analysis of the specific characteristics of each song provided by the Spotify API [1], Random Forest is the most successful algorithm for predicting whether a user will like a song with an accuracy of 84%. This is higher than the accuracy of 82.72% found by Mungekar using the Random Forest technique and slightly different characteristics of a song [2]. The characteristics in Mungekars Random Forest algorithm focus more on the artist and popularity rather than the sonic features of the songs. Removing the popularity aspect and focusing purely on the sonic qualities improve the accuracy of recommendations. Finally, this paper shows how song prediction can be accomplished without any monetary investments, and thus, inspires an idea of what amazing results can be accomplished with full financial research.
Silicon-on-sapphire MOSFET distributed amplifier with coplanar waveguide matching

Analog applications of silicon RF MOSFET circuits have received increased attention as potentially lower cost and higher integration alternatives to III-V technology. In this work, a thin-film silicon-on-sapphire (SOS) n-MOSEET based distributed amplifier is demonstrated. Impedance matching was achieved by a coplanar waveguide (CPW) on sapphire. The distributed amplifier has a bandwidth of 10 GHz and 5 dB gain. The amplifier's bandwidth is the broadest ever reported for a Si MOSFET technology.
Improving Ethical Outcomes with Machine-in-the-Loop: Broadening Human Understanding of Data Annotations

We introduce a machine-in-the-loop pipeline that aims to address root causes of unwanted bias in natural language based supervised machine learning tasks in the education domain. Learning from the experiences of students is foundational for education researchers, and academic administrators. 21st-century skills learned from experience are becoming a core part of college and career readiness as well as the hiring process in the new knowledge economy. Minoritized students demonstrate these skills in their daily lives, but documenting, assessing, and validating these skills is a huge problem for educational institutions. As an equity focused online platform, LivedX translates minoritized students' lived experiences into the 21st century skills, issues micro-credentials, and creates personal 21st century skills portfolio. To automate the micro credential mining from the natural language texts received from the students' submitted essays, we employed a bag-of-word model to construct a multi-output classifier. Despite our goal, our model initially exacerbated disparate impact on minoritized students. We used a machine-in-the-loop model development pipeline to address the problem and refine the aforementioned model to ensure fairness in its prediction.
Variational Bayes for high-dimensional proportional hazards models with applications to gene expression variable selection

High-throughput sequencing has led to a wave of innovation in biomedical sciences, oﬀering extraordinary opportunities for prognostic modelling and understanding disease drivers. However, the high-dimensionality and heterogeneity of large-scale proﬁling data introduces considerable challenges. We propose an interpretable and scalable Bayesian proportional hazards model for prediction and variable selection, referred to as SVB. Our method, based on a mean-ﬁeld variational approximation, overcomes the high computational cost of MCMC whilst retaining the useful features, providing excellent point estimates and oﬀering a natural mechanism for variable selection via posterior inclusion probabilities. The performance of our proposed method is assessed via extensive simulations and compared against other state-of-the-art Bayesian variable selection methods, demonstrating comparable or better performance. Finally, we demonstrate how the proposed method can be used for variable selection on two transcriptomic datasets with censored survival outcomes, where we identify genes with pre-existing biological interpretations. Availability and implementation: our method has been implemented as a freely available R package survival.svb ( https://github.com/mkomod/survival.svb ). R scripts to repro-duce our results can be found at https://github.com/mkomod/svb.exp .
A new quality preserving polygonal mesh refinement algorithm for Virtual Element Methods

Mesh adaptivity is a useful tool for efficient solution to partial differential equations in very complex geometries. In the present paper we discuss the use of polygonal mesh refinement in order to tackle two common issues: first, adaptively refine a provided good quality polygonal mesh preserving quality, second, improve the quality of a coarse poor quality polygonal mesh during the refinement process on very complex domains. For finite element methods and triangular meshes, convergence of a posteriori mesh refinement algorithms and optimality properties have been widely investigated, whereas convergence and optimality are still open problems for polygonal adaptive methods. In this article, we propose a new refinement method for convex cells with the aim of introducing some properties useful to tackle convergence and optimality for adaptive methods. The key issues in refining convex general polygons are: a refinement dependent only on the marked cells for refinement at each refinement step; a partial quality improvement, or, at least, a non degenerate quality of the mesh during the refinement iterations; a bound of the number of unknowns of the discrete problem with respect to the number of the cells in the mesh. Although these properties are quite common for refinement algorithms of triangular meshes, these issues are still open problems for polygonal meshes
RvS: What is Essential for Offline RL via Supervised Learning?

Recent work has shown that supervised learning alone, without temporal difference (TD) learning, can be remarkably effective for offline RL. When does this hold true, and which algorithmic components are necessary? Through extensive experiments, we boil supervised learning for offline RL down to its essential elements. In every environment suite we consider, simply maximizing likelihood with a two-layer feedforward MLP is competitive with state-of-the-art results of substantially more complex methods based on TD learning or sequence modeling with Transformers. Carefully choosing model capacity (e.g., via regularization or architecture) and choosing which information to condition on (e.g., goals or rewards) are critical for performance. These insights serve as a field guide for practitioners doing Reinforcement Learning via Supervised Learning (which we coin"RvS learning"). They also probe the limits of existing RvS methods, which are comparatively weak on random data, and suggest a number of open problems.
RoboAssembly: Learning Generalizable Furniture Assembly Policy in a Novel Multi-robot Contact-rich Simulation Environment

Part assembly is a typical but challenging task in robotics, where robots assemble a set of individual parts into a complete shape. In this paper, we develop a robotic assembly simulation environment for furniture assembly. We formulate the part assembly task as a concrete reinforcement learning problem and propose a pipeline for robots to learn to assemble a diverse set of chairs. Experiments show that when testing with unseen chairs, our approach achieves a success rate of 74.5% under the object-centric setting and 50.0% under the full setting. We adopt an RRT-Connect algorithm as the baseline, which only achieves a success rate of 18.8% after a significantly longer computation time. Supplemental materials and videos are available on our project webpage.
Performance analysis of SDN controllers: POX, Floodlight and Opendaylight

The IP network is time-consuming for configuration and troubleshooting because it requires access to every device command line interface (CLI) or Graphical User Interface (GUI). In other words, the control plane (gathering information to forward data) and data plane (data forwarding plane) are run on every intermediary device. To solve this problem, the software defined network emerged and separated the control plane (done by software) from the data plane (done by hardware) [1]. In addition, the control plane is operated in the central point named the controller. There are many controller software programs in simulation and production network environments. In this paper, we compare three open source controllers called POX, Floodlight and Opendaylight (ODL) in simulation network created by MININET SDN emulation program in terms of TCP and UDP throughput and average Round Trip Time (RTT) of the first packet of the flow in the mesh and tree topology.
Time-Aware Neighbor Sampling for Temporal Graph Networks

We present a new neighbor sampling method on temporal graphs. In a temporal graph, predicting different nodes' time-varying properties can require the receptive neighborhood of various temporal scales. In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time. Learning how to sample neighbors is non-trivial, since the neighbor indices in time order are discrete and not differentiable. To address this challenge, we transform neighbor indices from discrete values to continuous ones by interpolating the neighbors' messages. TNS can be flexibly incorporated into popular temporal graph networks to improve their effectiveness without increasing their time complexity. TNS can be trained in an end-to-end manner. It needs no extra supervision and is automatically and implicitly guided to sample the neighbors that are most beneficial for prediction. Empirical results on multiple standard datasets show that TNS yields significant gains on edge prediction and node classification.
A Predictive Autonomous Decision Aid for Calibrating Human-Autonomy Reliance in Multi-Agent Task Assignment

In this work, we develop a game-theoretic modeling of the interaction between a human operator and an autonomous decision aid when they collaborate in a multi-agent task allocation setting. In this setting, we propose a decision aid that is designed to calibrate the operator's reliance on the aid through a sequence of interactions to improve overall human-autonomy team performance. The autonomous decision aid employs a long short-term memory (LSTM) neural network for human action prediction and a Bayesian parameter filtering method to improve future interactions, resulting in an aid that can adapt to the dynamics of human reliance. The proposed method is then tested against a large set of simulated human operators from the choice prediction competition (CPC18) data set, and shown to significantly improve human-autonomy interactions when compared to a myopic decision aid that only suggests predicted human actions without an understanding of reliance.