Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rotation-Strain Method to Model Surfaces using Plasticity

Modeling arbitrarily large deformations of surfaces smoothly embedded in three-dimensional space is challenging. The difficulties come from two aspects: the existing geometry processing or forward simulation methods penalize the difference between the current status and the rest configuration to maintain the initial shape, which will lead to sharp spikes or wiggles for large deformations; the co-dimensional nature of the problem makes it more complicated because the deformed surface has to locally satisfy compatibility conditions on fundamental forms to guarantee a feasible solution exists. To address these two challenges, we propose a rotation-strain method to modify the fundamental forms in a compatible way, and model the large deformation large strains. The muscle MRI data was provided by the project [Wang et al. 2021]. In contrast to [Wang et al. 2021], our method does not need a tetrahedral mesh and operates directly on the surface DOFs, whose number is substantially smaller than that of the volumetric mesh. We experimentally compared the performance: our method is 6 × faster in the running time per iteration, and an overall 3 × faster than [Wang et al. 2021] to produce a result of equivalent quality.
Intersecting diametral balls induced by a geometric graph

For a graph whose vertex set is a finite set of points in the Euclidean $d$-space consider the closed (open) balls with diameters induced by its edges. The graph is called a (an open) Tverberg graph if these closed (open) balls intersect. Using the idea of halving lines, we show that ($i$) for any finite set of points in the plane, there exists a Hamiltonian cycle that is a Tverberg graph; ($ii$) for any $ n $ red and $ n $ blue points in the plane, there exists a perfect red-blue matching that is a Tverberg graph. Also, we prove that ($iii$) for any even set of points in the Euclidean $ d $-space, there exists a perfect matching that is an open Tverberg graph; ($iv$) for any $ n $ red and $ n $ blue points in the Euclidean $ d $-space, there exists a perfect red-blue matching that is a Tverberg graph.
A time-to-first-spike coding and conversion aware training for energy-efficient deep spiking neural network processor design

In this paper, we present an energy-efficient SNN architecture, which can seamlessly run deep spiking neural networks (SNNs) with improved accuracy. First, we propose a conversion aware training (CAT) to reduce ANN-to-SNN conversion loss without hardware implementation overhead. In the proposed CAT, the activation function developed for simulating SNN during ANN training, is efficiently exploited to reduce the data representation error after conversion. Based on the CAT technique, we also present a time-to-first-spike coding that allows lightweight logarithmic computation by utilizing spike time information. The SNN processor design that supports the proposed techniques has been implemented using 28nm CMOS process. The processor achieves the top-1 accuracies of 91.7%, 67.9% and 57.4% with inference energy of 486.7uJ, 503.6uJ, and 1426uJ to process CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively, when running VGG-16 with 5bit logarithmic weights.
Accelerated Lifetime Test of The SRF Dressed Cavity/Tuner System for LCLS II HE Project

The off-frequency detune method is being considered for application in the LCLS-II-HE superconducting linac to produce multi-energy electron beams for supporting multiple undulator lines simultaneously [1]. Design of the tuner has been changed to deliver roughly 3 times larger frequency tuning range. Working requirements for off-frequency operation (OFO) state that cavities be tuned at least twice a month. This specification requires the in-crease of the tuner longevity by 20 times compared with LCLS-II demands. Accelerated longevity tests of the LCLS-II HE dressed cavity with tuner were conducted at FNAL's HTS. Detail analysis of wearing and impacts on performances of the tuner's piezo and stepper motor actuators will be presented. Additionally, results of longevity testing of the dressed cavity bellow, when cooled down to 2 K and compressed by 2.6 mm for roughly 2000 cycles, will be presented.
Adaptive kernel density estimation proposal in gravitational wave data analysis

Markov Chain Monte Carlo approach is frequently used within Bayesian framework to sample the target posterior distribution. Its efficiency strongly depends on the proposal used to build the chain. The best jump proposal is the one that closely resembles the unknown target distribution, therefore we suggest an adaptive proposal based on Kernel Density Estimation (KDE). We group parameters of the model according to their correlation and build KDE based on the already accepted points for each group. We adapt the KDE-based proposal until it stabilizes. We argue that such a proposal could be helpful in applications where the data volume is increasing and in the hyper-model sampling. We tested it on several astrophysical datasets (IPTA and LISA) and have shown that in some cases KDE-based proposal also helps to reduce the autocorrelation length of the chains. The efficiency of this proposal is reduces in case of the strong correlations between a large group of parameters.
A Theoretical View on Sparsely Activated Networks

Deep and wide neural networks successfully fit very complex functions today, but dense models are starting to be prohibitively expensive for inference. To mitigate this, one promising direction is networks that activate a sparse subgraph of the network. The subgraph is chosen by a data-dependent routing function, enforcing a fixed mapping of inputs to subnetworks (e.g., the Mixture of Experts (MoE) paradigm in Switch Transformers). However, prior work is largely empirical, and while existing routing functions work well in practice, they do not lead to theoretical guarantees on approximation ability. We aim to provide a theoretical explanation for the power of sparse networks. As our first contribution, we present a formal model of data-dependent sparse networks that captures salient aspects of popular architectures. We then introduce a routing function based on locality sensitive hashing (LSH) that enables us to reason about how well sparse networks approximate target functions. After representing LSH-based sparse networks with our model, we prove that sparse networks can match the approximation power of dense networks on Lipschitz functions. Applying LSH on the input vectors means that the experts interpolate the target function in different subregions of the input space. To support our theory, we define various datasets based on Lipschitz target functions, and we show that sparse networks give a favorable trade-off between number of active units and approximation quality.
Bounding entanglement dimensionality from the covariance matrix

High-dimensional entanglement has been identified as an important resource in quantum information processing, and also as a main obstacle for simulating quantum systems. Its certification is often difficult, and most widely used methods for experiments are based on fidelity measurements with respect to highly entangled states. Here, instead, we consider covariances of collective observables, as in the well-known Covariance Matrix Criterion (CMC) \cite{guhnecova} and present a generalization of the CMC for determining the Schmidt number of a bipartite system. This is potentially particularly advantageous in many-body systems, such as cold atoms, where the set of practical measurements is very limited and only variances of collective operators can typically be estimated. To show the practical relevance of our results, we derive simpler Schmidt-number criteria that require similar information as the fidelity-based witnesses, yet can detect a wider set of states. We also consider paradigmatic criteria based on spin covariances, which would be very helpful for experimental detection of high-dimensional entanglement in cold atom systems. We conclude by discussing the applicability of our results to a multiparticle ensemble and some open questions for future work.
Acceleration of an electron bunch with a non-Gaussian transverse profile in a quasilinear plasma wakefield

Beam-driven plasma wakefield accelerators typically use the external injection scheme to ensure controllable beam quality at injection. However, the externally injected witness bunch may exhibit a non-Gaussian transverse density distribution. Using particle-in-cell simulations, we show that the common beam quality factors, such as the normalized RMS emittance and beam radius, do not strongly depend on the initial transverse shapes of the witness beam. Nonetheless, a beam with a highly-peaked transverse spatial profile can achieve a higher fraction of the total beam charge in the core. The same effect can be seen when the witness beam's transverse momentum profile has a peaked non-Gaussian distribution. In addition, we find that an initially non-axisymmetric beam becomes symmetric due to the interaction with the plasma wakefield, and so it does not cause a detrimental effect for the beam acceleration.
Holes with Dirichlet condition homogenized to a simple function potential

We consider solutions u ε of Poisson problems with Dirichlet condition on domains Ω ε with holes concentrated at subsets of a domain Ω non-periodically. We show u ε converges to a solution of Poisson problem with a simple function potential. This is a generalized result of a sample model given by Cioranescu and Murat (1997). They showed for case that holes are distributed at Ω periodically.
Combining Variational Modeling with Partial Gradient Perturbation to Prevent Deep Gradient Leakage

Exploiting gradient leakage to reconstruct supposedly private training data, gradient inversion attacks are an ubiquitous threat in collaborative learning of neural networks. To prevent gradient leakage without suffering from severe loss in model performance, recent work proposed a PRivacy EnhanCing mODulE (PRECODE) based on variational modeling as extension for arbitrary model architectures. In this work, we investigate the effect of PRECODE on gradient inversion attacks to reveal its underlying working principle. We show that variational modeling induces stochasticity on PRECODE's and its subsequent layers' gradients that prevents gradient attacks from convergence. By purposefully omitting those stochastic gradients during attack optimization, we formulate an attack that can disable PRECODE's privacy preserving effects. To ensure privacy preservation against such targeted attacks, we propose PRECODE with Partial Perturbation (PPP), as strategic combination of variational modeling and partial gradient perturbation. We conduct an extensive empirical study on four seminal model architectures and two image classification datasets. We find all architectures to be prone to gradient leakage, which can be prevented by PPP. In result, we show that our approach requires less gradient perturbation to effectively preserve privacy without harming model performance.
Integrating Connection Search in Graph Queries

When graph database users explore unfamiliar graphs, potentially with heterogeneous structure, users may need to find how two or more groups of nodes are connected in a graph, even when users are not able to describe the connections. This is only partially supported by existing query languages, which allow searching for paths, but not for trees connecting three or more node groups.In this work, we formally show how to integrate connecting tree patterns (CTPs, in short) with a graph query language such as GPML [1], SPARQL or Cypher, leading to Extended Queries (or EQs, in short). We then study a set of algorithms for evaluating CTPs; we generalize prior keyword search work to be complete, most importantly by (i) considering bidirectional edge traversal, (ii) allowing users to select any score function for ranking CTP results and (iii) returning all results. To cope with very large search spaces, we propose efficient pruning techniques and formally establish a large set of cases where our best algorithm, MOLESP, is complete even with pruning. Our experiments validate the performance of our algorithms on many synthetic and real-world workloads.
Deep Patch Visual Odometry

We propose Deep Patch Visual Odometry (DPVO), a new deep learning system for monocular Visual Odometry (VO). DPVO uses a novel recurrent network architecture designed for tracking image patches across time. Recent approaches to VO have significantly improved the state-of-the-art accuracy by using deep networks to predict dense flow between video frames. However, using dense flow incurs a large computational cost, making these previous methods impractical for many use cases. Despite this, it has been assumed that dense flow is important as it provides additional redundancy against incorrect matches. DPVO disproves this assumption, showing that it is possible to get the best accuracy and efficiency by exploiting the advantages of sparse patch-based matching over dense flow. DPVO introduces a novel recurrent update operator for patch based correspondence coupled with differentiable bundle adjustment. On Standard benchmarks, DPVO outperforms all prior work, including the learning-based state-of-the-art VO-system (DROID) using a third of the memory while running 3x faster on average. Code is available at https://github.com/princeton-vl/DPVO
Costly Evidence and Discretionary Disclosure

A sender ﬂexibly acquires evidence–which she may pay a third party to certify– to disclose to a receiver. When evidence acquisition is overt, the receiver observes the evidence gathering process irrespective of whether its outcome is certiﬁed. When acquisition is covert, the receiver does not. In contrast to the case with exogenous evidence, the receiver prefers a strictly positive certiﬁcation cost. As acquisition costs vanish, equilibria converge to the Pareto-worst free-learning equilibrium. The receiver always prefers covert to overt evidence acquisition.
Sports Video Analysis on Large-Scale Data

This paper investigates the modeling of automated machine description on sports video, which has seen much progress recently. Nevertheless, state-of-the-art approaches fall quite short of capturing how human experts analyze sports scenes. There are several major reasons: (1) The used dataset is collected from non-official providers, which naturally creates a gap between models trained on those datasets and real-world applications; (2) previously proposed methods require extensive annotation efforts (i.e., player and ball segmentation at pixel level) on localizing useful visual features to yield acceptable results; (3) very few public datasets are available. In this paper, we propose a novel large-scale NBA dataset for Sports Video Analysis (NSVA) with a focus on captioning, to address the above challenges. We also design a unified approach to process raw videos into a stack of meaningful features with minimum labelling efforts, showing that cross modeling on such features using a transformer architecture leads to strong performance. In addition, we demonstrate the broad application of NSVA by addressing two additional tasks, namely fine-grained sports action recognition and salient player identification. Code and dataset are available at https://github.com/jackwu502/NSVA.
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints

The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-{10, 100}, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance.
Trace formula for the magnetic Laplacian at zero energy level

The paper is devoted to the trace formula for the magnetic Laplacian associated with a magnetic system on a compact manifold. This formula is a natural generalization of the semiclassical Gutzwiller trace formula and reduces to it in the case when the magnetic ﬁeld form is exact. It diﬀers somewhat from the Guillemin-Uribe trace formula studied in the author’s previous work with I.A. Taimanov. Moreover, in contrast to that work, the focus is on the trace formula at the zero energy level, which is a critical energy level. The paper gives an overview of the main notions and results related to the trace formula at the zero energy level, describes various approaches to its proof, and gives concrete examples of its computation. In addition, a brief review of the Gutzwiller trace formula for regular and critical energy levels is given.
Stochastic versus Periodic Quantum Collision Models

Most literature on quantum collision models (CMs) usually considers periodic weak collisions featuring a fixed waiting time between two next collisions. Some works have yet addressed CMs with random waiting time and strong collisions (stochastic CMs). This short paper discusses how the open dynamics arising from these two types of models can be formally mapped with one another. This can be achieved for a given stochastic CM by constructing an associated periodic CM such that the waiting time randomness of the former is turned into the mixedness of the ancilla’s initial state of the latter, introducing at the same time an additional state of the ancilla. Through this mapping, non-Markovian behaviour arising from a constrained number of stochatic collisions can be linked to initial ancilla–ancilla correlations of the associated periodic CM.
Charge excitations across a superconductor-insulator transition

We study the superconductor-insulator transition (SIT) in the ground state of the attractive honeycomb Hubbard model in the presence of a staggered potential (a mass term), using a combination of unbiased computational methods, namely, exact diagonalization and quantum Monte Carlo simulations. We probe the nature of the lowest-energy charge excitations across the SIT and show that they are bosonic, as inferred (and shown in the strongly interacting regime) in a previous study of the same model in the square lattice. Increasing the strength of the staggered potential leads to a crossover in which bosonic low-energy excitations give way to fermionic ones within the insulating phase. We also show that the SIT belongs to the 3$d$-XY universality class, like in its square lattice counterpart. The robustness of our results in these two lattice geometries supports the expectation that our findings are universal for SITs in clean systems.
Kinematics of the Hα and Hβ broad line region in an SDSS sample of type 1 AGNs

Here we investigate the kinematics of the part of the broad line region (BLR) in active galactic nuclei (AGNs) emitting Hβ and Hα emission lines. We explore the widths and asymmetries of the broad Hβ and Hα emission lines in a sample of high quality (i.e. high signal to noise ratio) spectra of type 1 AGN taken from the Data Release 16 of the Sloan Digital Sky Survey, in order to explore possible deviation from the gravitationally bound motion. To find only the broad component of Hβ and Hα we use the FANTASY (Fully Automated pythoN Tool for AGN Spectra analYsis) code for the multi-component modeling of the AGN spectra and for careful extraction of the broad emission line parameters. We show that based on the broad line profiles widths and asymmetries, the BLR gas emitting Hβ and Hα lines follows similar kinematics, and seems to be virialized in our sample of type 1 AGN.
NRBdMF: A recommendation algorithm for predicting drug effects considering directionality

Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems, and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive (+1) label and known treatment effects were assigned a negative (-1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.
Kepler and TESS Observations of PG 1159-035

PG 1159-035 is the prototype of the PG 1159 hot (pre-)white dwarf pulsators. This important object was observed during the Kepler satellite K2 mission for 69 days in 59 s cadence mode and by the TESS satellite for 25 days in 20 s cadence mode. We present a detailed asteroseismic analysis of those data. We identify a total of 107 frequencies representing 32 ℓ = 1 modes, 27 frequencies representing 12 ℓ = 2 modes, and eight combination frequencies. The combination frequencies and the modes with very high k values represent new detections. The multiplet structure reveals an average splitting of 4.0 ± 0.4 μHz for ℓ = 1 and 6.8 ± 0.2 μHz for ℓ = 2, indicating a rotation period of 1.4 ± 0.1 days in the region of period formation. In the Fourier transform of the light curve, we find a significant peak at 8.904 ± 0.003 μHz suggesting a surface rotation period of 1.299 ± 0.002 days. We also present evidence that the observed periods change on timescales shorter than those predicted by current evolutionary models. Our asteroseismic analysis finds an average period spacing for ℓ = 1 of 21.28 ± 0.02 s. The ℓ = 2 modes have a mean spacing of 12.97 ± 0.4 s. We performed a detailed asteroseismic fit by comparing the observed periods with those of evolutionary models. The best-fit model has T eff = 129, 600 ± 11 100 K, M * = 0.565 ± 0.024M ⊙, and logg=7.41−0.54+0.38 , within the uncertainties of the spectroscopic determinations. We argue for future improvements in the current models, e.g., on the overshooting in the He-burning stage, as the best-fit model does not predict excitation for all of the pulsations detected in PG 1159-035.