Miracle-dz/UniFAR-FTUs huggingface dataset:
Step-by-step dimensional crossover of an optically-trapped quantum gas with disorder

Dimensionality serves as an indispensable ingredient to any attempt of formulating the low-dimensional physics, and studying dimensional crossover on a fundamental level is challenging. The purpose of this work is to study the step-by-step dimensional crossover from three-dimension (3D) to quasi-2D and then 1D. Our model system consists of a 3D Bose-Einstein condensate (BEC) trapped in an anisotropic 2D optical lattice characterized by the lattice depths of V 1 along x -direction and V 2 along y -direction respectively. The gradually dimensional crossover is controlled by the continuous increase of V 1 and V 2 . Then we analyze the combined eﬀects of dimensionality and disorder on both equilibrium and non-equilibrium quantum ﬂuctuations of the model system. Accordingly, the analytical expressions of the ground-state energy, quantum depletion and superﬂuid density of the system are obtained. Our results show that the step-by-step dimensional crossover induces a characteristic 3D to quasi-2D and 1D step-by crossover in the behavior of quantum ﬂuctuations. Conditions for possible experimental realization of our scenario are also discussed.
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce

In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for widespread applications such as product search and recommendation. For example, when users search for ``running'' in e-commerce, they would like to find products highly related to running, such as ``running shoes'' rather than ``shoes''. Nevertheless, many existing CSK collections rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective. In this work, we define the task of supervised salience evaluation, where given a CSK triple, the model is required to learn whether the triple is salient or not. In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation. We conduct experiments in the dataset with several representative baseline models. The experimental results show that salience evaluation is a challenging task where models perform poorly on our evaluation set. We further propose a simple but effective approach, PMI-tuning, which shows promise for solving this novel problem. Code is available in \url{https://github.com/OpenBGBenchmark/OpenBG-CSK.
Using General-Purpose Serial-Link Manipulators for Laparoscopic Surgery with Moving Remote Center of Motion

Minimally invasive surgical systems are being widely used to aid operating rooms across the globe. Although arguably successful in laparoscopic surgery, the da Vinci robotic system has limitations mostly regarding cost and lack of patient physiological motion compensation. To obtain a more cost-effective alternative, earlier works used general-purpose fully actuated serial-link robots to control instruments in laparoscopic research using constrained Jacobian techniques. In contrast with those works, we present a new technique to solve the laparoscopic constraints for the serial-link manipulator by using a constrained trajectory. This novel technique allows complex 3D remote center-of-motion trajectories to be taken into account. Moreover, it does not have problems related to drifting, and is less prone to singularity related issues as it can be used with redundant manipulators. The proof-of-concept experiments are done by performing artificial trajectories with static and moving trocar points using a physical robot manipulator. Furthermore, the system is tested using user input of 13 medically untrained personnel in an endoscope navigation task. The experiments show that the system can be operated reliably under arbitrary and unpredictable user inputs.
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum

Adaptive Moment Estimation (Adam), which combines Adaptive Learning Rate and Momentum, would be the most popular stochastic optimizer for accelerating the training of deep neural networks. However, it is empirically known that Adam often generalizes worse than Stochastic Gradient Descent (SGD). The purpose of this paper is to unveil the mystery of this behavior in the diffusion theoretical framework. Specifically, we disentangle the effects of Adaptive Learning Rate and Momentum of the Adam dynamics on saddle-point escaping and flat minima selection. We prove that Adaptive Learning Rate can escape saddle points efficiently, but cannot select flat minima as SGD does. In contrast, Momentum provides a drift effect to help the training process pass through saddle points, and almost does not affect flat minima selection. This partly explains why SGD (with Momentum) generalizes better, while Adam generalizes worse but converges faster. Furthermore, motivated by the analysis, we design a novel adaptive optimization framework named Adaptive Inertia, which uses parameter-wise adaptive inertia to accelerate the training and provably favors flat minima as well as SGD. Our extensive experiments demonstrate that the proposed adaptive inertia method can generalize significantly better than SGD and conventional adaptive gradient methods.
Group draw with unknown qualified teams: A lesson from the 2022 FIFA World Cup draw

The draw for the 2022 FIFA World Cup has been organised before the identity of three winners of the play-offs is revealed. Seeding has been based on the FIFA World Ranking released on 31 March 2022 but these three teams have been drawn from the weakest Pot 4. We show that the official seeding policy does not balance the difficulty levels of the groups to the extent possible: a better alternative would have been to assign the placeholders according to the highest-ranked potential winner, similar to the rule used in the UEFA Champions League qualification. Our simulations reinforce that this is the best strategy in general to create balanced groups in the FIFA World Cup.
Reconstructing Decision Trees

We give the first reconstruction algorithm for decision trees: given queries to a function f that is opt-close to a size- s decision tree, our algorithm provides query access to a decision tree T where: T has size S := s O ((log s ) 2 /ε 3 ) ; dist( f, T ) ≤ O (opt) + ε ; Every query to T is answered with poly((log s ) /ε ) · log n queries to f and in poly((log s ) /ε ) · n log n time. This yields a tolerant tester that distinguishes functions that are close to size- s decision trees from those that are far from size- S decision trees. The polylogarithmic dependence on s in the efficiency of our tester is exponentially smaller than that of existing testers. Since decision tree complexity is well known to be related to numerous other boolean function properties, our results also provide a new algorithm for reconstructing and testing these properties.
A Geometric Understanding of Natural Gradient

While natural gradients have been widely studied from both theoretical and empirical perspectives, we argue that some fundamental theoretical issues regarding the existence of gradients in infinite dimensional function spaces remain underexplored. We address these issues by providing a geometric perspective and mathematical framework for studying natural gradient that is more complete and rigorous than existing studies. Our results also establish new connections between natural gradients and RKHS theory, and specifically to the Neural Tangent Kernel (NTK). Based on our theoretical framework, we derive a new family of natural gradients induced by Sobolev metrics and develop computational techniques for efficient approximation in practice. Preliminary experimental results reveal the potential of this new natural gradient variant.
Centrosome linker protein C‐Nap1 maintains stem cells in mouse testes

The centrosome linker component C‐Nap1 (encoded by CEP250) anchors filaments to centrioles that provide centrosome cohesion by connecting the two centrosomes of an interphase cell into a single microtubule organizing unit. The role of the centrosome linker during development of an animal remains enigmatic. Here, we show that male CEP250−/− mice are sterile because sperm production is abolished. Premature centrosome separation means that germ stem cells in CEP250−/− mice fail to establish an E‐cadherin polarity mark and are unable to maintain the older mother centrosome on the basal site of the seminiferous tubules. This failure prompts premature stem cell differentiation in expense of germ stem cell expansion. The concomitant induction of apoptosis triggers the complete depletion of germ stem cells and consequently infertility. Our study reveals a role for centrosome cohesion in asymmetric cell division, stem cell maintenance, and fertility.
Energy of anti–de Sitter black holes in odd-dimensional quadratic curvature gravity

We provide new formulas for the energy of black hole solutions in the anti-de Sitter (AdS) sector of a generic gravity theory with curvature-squared terms. This is achieved by the addition of counterterms of the extrinsic type ( Kounterterms ) which produces a ﬁnite variation of the total action. The procedure is compatible with the boundary conditions in asymptotically AdS spaces within the framework of Quadratic Curvature Gravity (QCG) theory. As a consequence, without resorting to background-substraction, or linearized methods, the conserved charges correctly reproduce the energy of static black holes in QCG.
eBIM-GNN Fast and Scalable energy analysis through BIMs and Graph Neural Networks

Building Information Modeling has been used to analyze as well as increase the energy efficiency of the buildings. It has shown significant promise in existing buildings by deconstruction and retrofitting. Current cities which were built without the knowledge of energy savings are now demanding better Ways to become smart in energy utilization. However, the existing methods of generating BIMs work on building basis. Hence they are slow and expensive When we scale to a larger community or even entire towns or cities. In this paper, we propose a method to creation of prototype buildings that enable us to match and generate statistics very efficiently. Our method suggests better energy efficient prototypes for the existing buildings. The existing buildings are identified and located in the 3D point cloud. We perform experiments on synthetic dataset to demonstrate the working of our approach.
Arbitrary Reduction of MRI Slice Spacing Based on Local-Aware Implicit Representation

Magnetic resonance (MR) images are often acquired in 2D settings for real clinical applications. The 3D volumes reconstructed by stacking multiple 2D slices have large inter-slice spacing, resulting in lower inter-slice resolution than intra-slice resolution. Super-resolution is a powerful tool to reduce the inter-slice spacing of 3D images to facilitate subsequent visualization and computation tasks. However, most existing works train the super-resolution network at a fixed ratio, which is inconvenient in clinical scenes due to the heterogeneous parameters in MR scanning. In this paper, we propose a single super-resolution network to reduce the inter-slice spacing of MR images at an arbitrarily adjustable ratio. Specifically, we view the input image as a continuous implicit function of coordinates. The intermediate slices of different spacing ratios could be constructed according to the implicit representation up-sampled in the continuous domain. We particularly propose a novel local-aware spatial attention mechanism and long-range residual learning to boost the quality of the output image. The experimental results demonstrate the superiority of our proposed method, even compared to the models trained at a fixed ratio.
Interpretable Proof Generation via Iterative Backward Reasoning

We present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual rules and facts to find out the related proof path and derive the final answer. We handle the limitations of existed works in two folds: 1) enhance the interpretability of reasoning procedures with detailed tracking, by predicting nodes and edges in the proof path iteratively backward from the question; 2) promote the efficiency and accuracy via reasoning on the elaborate representations of nodes and history paths, without any intermediate texts that may introduce external noise during proof generation. There are three main modules in IBR, QA and proof strategy prediction to obtain the answer and offer guidance for the following procedure; parent node prediction to determine a node in the existing proof that a new child node will link to; child node prediction to find out which new node will be added to the proof. Experiments on both synthetic and paraphrased datasets demonstrate that IBR has better in-domain performance as well as cross-domain transferability than several strong baselines. Our code and models are available at https://github. com/find-knowledge/IBR.
Parker Solar Probe Observations of Near-f Ce Harmonic Emissions in the Near-Sun Solar Wind and Their Dependence on the Magnetic Field Direction

Wave emissions at frequencies near electron gyrofrequency harmonics are observed at small heliocentric distances below about 40 R ☉ and are known to occur in regions with quiescent magnetic fields. We show the close connection of these waves to the large-scale properties of the magnetic field. Near electron gyrofrequency harmonic emissions occur only when the ambient magnetic field points to a narrow range of directions bounded by polar and azimuthal angular ranges in the RTN coordinate system of correspondingly 80° ≲ θ B ≲ 100° and 10° ≲ ϕ B ≲ 30°. We show that the amplitudes of wave emissions are highest when both angles are close to the center of their respective angular interval favorable to wave emissions. The intensity of wave emissions correlates with the magnetic field angular changes at both large and small timescales. Wave emissions intervals correlate with intervals of decreases in the amplitudes of broadband magnetic fluctuations at low frequencies of 10–100 Hz. We discuss possible generation mechanisms of the waves.
Sparse Modeling Approach to the Arbitrage-Free Interpolation of Plain-Vanilla Option Prices and Implied Volatilities

We present a method for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, which is based on a system of integral equations that relates terminal density and option prices. Using a discretization of the terminal density, we write these integral equations as a system of linear equations. We show that the kernel matrix of this system is, in general, ill-conditioned, so that it cannot be solved for the discretized density using a naive approach. Instead, we construct a sparse model for the kernel matrix using singular value decomposition (SVD), which allows us not only to systematically improve the condition number of the kernel matrix, but also determines the computational effort and accuracy of our method. In order to allow for the treatment of realistic inputs that may contain arbitrage, we reformulate the system of linear equations as an optimization problem, in which the SVD-transformed density minimizes the error between the input prices and the arbitrage-free prices generated by our method. To further stabilize the method in the presence of noisy input prices or arbitrage, we apply an L1-regularization to the SVD-transformed density. Our approach, which is inspired by recent progress in theoretical physics, offers a flexible and efficient framework for the arbitrage-free interpolation of plain-vanilla option prices and implied volatilities, without the need to explicitly specify a stochastic process, expansion basis functions or any other kind of model. We demonstrate the capabilities of our method in a number of artificial and realistic test cases.
Learning Multi-Expert Distribution Calibration for Long-Tailed Video Classification

Most existing state-of-the-art video classification methods assume that the training data obey a uniform distribution. However, video data in the real world typically exhibit an imbalanced long-tailed class distribution, resulting in a model bias towards head class and relatively low performance on tail class. While the current long-tailed classification methods usually focus on image classification, adapting them to video data is not a trivial extension. We propose an end-to-end multi-expert distribution calibration method to address these challenges based on two-level distribution information. The method jointly considers the distribution of samples in each class (intra-class distribution) and the overall distribution of diverse data (inter-class distribution) to solve the issue of imbalanced data under long-tailed distribution. By modeling the two-level distribution information, the model can jointly consider the head classes and the tail classes and significantly transfer the knowledge from the head classes to improve the performance of the tail classes. Extensive experiments verify that our method achieves state-of-the-art performance on the long-tailed video classification task.
Linking Distributed and Integrated Fiber‐Optic Sensing

Distributed Acoustic Sensing (DAS) has become a popular method of observing seismic wavefields: backscattered pulses of light reveal strains or strain rates at any location along a fiber‐optic cable. In contrast, a few newer systems transmit light through a cable and collect integrated phase delays over the entire cable, such as the Microwave Frequency Fiber Interferometer (MFFI). These integrated systems can be deployed over significantly longer distances, may be used in conjunction with live telecommunications, and can be significantly cheaper. However, they provide only a single time series representing strain over the entire length of the fiber. This work discusses theoretically how a distributed and integrated system can be quantitatively compared, and we note that the sensitivity depends strongly on points of curvature. Importantly, this work presents the first results of a quantitative, head‐to‐head comparison of a DAS and the integrated MFFI system using pre‐existing telecommunications fibers in Athens, Greece.
Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network

— The provision of paratransit services is one option to meet the transportation needs of Vulnerable Road Users (VRUs). Like any other means of transportation, paratransit has obstacles such as high operational costs and longer trip times. As a result, customers are dissatisfied, and paratransit operators have a low approval rating. Researchers have undertaken various studies over the years to better understand the travel behaviors of paratransit customers and how they are operated. According to the findings of these research, paratransit operators confront the challenge of determining the optimal route for their trips in order to save travel time. Depending on the nature of the challenge, most research used different optimization techniques to solve these routing problems. As a result, the goal of this study is to use Graph Convolutional Neural Networks (GCNs) to assist paratransit operators in researching various operational scenarios in a strategic setting in order to optimize routing, minimize operating costs and minimize their users' travel time. The study was carried out by using a randomized simulated dataset to help determine the decision to make in terms of fleet composition and capacity under different situations. For the various scenarios investigated, the GCN assisted in determining the minimum optimal gap.
ALPINE: A set of performance portable plasma physics particle-in-cell mini-apps for exascale computing

Alpine consists of a set of mini-apps that makes use of exascale computing capabilities to numerically solve some classical problems in plasma physics. It is based on IPPL (Independent Parallel Particle Layer), a framework that is designed around performance portable and dimension independent particles and ﬁelds. In this work, IPPL is used to implement a particle-in-cell scheme. The article describes in detail the following mini-apps: weak and strong Landau damping, bump-on-tail and two-stream instabilities, and the dynamics of an electron bunch in a charge-neutral Penning trap. We benchmark the simulations with varying parameters such as grid resolutions (512 3 to 2048 3 ) and number of simulation particles (10 9 to 10 11 ). We show strong and weak scaling and analyze the performance of diﬀerent components on several pre-exascale architectures such as Piz-Daint, Cori, Summit and Perlmutter.
Experimental observation of violent relaxation and the formation of out-of-equilibrium quasi-stationary states

Large scale structures in the Universe, ranging from globular clusters to entire galaxies, are the manifestation of relaxation to out-of-equilibrium states that are not described by standard statistical mechanics at equilibrium. Instead, they are formed through a process of a very different nature, i.e. violent relaxation. However, astrophysical time-scales are so large that it is not possible to directly observe these relaxation dynamics and therefore verify the details of the violent relaxation process. We develop a table-top experiment and model that allows us to directly observe effects such as mixing of phase space, and violent relaxation, leading to the formation of a table-top analogue of a galaxy. The experiment allows us to control a range of parameters, including the nonlocal (gravitational) interaction strength and quantum effects, thus providing an effective test-bed for gravitational models that cannot otherwise be directly studied in experimental settings.
Variable-Input Deep Operator Networks

Existing architectures for operator learning require that the number and locations of sensors (where the input functions are evaluated) remain the same across all training and test samples, significantly restricting the range of their applicability. We address this issue by proposing a novel operator learning framework, termed Variable-Input Deep Operator Network (VIDON), which allows for random sensors whose number and locations can vary across samples. VIDON is invariant to permutations of sensor locations and is proved to be universal in approximating a class of continuous operators. We also prove that VIDON can efficiently approximate operators arising in PDEs. Numerical experiments with a diverse set of PDEs are presented to illustrate the robust performance of VIDON in learning operators.