Miracle-dz/UniFAR-FTUs huggingface dataset:
High-order matrix method with delimited expansion domain

Motivated by the substantial instability of the fundamental and high-overtone quasinormal modes (QNMs), recent developments regarding the notion of black hole pseudospectrum call for numerical results with unprecedented precision. This work generalizes and improves the matrix method for black hole QNMs to higher orders, specifically aiming at a class of perturbations to the metric featured by discontinuity intimately associated with the QNM structural instability. The approach is based on the mock-Chebyshev grid, which guarantees its convergence in the degree of the interpolant. In practice, solving for black hole QNMs is a formidable task. The presence of discontinuity poses a further difficulty so that many well-known approaches cannot be employed straightforwardly. Compared with other viable methods, the modified matrix method is competent in speed and accuracy. Therefore, the method serves as a helpful gadget for relevant studies.
Reversal collision dynamics

Motivated by the study of reversal behaviour of myxobacteria, in this article we are interested in a kinetic model for reversal dynamics, in which particles with directions close to be opposite undergo binary collision resulting in reversing their orientations. To this aim, a generic model for binary collisions between particles with states in a general metric space exhibiting specific symmetry properties is proposed and investigated. The reversal process is given by an involution on the space, and the rate of collision is only supposed to be bounded and lower semi-continuous. We prove existence and uniqueness of measure solutions as well as their convergence to equilibrium, using the graph-theoretical notion of connectivity. We first characterise the shape of equilibria in terms of connected components of a graph on the state space, which can be associated to the initial data of the problem. Strengthening the notion of connectivity on subsets for which the rate of convergence is bounded below, we then show exponential convergence towards the unique steady-state associated to the initial condition. The article is concluded with numerical simulations set on the one-dimensional torus giving evidence to the analytical results.
Robot Localization using Situational Graphs and Building Architectural Plans

— Robots in the construction industry can reduce costs through constant monitoring of the work progress, using high precision data capturing. Accurate data capturing requires precise localization of the mobile robot within the environment. In this paper we present our novel work on robot localization which extracts geometric, semantic as well as the topological information from the architectural plans in the form of walls and rooms, and creates the topological and metric-semantic layer of the Situational Graphs ( S-Graphs ) [1] before navigating in the environment. When the robot navigates in the construction environment, it uses the robot odometry and the sensorial observations in the form of planar walls extracted from the 3D lidar measurements, to estimate its pose relying on a particle ﬁlter method, by exploiting the previously built situational graph and its available geometric, semantic and topological information. We validate our approach in both simulated and real datasets captured on actual on-going construction sites presenting state-of-the-art results when comparing it against traditional geometry based localization techniques.
Heisenberg-limited quantum phase estimation of multiple eigenvalues with few control qubits

Quantum phase estimation is a cornerstone in quantum algorithm design, allowing for the inference of eigenvalues of exponentially-large sparse matrices.The maximum rate at which these eigenvalues may be learned, –known as the Heisenberg limit–, is constrained by bounds on the circuit complexity required to simulate an arbitrary Hamiltonian. Single-control qubit variants of quantum phase estimation that do not require coherence between experiments have garnered interest in recent years due to lower circuit depth and minimal qubit overhead. In this work we show that these methods can achieve the Heisenberg limit, also when one is unable to prepare eigenstates of the system. Given a quantum subroutine which provides samples of a `phase function' g(k)=∑jAjeiϕjk with unknown eigenphases ϕj and overlaps Aj at quantum cost O(k), we show how to estimate the phases {ϕj} with (root-mean-square) error δ for total quantum cost T=O(δ−1). Our scheme combines the idea of Heisenberg-limited multi-order quantum phase estimation for a single eigenvalue phase [Higgins et al (2009) and Kimmel et al (2015)] with subroutines with so-called dense quantum phase estimation which uses classical processing via time-series analysis for the QEEP problem [Somma (2019)] or the matrix pencil method. For our algorithm which adaptively fixes the choice for k in g(k) we prove Heisenberg-limited scaling when we use the time-series/QEEP subroutine. We present numerical evidence that using the matrix pencil technique the algorithm can achieve Heisenberg-limited scaling as well.
TTCDist: Fast Distance Estimation From an Active Monocular Camera Using Time-to-Contact

Distance estimation from vision is fundamental for a myriad of robotic applications such as navigation, manipu-lation, and planning. Inspired by the mammal's visual system, which gazes at specific objects, we develop two novel constraints relating time-to-contact, acceleration, and distance that we call the $\tau$ -constraint and $\Phi$ -constraint. They allow an active (moving) camera to estimate depth efficiently and accurately while using only a small portion of the image. The constraints are applicable to range sensing, sensor fusion, and visual servoing. We successfully validate the proposed constraints with two experiments. The first applies both constraints in a trajectory estimation task with a monocular camera and an Inertial Measurement Unit (IMU). Our methods achieve 30-70% less average trajectory error while running $25\times$ and $6.2\times$ faster than the popular Visual-Inertial Odometry methods VINS-Mono and ROVIO respectively. The second experiment demonstrates that when the constraints are used for feedback with efference copies the resulting closed loop system's eigenvalues are invariant to scaling of the applied control signal. We believe these results indicate the $\tau$ and $\Phi$ constraint's potential as the basis of robust and efficient algorithms for a multitude of robotic applications.
Sense: Model-Hardware Codesign for Accelerating Sparse CNNs on Systolic Arrays

Sparsity is an intrinsic property of convolutional neural networks (CNNs), worth exploiting for CNN accelerators. However, the extra processing involved comes with hardware overhead, resulting in only marginal profits for most architectures. Meanwhile, systolic arrays have become increasingly competitive on CNN acceleration for its high spatiotemporal locality and low hardware overhead. However, the irregularity of sparsity induces imbalanced workloads under the rigid systolic dataflow, causing performance degradation. Thus, this article proposed a systolic-array-based architecture, called Sense, for sparse CNN acceleration by model-hardware codesign, enabling large performance gains. To balance input feature map (IFM) and weight loads across the processing element (PE) array, we applied channel clustering to gather IFMs with approximate sparsity for array computation and codesigned a load-balancing weight pruning method to keep the sparsity ratio of each kernel at a certain value with little accuracy loss, improving PE utilization and overall performance. In addition, adaptive dataflow configuration was applied to determine the computing strategy based on the storage ratio of IFMs and weights, lowering <inline-formula> <tex-math notation="LaTeX">$1.17\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$1.8\times $ </tex-math></inline-formula> dynamic random access memory (DRAM) access compared with Swallow and further reducing system energy consumption. The whole design was implemented on ZynqZCU102 with 200 MHz and performs at 471, 34, 53, and 191 image/s for AlexNet, VGG-16, ResNet-50, and GoogleNet, respectively. Compared with sparse systolic-array-based accelerators, Swallow, fusion-enabled systolic architecture (FESA), and SPOTS, Sense achieves <inline-formula> <tex-math notation="LaTeX">$0.97\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$2.18\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$1.3\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$1.67\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$0.94\times $ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$1.82\times $ </tex-math></inline-formula> energy efficiency (image/J) on these CNNs, respectively.
A variance-aware multiobjective Louvain-like method for community detection in multiplex networks

In this paper, we focus on the community detection problem in multiplex networks, i.e., networks with multiple layers having same node sets and no inter-layer connections. In particular, we look for groups of nodes that can be recognized as communities consistently across the layers. To this end, we propose a new approach that generalizes the Louvain method by (a) simultaneously updating average and variance of the modularity scores across the layers, and (b) reformulating the greedy search procedure in terms of a filter-based multiobjective optimization scheme. Unlike many previous modularity maximization strategies, which rely on some form of aggregation of the various layers, our multiobjective approach aims at maximizing the individual modularities on each layer simultaneously. We report experiments on synthetic and real-world networks, showing the effectiveness and the robustness of the proposed strategies both in the informative case, where all layers show the same community structure, and in the noisy case, where some layers represent only noise.
Spatial-temporal Transformers for EEG Emotion Recognition

Electroencephalography (EEG) is a popular and effective tool for emotion recognition. However, the propagation mechanisms of EEG in the human brain and its intrinsic correlation with emotions are still obscure to researchers. This work proposes four variant transformer frameworks (spatial attention, temporal attention, sequential spatial-temporal attention and simultaneous spatial-temporal attention) for EEG emotion recognition to explore the relationship between emotion and spatial-temporal EEG features. Specifically, spatial attention and temporal attention are to learn the topological structure information and time-varying EEG characteristics for emotion recognition respectively. Sequential spatial-temporal attention does the spatial attention within a one-second segment and temporal attention within one sample sequentially to explore the influence degree of emotional stimulation on EEG signals of diverse EEG electrodes in the same temporal segment. The simultaneous spatial-temporal attention, whose spatial and temporal attention are performed simultaneously, is used to model the relationship between different spatial features in different time segments. The experimental results demonstrate that simultaneous spatial-temporal attention leads to the best emotion recognition accuracy among the design choices, indicating modeling the correlation of spatial and temporal features of EEG signals is significant to emotion recognition.
Introducing nonlinear activations into quantum generative models

Due to the linearity of quantum mechanics, it remains a challenge to design quantum generative machine learning models that embed non-linear activations into the evolution of the statevector. However, some of the most successful classical generative models, such as those based on neural networks, involve highly non-linear dynamics for quality training. In this paper, we explore the effect of these dynamics in quantum generative modeling by introducing a model that adds non-linear activations via a neural network structure onto the standard Born Machine framework - the Quantum Neuron Born Machine (QNBM). To achieve this, we utilize a previously introduced Quantum Neuron subroutine, which is a repeat-until-success circuit with mid-circuit measurements and classical control. After introducing the QNBM, we investigate how its performance depends on network size, by training a 3-layer QNBM with 4 output neurons and various input and hidden layer sizes. We then compare our non-linear QNBM to the linear Quantum Circuit Born Machine (QCBM). We allocate similar time and memory resources to each model, such that the only major difference is the qubit overhead required by the QNBM. With gradient-based training, we show that while both models can easily learn a trivial uniform probability distribution, on a more challenging class of distributions, the QNBM achieves an almost 3x smaller error rate than a QCBM with a similar number of tunable parameters. We therefore provide evidence that suggests that non-linearity is a useful resource in quantum generative models, and we put forth the QNBM as a new model with good generative performance and potential for quantum advantage.
CMS HI Physics at LHC Runs 3+4 and Beyond

The heavy ion (HI) program at the LHC has proven to be a successful and indispensable part of the LHC physics program. Its chief aim had been the detailed characterization of the quark-gluon plasma (QGP) in lead-lead collisions. Using additional data sets of proton-lead, proton-proton, and xenon-xenon collisions, the program has also included many advances, for example, in the understanding of the partonic nuclear structure, collectivity in smaller collision systems, and electromagnetic interactions. This Letter of Interest outlines the CMS Heavy Ion Group point of view regarding the scientific case for the use of ultrarelativistic HI beams in the coming decade to characterize QGP with unparalleled precision and to probe novel fundamental physics phenomena. More specifically, it outlines the open questions in the field which can be addressed with CMS, and aims to promote engagement from the US community and its international partners by building upon the recently concluded Snowmass 2022 exercise, the input provided to the European Strategy for Particle Physics, and proposed continuations and extensions of the last version of the US Long-Range Plan for Nuclear Physics.
Exploration of cortical Î²-Amyloid load in Alzheimer's disease using quantitative susceptibility mapping at 9.4T

Early detection of {beta}-Amyloid (A{beta}) deposits in Alzheimer's disease (AD) patients may enable treatment in the early stages of the disease. To date, there are no validated, specific, and non-invasive routines for early A{beta} detection which are suitable for clinical practice. Ultra-high resolution quantitative susceptibility mapping (QSM) at 14.1T has previously shown different contrasts in cortical areas of an AD sample that resembled distinct A{beta} spatial patterns in histological sections of the same specimen. These contrasts appeared different in the QSM from a healthy control (HC) sample where, instead, no plaques were detected. In a few cases, this distinction in the cortical magnetic effects (para- and diamagnetic) between AD and HC was also observed in vivo using ultra-high resolution single-echo Acquisition Weighted (AW) gradient echo MRI at 9.4T. Based on this evidence, a method to quantify the paramagnetic and diamagnetic effects of A{beta} to possibly distinguish between AD and HC was developed. In this study, we extended those results and explored the ability to use QSM to estimate {beta}-Amyloid plaque load in the cortex of 7 elderly patients with early AD and 7 healthy age-matched HC. Besides ultra-high resolution AW images, we acquired lower resolution multi-echo (MTE) data and compared the previously used RESHARP-based method for background removal (''AW-filter'', with a high Tikhonov term and a small kernel size) with a method widely used in the literature (''mild-filter'', with a low-regularization term and a large kernel size) for the MTE processing. Paramagnetic and diamagnetic QSM changes were assessed in 16 cortical areas. All methods enabled the detection of regions with high QSM values (up to 45 ppb in AD and up to 25 ppb in HC) and known as early A{beta} accumulation areas in AD progression. A distinct cortical pattern was observed at both spatial resolutions using the AW-filter. This was not the case with the mild-filter at the lower resolution. AW-QSM outperformed MTE maps with the AW-filter for the detection of areas with prominent cortical paramagnetic effects, including regions where A{beta} accumulation happens in the earliest AD stages, such as the precuneus and posterior cingulate cortex. Diamagnetic changes were more prominent than the paramagnetic effects regardless of the spatial resolution used and this difference was further enhanced with the mild-filter. This explorative study points toward the development of more accessible clinical methods to non-invasively detect effects of A{beta} accumulation in AD patients by exploring cortical features that can be detected by ultra-high field QSM at different spatial resolutions.
Structure and specificity of an anti‐chloramphenicol single domain antibody for detection of amphenicol residues

Antibiotics in aquaculture prevent bacterial infection of fish, but their misuse is a public health risk and contributes to the unintentional creation of multiresistant pathogens. Regulatory agencies cannot do the rigorous, expensive testing required to keep up with the volume of seafood shipments. Current rapid test kits for these drugs enable the increase in testing needed for adequate monitoring of food supply chains, but they lack a high degree of accuracy. To combat this, we set out to discover and engineer single‐domain antibodies (VHHs) that bind to small molecule antibiotics, and that can be used in rapid test kits. The small size, solubility, and stability of VHHs are useful properties that can improve the reliability and shelf‐life of test kits for these adulterants. Here, we report a novel anti‐chloramphenicol VHH (Chl‐VHH) with a disassociation constant of 57 nM. This was achieved by immunizing a llama against a chloramphenicol‐keyhole limpet hemocyanin (KLH) conjugate and screening for high affinity binders through phage display. The crystal structure of the bound‐VHH to chloramphenicol was key to identifying a mutation in the binding pocket that resulted in a 16‐fold improvement in binding affinity. In addition, the structure provides new insights into VHH‐hapten interactions that can guide future engineering of VHHs against additional targets.
Importance of Host Abundance and Microhabitat in Tick Abundance

Abstract To reduce the risk of zoonoses, it is necessary to understand the infection process, including the ecology of animals and vectors (i.e., the ‘One Health’ approach). In temperate climates, ticks are the major vectors of zoonoses, so factors determining their abundance, such as host mammal abundance and microhabitat conditions, should be clarified. Sika deer (Cervus nippon) are a major tick host and are rapidly expanding their distribution in Japan. We established 12 plots along a gradient of sika deer abundance in Tochigi Prefecture, Japan. We monitored the occurrence of mammal species with camera traps and sampled questing ticks on a monthly basis by flagging along three transects (center of a trail, forest edge, and forest interior) at each site from April to November 2018. The camera traps recorded 12 mammal species, predominantly sika deer. Five Haemaphysalis species and three Ixodes species were sampled. The numbers of ticks sampled were explained by the photographic frequency of sika deer, and partly by that of other mammal species, depending on tick species and their developmental stages. The numbers of sampled adult and nymphal ticks were the highest at the forest edge, where vegetation cover was greatest. Thus, vegetation management in tick habitats and the control of sika deer populations may reduce tick abundance.
Application of the Radon transform to optical production of the Wigner distribution function

The Wigner distribution function (WDF), a simultaneous coordinate and frequency representation of a signal, has properties useful in pattern recognition. Because the WDF is computationally demanding, its use is not usually appropriate in digital processing. Optical schemes have been developed to compute the WDF for one-dimensional (1 -D) signals, often using acousto-optic signal transducers. Some recent work has demonstrated the computation of two-dimensional (2-D) slices of the four-dimensional (4-D) WDF of a 2-D input transparency. In this latter case, the required 2-D Fourier transformation is performed by coherent optics. We demonstrate that computation of the WDF of real 2-D signals is susceptible to Radon transform solution. The 2-D operation is reduced to a series of 1 -D operations on the line-integral projections. The required projection data are produced optically, and the Fourier transformation is performed by efficient 1 -D processors (surface acoustic wave filters) by means of the chirp-transform algorithm. The resultant output gives 1 -D slices through the 4-D WDF nearly in real time, and the computation is not restricted to coherently illuminated transparencies. This approach may be useful in distinguishing patterns with known texture direction. The optical setup is easily modified to produce the cross-Wigner distribution function, a special case of the complex, or windowed, spectrogram.
Screening tools for work-related asthma and their diagnostic accuracy: a systematic review protocol

Introduction Work-related asthma (WRA) refers to asthma caused by exposures at work (occupational asthma) and asthma made worse by work conditions (work-exacerbated asthma). WRA is common among working-age adults with asthma and impacts individual health, work–life and income but is often not detected by healthcare services. Earlier identification can lead to better health and employment outcomes. However, the optimal tool for screening and its effectiveness in practice is not well established. Screening tools may include whole questionnaires, questionnaire items, physiological measurements and/or immunological tests. Since the publication of the most contemporary WRA or occupational asthma-specific guidelines, further studies evaluating tools for identifying WRA have been performed. Our systematic review aims to summarise and compare the performance of screening tools for identifying WRA in both clinical and workplace settings. Methods and analysis We will conduct a systematic review of observational and experimental studies (1975–2021) using MEDLINE, EMBASE, CINAHL Plus, Web of Science, CDSR, DARE, HTA, CISDOC databases and grey literature. Two independent reviewers will screen the studies using predetermined criteria, extract data according to a schedule and assess study quality using the Quality Assessment of Diagnostic Test Accuracy 2 tool. Screening tools and test accuracy measures will be summarised. Paired forest plots and summary receiver operating characteristic curves of sensitivities and specificities will be evaluated for heterogeneity between studies, using subgroup analyses, where possible. If the studies are sufficiently homogenous, we will use a bivariate random effect model for meta-analysis. A narrative summary and interpretation will be provided if meta-analysis is not appropriate. Ethics and dissemination As this is a systematic review and does not involve primary data collection, formal ethical review is not required. We will disseminate our findings through open access peer-reviewed publication as well as through other academic and social media. PROSPERO registration number CRD42021246031.