Search

Current artificial intelligence (AI) models often focus on enhancing performance through meticulous parameter tuning and optimization techniques. However, the fundamental design principles behind these models receive comparatively less attention, which can limit our understanding of their potential and constraints. This comprehensive review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI). We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We also examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges. By delving into these areas, we provide new insights and propose future research directions to drive innovation and address current gaps in the field. This review offers researchers and practitioners a comprehensive overview of the BIAI landscape, helping them harness its potential and expedite advancements in AI development., Comment: 35 pages, 4 figures

Retrieval-Augmented Large Language Models (RALMs) have made significant strides in enhancing the accuracy of generated responses.However, existing research often overlooks the data quality issues within retrieval results, often caused by inaccurate existing vector-distance-based retrieval methods.We propose to boost the precision of RALMs' answers from a data quality perspective through the Context-Driven Index Trimming (CDIT) framework, where Context Matching Dependencies (CMDs) are employed as logical data quality rules to capture and regulate the consistency between retrieved contexts.Based on the semantic comprehension capabilities of Large Language Models (LLMs), CDIT can effectively identify and discard retrieval results that are inconsistent with the query context and further modify indexes in the database, thereby improving answer quality.Experiments demonstrate on challenging question-answering tasks.Also, the flexibility of CDIT is verified through its compatibility with various language models and indexing methods, which offers a promising approach to bolster RALMs' data quality and retrieval precision jointly.

We theoretically study the decoherence of a two-level quantum system coupled to noisy environments exhibiting linear and quadratic fluctuations within the framework of a stochastic Liouville equation. It is shown that the intrinsic energy levels of the quantum system renormalize under either the linear or quadratic influence of the environmental noise. In the case of quadratic dependence, the renormalization of the energy levels of the system emerges even if the environmental noise exhibits stationary statistical properties. This is in contrast to the case under linear influence, where the intrinsic energy levels of the system renormalize only if the environmental noise displays nonstationary statistics. We derive the analytical expressions of the decoherence function in the cases where the fluctuation of the frequency difference depends linearly and quadratically on the nonstationary Ornstein-Uhlenbeck noise (OUN) and random telegraph noise (RTN) processes, respectively. In the case of the linear dependence of the OUN, the environmental nonstationary statistical property can enhance the dynamical decoherence. However, the nonstationary statistics of the environmental noise can suppress the quantum decoherence in this case under the quadratic influence of the OUN. In the presence of the RTN, the quadratic influence of the environmental noise does not give rise to decoherence but only causes a determinate frequency renormalization in dynamical evolution. The environmental nonstationary statistical property can suppress the quantum decoherence of the case under the linear influence of the RTN.

Wood-leaf classification is an essential and fundamental prerequisite in the analysis and estimation of forest attributes from terrestrial laser scanning (TLS) point clouds,including critical measurements such as diameter at breast height(DBH),above-ground biomass(AGB),wood volume.To address this,we introduce the Wood-Leaf Classification Network(WLC-Net),a deep learning model derived from PointNet++,designed to differentiate between wood and leaf points within tree point clouds.WLC-Net enhances classification accuracy,completeness,and speed by incorporating linearity as an inherent feature,refining the input-output framework,and optimizing the centroid sampling technique.WLC-Net was trained and assessed using three distinct tree species datasets,comprising a total of 102 individual tree point clouds:21 Chinese ash trees,21 willow trees,and 60 tropical trees.For comparative evaluation,five alternative methods,including PointNet++,DGCNN,Krishna Moorthy's method,LeWoS, and Sun's method,were also applied to these datasets.The classification accuracy of all six methods was quantified using three metrics:overall accuracy(OA),mean Intersection over Union(mIoU),and F1-score.Across all three datasets,WLC-Net demonstrated superior performance, achieving OA scores of 0.9778, 0.9712, and 0.9508;mIoU scores of 0.9761, 0.9693,and 0.9141;and F1-scores of 0.8628, 0.7938,and 0.9019,respectively.The time costs of WLC-Net were also recorded to evaluate the efficiency.The average processing time was 102.74s per million points for WLC-Net.In terms of visual inspect,accuracy evaluation and efficiency evaluation,the results suggest that WLC-Net presents a promising approach for wood-leaf classification,distinguished by its high accuracy. In addition,WLC-Net also exhibits strong applicability across various tree point clouds and holds promise for further optimization., Comment: 41 pages, 14 figures, 5 tables

Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from 70 MHz to 8 GHz). Consequently, for network monitoring and spectrum sharing applications, a sniffer for extracting physical layer information, such as structure of packet, with low sampling rate (especially, sub-Nyquist sampling) can significantly improve their cost- and energy-efficiency. However, to achieve a multiband signals sniffer is really a challenge. To this end, we propose Sums, a system that can sniff and analyze multiband signals in a blind manner. Our Sums takes advantage of hardware and algorithm co-design, multi-coset sub-Nyquist sampling hardware, and a multi-task deep learning framework. The hardware component breaks the Nyquist rule to sample GHz bandwidth, but only pays for a 50 MSPS sampling rate. Our multi-task learning framework directly tackles the sampling data to perform spectrum sensing, physical layer protocol recognition, and demodulation for deep inspection from multiband signals. Extensive experiments demonstrate that Sums achieves higher accuracy than the state-of-theart baselines in spectrum sensing, modulation classification, and demodulation. As a result, our Sums can help researchers and end-users to diagnose or troubleshoot their problems of wireless infrastructures deployments in practice., Comment: 12 pages, 9 figures

To achieve the physics goal of precisely measure the Higgs, Z, W bosons and the top quark, future electron-positron colliders require that their detector system has excellent jet energy resolution. One feasible technical option is the high granular calorimetery based on the particle flow algorithm (PFA). A new high-granularity hadronic calorimeter with glass scintillator tiles (GSHCAL) has been proposed, which focus on the significant improvement of hadronic energy resolution with a notable increase of the energy sampling fraction by using high-density glass scintillator tiles. The minimum ionizing particle (MIP) response of a glass scintillator tile is crucial to the hadronic calorimeter, so a dedicated beamtest setup was developed for testing the first batch of large-size glass scintillators. The maximum MIP response of the first batch of glass scintillator tiles can reach up to 107 p.e./MIP, which essentially meets the design requirements of the CEPC GSHCAL. An optical simulation model of a single glass scintillator tile has been established, and the simulation results are consistent with the beamtest results.

The spread of the Coronavirus disease-2019 epidemic has caused many courses and exams to be conducted online. The cheating behavior detection model in examination invigilation systems plays a pivotal role in guaranteeing the equality of long-distance examinations. However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze angle, body posture, and background information in the task of cheating behavior detection. In this paper, we develop and present CHEESE, a CHEating detection framework via multiplE inStancE learning. The framework consists of a label generator that implements weak supervision and a feature encoder to learn discriminative features. In addition, the framework combines body posture and background features extracted by 3D convolution with eye gaze, head posture and facial features captured by OpenFace 2.0. These features are fed into the spatio-temporal graph module by stitching to analyze the spatio-temporal changes in video clips to detect the cheating behaviors. Our experiments on three datasets, UCF-Crime, ShanghaiTech and Online Exam Proctoring (OEP), prove the effectiveness of our method as compared to the state-of-the-art approaches, and obtain the frame-level AUC score of 87.58% on the OEP dataset., Comment: 12 pages, 7 figures

We formalize Italian smocking, an intricate embroidery technique that gathers flat fabric into pleats along meandering lines of stitches, resulting in pleats that fold and gather where the stitching veers. In contrast to English smocking, characterized by colorful stitches decorating uniformly shaped pleats, and Canadian smocking, which uses localized knots to form voluminous pleats, Italian smocking permits the fabric to move freely along the stitched threads following curved paths, resulting in complex and unpredictable pleats with highly diverse, irregular structures, achieved simply by pulling on the threads. We introduce a novel method for digital previewing of Italian smocking results, given the thread stitching path as input. Our method uses a coarse-grained mass-spring system to simulate the interaction between the threads and the fabric. This configuration guides the fine-level fabric deformation through an adaptation of the state-of-the-art simulator, C-IPC. Our method models the general problem of fabric-thread interaction and can be readily adapted to preview Canadian smocking as well. We compare our results to baseline approaches and physical fabrications to demonstrate the accuracy of our method.

The rapid progress in gravitational wave astronomy has provided an opportunity for investigating the presence of long-range scalar forces that exclusively manifest around astrophysical black holes. In this paper, we explore a new possibility in this context, particularly in connection to the hypothesis that astrophysical black holes might be horizonless ultracompact objects (UCOs). In the absence of horizons, UCOs could feature unique interiors with extreme environments. This could help generate non-trivial scalar profiles and significant scalar charges. For demonstration, we consider 2-2-holes in quadratic gravity as a concrete example of UCOs. These objects can be formed by ordinary gases and closely resemble black holes externally. However, they have distinct interiors characterized by high curvatures and substantial redshift. In particular, the gases inside could reach extremely high temperatures or densities, making them an ideal object for investigating the generation of scalar profiles by UCOs. Within a minimal model of the scalar field, we find that this unique environment enables the generation of a substantial scalar charge for astrophysical 2-2-holes, which is challenging for other stellar objects. The predicted scalar charge-to-mass ratio of 2-2-holes remains nearly constant across a wide range of masses, offering different predictions for gravitational wave observations compared to other mechanisms., Comment: 30 pages, 10 figures. v2: typos fixed, matches published version in PRD

We develop an optimization-based method to model smocking, a surface embroidery technique that provides decorative geometric texturing while maintaining stretch properties of the fabric. During smocking, multiple pairs of points on the fabric are stitched together, creating non-manifold geometric features and visually pleasing textures. Designing smocking patterns is challenging, because the outcome of stitching is unpredictable: the final texture is often revealed only when the whole smocking process is completed, necessitating painstaking physical fabrication and time consuming trial-and-error experimentation. This motivates us to seek a digital smocking design method. Straightforward attempts to compute smocked fabric geometry using surface deformation or cloth simulation methods fail to produce realistic results, likely due to the intricate structure of the designs, the large number of contacts and high-curvature folds. We instead formulate smocking as a graph embedding and shape deformation problem. We extract a coarse graph representing the fabric and the stitching constraints, and then derive the graph structure of the smocked result. We solve for the 3D embedding of this graph, which in turn reliably guides the deformation of the high-resolution fabric mesh. Our optimization based method is simple, efficient, and flexible, which allows us to build an interactive system for smocking pattern exploration. To demonstrate the accuracy of our method, we compare our results to real fabrications on a large set of smocking patterns, Comment: 17 pages, 35 figures

Postmerger gravitational wave echoes provide a unique opportunity to probe the near-horizon structure of astrophysical black holes, which may be modified due to nonperturbative quantum gravity phenomena. However, since the waveform is subject to large theoretical uncertainties, it is necessary to develop search methods that are less reliant on specific models for detecting echoes from observational data. A promising strategy is to identify the characteristic quasinormal modes (QNMs) associated with echoes, {\it in frequency space}, which complements existing searches of quasiperiodic pulses in time. In this study, we build upon our previous work targeting these modes by incorporating relative phase information to optimize the Bayesian search algorithm. Using a new phase-marginalized likelihood, the performance can be significantly improved for well-resolved QNMs. This enables an efficient search for QNMs of various shapes, utilizing a simple search template that is independent of specific models. To demonstrate the robustness of the search algorithm, we construct four complementary benchmarks for the echo waveform that span a diverse range of different theoretical possibilities for the near-horizon structure. We then validate our Bayesian search algorithms by injecting the benchmark models into different realizations of Gaussian noise. Using two types of phase-marginalized likelihoods, we find that the search algorithm can efficiently detect the corresponding QNMs. Therefore, our search strategy provides a concrete Bayesian and model-independent approach to "quantum black hole seismology.", Comment: 46 pages, 19 figures, 4 tables. Python code to reproduce figures is available at the link http://github.com/hermione-evans/echomase; v2: typos fixed, matches published version in PRD

High-frequency gravitational waves (HFGWs) carry a wealth of information on the early Universe with a tiny comoving horizon and astronomical objects of small scale but with dense energy. We demonstrate that the nearby planets, such as Earth and Jupiter, can be utilized as a laboratory for detecting the HFGWs. These GWs are then expected to convert to signal photons in the planetary magnetosphere, across the frequency band of astronomical observation. As a proof of concept, we present the first limits from the existing low-Earth-orbit satellite for specific frequency bands and project the sensitivities for the future more-dedicated detections. The first limits from Juno, the latest mission orbiting Jupiter, are also presented. Attributed to the long path of effective GW-photon conversion and the wide angular distribution of signal flux, we find that these limits are highly encouraging, for a broad frequency range including a large portion unexplored before., Comment: 12 pages, 10 figures. Final published version

Pulsar timing arrays (PTAs) can detect disturbances in the fabric of spacetime on a galactic scale by monitoring the arrival time of pulses from millisecond pulsars (MSPs). Recent advancements have enabled the use of $\gamma$-ray radiation emitted by MSPs, in addition to radio waves, for PTA experiments. Wave dark matter (DM), a prominent class of DM candidates, can be detected with PTAs due to its periodic perturbations of the spacetime metric. In response to this development, we perform in this Letter a first analysis of applying the $\gamma$-ray PTA to detect the ultralight axion-like wave DM, with the data of Fermi Large Area Telescope (Fermi-LAT). Despite its much smaller collecting area, the Fermi-LAT $\gamma$-ray PTA demonstrates a promising sensitivity potential. We show that the upper limits not far from those of the dedicated radio-PTA projects can be achieved. Moreover, we initiate a cross-correlation analysis using the data of two Fermi-LAT pulsars. The cross-correlation of phases, while carrying key information on the source of the spacetime perturbations, has been ignored in the existing data analyses for the wave DM detection with PTAs. Our analysis indicates that taking this information into account can improve the sensitivity to wave DM by $\gtrsim 50\%$ at masses below $10^{-23}$ eV., Comment: 11 pages, 4 figures. Published version

Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks like, node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network (GCN), graph attention network (GAT), graph autoencoder (GAE), and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.