Miracle-dz/UniFAR-FTUs huggingface dataset:
Emergence of an ancient and pathogenic mammarenavirus

ABSTRACT Emerging zoonoses of wildlife origin caused by previously unknown agents are one of the most important challenges for human health. The Qinghai-Tibet Plateau represents a unique ecological niche with diverse wildlife that harbours several human pathogens and numerous previously uncharacterized pathogens. In this study, we identified and characterized a novel arenavirus (namely, plateau pika virus, PPV) from plateau pikas (Ochotona curzoniae) on the Qinghai-Tibet Plateau by virome analysis. Isolated PPV strains could replicate in several mammalian cells. We further investigated PPV pathogenesis using animal models. PPV administered via an intraventricular route caused trembling and sudden death in IFNαβR-/- mice, and pathological inflammatory lesions in brain tissue were observed. According to a retrospective serological survey in the geographical region where PPV was isolated, PPV-specific IgG antibodies were detected in 8 (2.4%) of 335 outpatients with available sera. Phylogenetic analyses revealed that this virus was clearly separated from previously reported New and Old World mammarenaviruses. Under the co-speciation framework, the estimated divergence time of PPV was 77–88 million years ago (MYA), earlier than that of OW and NW mammarenaviruses (26-34 MYA).
Computer Science and Control Systems

– An algorithm for search and retrieval of images from massive image collections is developed. The algorithm consists of two phases. The first phase uses well-known methods of image searching by descriptors based on the content of the searched image. In the second phase the progressive wavelet correlation method is applied on the small number of image candidates selected in previous search phase. The final search result is the wanted image, if it is in the data base. Experiments are performed with data bases of 1000 and 10 000 images .
Association of clear vs blue-light filtering intraocular lenses with mental and behavioral disorders and diseases of the nervous system among patients receiving bilateral cataract surgery

No associations of new-onset diagnoses were found for the use of blue-light filtering IOLs for health and neurological diseases after cataract surgery. Purpose: To analyze new-onset mental and behavioral disorders and nervous system diseases in patients with cataract implanted with either non–blue-light filtering (BLF) or BLF intraocular lenses (IOLs) in both eyes. Setting: Department of Ophthalmology, Kymenlaakso Central Hospital, Kotka, Finland. Design: A retrospective registry-based cohort study of patients operated between September 2007 and December 2018 who were followed until December 2021. We included 4986 patients who underwent bilateral cataract surgery. Methods: Patients were implanted with either non-BLF IOLs (N = 2609) or BLF IOLs (N = 2377) in both eyes. Follow-up before the first-eye surgery and between the first-eye and the second-eye surgery was performed to acknowledge the preexisting disorders and diseases. After the second-eye surgery, the groups were analyzed for the new-onset mental and behavioral disorders and diseases of the nervous system subcategorized by the International Classification of Diseases codes. Results: 1707 male and 3279 female patients, aged 73.2 ± 8.6 years at the first-eye surgery and 74.3 ± 8.8 years at the second-eye surgery, were identified. In univariate log-rank tests, the use of BLF IOLs showed no association in overall new-onset disorders or diseases over non-BLF IOLs, in any subtype diagnosis codes except for sleep disorders, which favored BLF IOLs (P = .003). A multivariate analysis adjusted for age and sex identified no associations in any new-onset disorders or diseases. Multivariate analysis of sleep disorders showed a nonsignificant advantage for BLF-IOLs (hazard ratio 0.756, 95% CI 0.534-1.070, P = .114). Conclusions: BLF IOLs were not associated with mental and behavioral disorders or diseases of the nervous system.
THE STRUCTURE OF THE SINGULAR SYMPLECTIC MATRIX SET

A symplectic matrix M is singular, if det(M-I)=0. In this paper we study the struc-ture of the singular set of symplectic mtrices. We discuss the changes of the dimension ofthe null space and the determinant of the difference between a singular symplectic matrixand the identity matrix under rotational perturbations. The results obtained will be used todefine a Maslov-type index theory for (degenerate) paths in symplectic groups, and thereforeto establish the existence of periodic solutions of asymptotically linear Hamiltonian systems.
UniFusion: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird’s-Eye-View

Bird’s eye view (BEV) representation is a new perception formulation for autonomous driving, which is based on spatial fusion. Further, temporal fusion is also introduced in BEV representation and gains great success. In this work, we propose a new method that unifies both spatial and temporal fusion and merges them into a unified mathematical formulation. The unified fusion could not only provide a new perspective on BEV fusion but also brings new capabilities. With the proposed unified spatial-temporal fusion, our method could support long-range fusion, which is hard to achieve in conventional BEV methods. Moreover, the BEV fusion in our work is temporal-adaptive and the weights of temporal fusion are learnable. In contrast, conventional methods mainly use fixed and equal weights for temporal fusion. Besides, the proposed unified fusion could avoid information lost in conventional BEV fusion methods and make full use of features. Extensive experiments and ablation studies on the NuScenes dataset show the effectiveness of the proposed method and our method gains the state-of-the-art performance in the map and vehicle segmentation task.
SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operations

Reverse engineering CAD models from raw geometry is a classic but strenuous research problem. Previous learning-based methods rely heavily on labels due to the supervised design patterns or reconstruct CAD shapes that are not easily editable. In this work, we introduce SECADNet, an end-to-end neural network aimed at reconstructing compact and easy-to-edit CAD models in a self-supervised manner. Drawing inspiration from the modeling language that is most commonly used in modern CAD software, we propose to learn 2D sketches and 3D extrusion parameters from raw shapes, from which a set of extrusion cylinders can be generated by extruding each sketch from a 2D plane into a 3D body. By incorporating the Boolean operation (i.e., union), these cylinders can be combined to closely approximate the target geometry. We advocate the use of implicit fields for sketch representation, which allows for creating CAD variations by interpolating latent codes in the sketch latent space. Extensive experiments on both ABC and Fusion 360 datasets demonstrate the effectiveness of our method, and show superiority over state-of-the-art alternatives including the closely related method for supervised CAD reconstruction. We further apply our approach to CAD editing and single-view CAD reconstruction. Code will be released at https://github.com/BunnySoCrazy/SECAD-Net.
Secondary Excitation of Spin Waves: How Electromagnetic Crosstalk Impacts Magnonic Devices

This work examines the impact of electromagnetic crosstalk in magnonic devices when using inductive spin-wave (SW) transducers. We present detailed electrical SW spectroscopy measurements showing the signal contributions to be considered in magnonic device design. We further provide a rule-of-thumb estimation for the crosstalk responsible for the secondary SW excitation at the output transducer. Simulations and calibrated electrical characterizations underpin this method. In addition, we visualize the secondary SW excitation via time-resolved magneto-optical Kerr effect (trMOKE) imaging in the forward-volume (FV) configuration in a 100 nm yttrium-iron-garnet (YIG) system.
The behaviour of positron clouds in the single-particle regime under the influence of rotating wall electric fields

Positron clouds are compressed following accumulation in a Surko-type two-stage buffer gas trap using an asymmetric rotating wall electric field. An analytic theory used to describe measurements of the rate of compression is discussed. Furthermore, we describe measurements taken without the rotating wall applied and with the rotating wall compression present during accumulation of the positron cloud. This has enabled total loss rates for the positrons via annihilation and collisional-induced radial transport to be isolated, with the latter mechanism found to be dominant. We have shown that the application of the rotating wall at a resonant frequency virtually eliminates radial transport, such that the positron loss is caused by annihilation in the gas.
Comparison of Non-deterministic Linear Systems by $(\gamma,\delta)$-Similarity

We introduce $(\gamma,\delta)$-similarity, a notion of system comparison that measures to what extent two stable linear dynamical systems behave similarly in an input-output sense. This behavioral similarity is characterized by measuring the sensitivity of the difference between the two output trajectories in terms of the external inputs to the two potentially non-deterministic systems. As such, $(\gamma,\delta)$-similarity is a notion that characterizes \emph{approximation} of input-output behavior, whereas existing notions of simulation target equivalence. Next, as this approximation is specified in terms of the $L_2$ signal norm, $(\gamma,\delta)$-similarity allows for integration with existing methods for analysis and synthesis of control systems, in particular, robust control techniques. We characterize the notion of $(\gamma,\delta)$-similarity as a linear matrix inequality feasibility problem and derive its interpretation in terms of transfer matrices. Our study on the compositional properties of $(\gamma,\delta)$-similarity shows that the notion is preserved through series and feedback interconnections. This highlights its potential application in compositional reasoning, namely abstraction and modular synthesis of large-scale interconnected dynamical systems. We further illustrate our results in an electrical network example.
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management

The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on developing dynamic inventory ordering policies for a multi-echelon, i.e. multi-stage, supply chain. Traditional inventory optimization methods aim to determine a static reordering policy. Thus, these policies are not able to adjust to dynamic changes such as those observed during the COVID-19 crisis. On the other hand, conventional strategies offer the advantage of being interpretable, which is a crucial feature for supply chain managers in order to communicate decisions to their stakeholders. To address this limitation, we propose an interpretable reinforcement learning approach that aims to be as interpretable as the traditional static policies while being as flexible and environment-agnostic as other deep learning-based reinforcement learning solutions. We propose to use Neural Additive Models as an interpretable dynamic policy of a reinforcement learning agent, showing that this approach is competitive with a standard full connected policy. Finally, we use the interpretability property to gain insights into a complex ordering strategy for a simple, linear three-echelon inventory supply chain.
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition. Our code is publicly available at https://github.com/wzh125/ABAW-5.
Estimation and inference for the Wasserstein distance between mixing measures in topic models

The Wasserstein distance between mixing measures has come to occupy a central place in the statistical analysis of mixture models. This work proposes a new canonical interpretation of this distance and provides tools to perform inference on the Wasserstein distance between mixing measures in topic models. We consider the general setting of an identifiable mixture model consisting of mixtures of distributions from a set $\mathcal{A}$ equipped with an arbitrary metric $d$, and show that the Wasserstein distance between mixing measures is uniquely characterized as the most discriminative convex extension of the metric $d$ to the set of mixtures of elements of $\mathcal{A}$. The Wasserstein distance between mixing measures has been widely used in the study of such models, but without axiomatic justification. Our results establish this metric to be a canonical choice. Specializing our results to topic models, we consider estimation and inference of this distance. Though upper bounds for its estimation have been recently established elsewhere, we prove the first minimax lower bounds for the estimation of the Wasserstein distance in topic models. We also establish fully data-driven inferential tools for the Wasserstein distance in the topic model context. Our results apply to potentially sparse mixtures of high-dimensional discrete probability distributions. These results allow us to obtain the first asymptotically valid confidence intervals for the Wasserstein distance in topic models.
Wave Propagation in Prestressed Curved Rods

The vibration and harmonic wave propagation in prestressed curved rods is theoretically studied. The equations of spatial elastic rods subjected to a generally deformation-dependent loading are employed and are specialized to prestressed planar circular arch equations. Torsional-lateral and extensional-flexural modes of motion of prestressed circular arches and rings are then investigated. The dispersion relations for each mode of motion are determined and are plotted for certain values of loading parameter. The loading considered in these problems consists of dead force, hydrostatic pressure, and centrally directed pressure loading.
Finite-strain Poynting-Thomson model: existence and linearization

We analyze the finite-strain Poynting-Thomson viscoelastic model. In its linearized small-deformation limit, this corresponds to the serial connection of an elastic spring and a Kelvin-Voigt viscoelastic element. In the finite-strain case, the total deformation of the body results from the composition of two maps, describing the deformation of the viscoelastic element and the elastic one, respectively. We prove the existence of suitably weak solutions by a time-discretization approach based on incremental minimization. Moreover, we prove a rigorous linearization result, showing that the corresponding small-strain model is indeed recovered in the small-loading limit.
Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence

This paper presents a new methodology to alleviate the fundamental trade-off between accuracy and latency in spiking neural networks (SNNs). The approach involves decoding confidence information over time from the SNN outputs and using it to develop a decision-making agent that can dynamically determine when to terminate each inference.The proposed method, Dynamic Confidence, provides several significant benefits to SNNs. 1. It can effectively optimize latency dynamically at runtime, setting it apart from many existing low-latency SNN algorithms. Our experiments on CIFAR-10 and ImageNet datasets have demonstrated an average 40% speedup across eight different settings after applying Dynamic Confidence. 2. The decisionmaking agent in Dynamic Confidence is straightforward to construct and highly robust in parameter space, making it extremely easy to implement. 3. The proposed method enables visualizing the potential of any given SNN, which sets a target for current SNNs to approach. For instance, if an SNN can terminate at the most appropriate time point for each input sample, a ResNet-50 SNN can achieve an accuracy as high as 82.47% on ImageNet within just 4.71 time steps on average. Unlocking the potential of SNNs needs a highly-reliable decision-making agent to be constructed and fed with a high-quality estimation of ground truth. In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs. Code is available1.
ANMS: Asynchronous Non-Maximum Suppression in Event Stream

The non-maximum suppression (NMS) is widely used in frame-based tasks as an essential post-processing algorithm. However, event-based NMS either has high computational complexity or leads to frequent discontinuities. As a result, the performance of event-based corner detectors is limited. This paper proposes a general-purpose asynchronous non-maximum suppression pipeline (ANMS), and applies it to corner event detection. The proposed pipeline extract fine feature stream from the output of original detectors and adapts to the speed of motion. The ANMS runs directly on the asynchronous event stream with extremely low latency, which hardly affects the speed of original detectors. Additionally, we evaluate the DAVIS-based ground-truth labeling method to fill the gap between frame and event. Evaluation on public dataset indicates that the proposed ANMS pipeline significantly improves the performance of three classical asynchronous detectors with negligible latency. More importantly, the proposed ANMS framework is a natural extension of NMS, which is applicable to other asynchronous scoring tasks for event cameras.
Why complex absorbing potentials work : a discrete-variable-representation perspective.

The use of a complex absorbing potential (CAP) of the form -i{eta}W to calculate the Siegert energy of a resonance state rests on a solid mathematical foundation [U. V. Riss and H. -D. Meyer, J. Phys. B 26, 4503 (1993)]. In this paper, in order to facilitate a better understanding of the basic principles underlying the CAP method, a radial one-particle Hamiltonian with a model potential supporting resonances is analyzed. Using a purely quadratic CAP [W(r)=r{sup 2}], the eigenstates of H=-(1/2)d{sup 2}/dr{sup 2}-i{eta}W(r) are employed to construct a discrete variable representation. The introduction of this grid method makes it transparent how using a CAP is related to the method of complex scaling, and why, in the limit of an infinite basis set, the exact Siegert energy may emerge in the spectrum as {eta}{yields}0{sup +}.
Efficient Map Sparsification Based on 2D and 3D Discretized Grids

Localization in a pre-built map is a basic technique for robot autonomous navigation. Existing mapping and localization methods commonly work well in small-scale environments. As a map grows larger, however, more memory is required and localization becomes inefficient. To solve these problems, map sparsification becomes a practical necessity to acquire a subset of the original map for localization. Previous map sparsification methods add a quadratic term in mixed-integer programming to enforce a uniform distribution of selected landmarks, which requires high memory capacity and heavy computation. In this paper, we formulate map sparsification in an efficient linear form and select uniformly distributed landmarks based on 2D discretized grids. Furthermore, to reduce the influence of different spatial distributions between the mapping and query sequences, which is not considered in previous methods, we also introduce a space constraint term based on 3D discretized grids. The exhaustive experiments in different datasets demonstrate the superiority of the proposed methods in both efficiency and localization performance. The relevant codes will be released at https://github.com/fishmarch/SLAM_Map_Compression.
An Evaluation of GPU Filters for Accelerating the 2D Convex Hull

The Convex Hull algorithm is one of the most important algorithms in computational geometry, with many applications such as in computer graphics, robotics, and data mining. Despite the advances in the new algorithms in this area, it is often needed to improve the performance to solve more significant problems quickly or in real-time processing. This work presents an experimental evaluation of GPU filters to reduce the cost of computing the 2D convex hull. The technique first performs a preprocessing of the input set, filtering all points within an eight-vertex polygon in logarithmic time, to obtain a reduced set of candidate points. We use parallel computation and the use of the Manhattan distance as a metric to find the vertices of the polygon and perform the point filtering. For the filtering stage we study different approaches; from custom CUDA kernels to libraries such as Thrust and CUB. Three types of point distributions are tested: a normal distribution (favorable case), circumference (the worst case), and a case where points are shifted randomly from the circumference (intermediate case). Experimental evaluation shows that the GPU filtering algorithm can be up to 23x faster than a sequential CPU implementation, and the whole convex hull computation can be up to 30x faster than the fastest implementation provided by the CGAL library.
Convergence analysis and acceleration of the smoothing methods for solving extensive-form games

The extensive-form game has been studied considerably in recent years. It can represent games with multiple decision points and incomplete information, and hence it is helpful in formulating games with uncertain inputs, such as poker. We consider an extended-form game with two players and zero-sum, i.e., the sum of their payoffs is always zero. In such games, the problem of finding the optimal strategy can be formulated as a bilinear saddle-point problem. This formulation grows huge depending on the size of the game, since it has variables representing the strategies at all decision points for each player. To solve such large-scale bilinear saddle-point problems, the excessive gap technique (EGT), a smoothing method, has been studied. This method generates a sequence of approximate solutions whose error is guaranteed to converge at $\mathcal{O}(1/k)$, where $k$ is the number of iterations. However, it has the disadvantage of having poor theoretical bounds on the error related to the game size. This makes it inapplicable to large games. Our goal is to improve the smoothing method for solving extensive-form games so that it can be applied to large-scale games. To this end, we make two contributions in this work. First, we slightly modify the strongly convex function used in the smoothing method in order to improve the theoretical bounds related to the game size. Second, we propose a heuristic called centering trick, which allows the smoothing method to be combined with other methods and consequently accelerates the convergence in practice. As a result, we combine EGT with CFR+, a state-of-the-art method for extensive-form games, to achieve good performance in games where conventional smoothing methods do not perform well. The proposed smoothing method is shown to have the potential to solve large games in practice.