Miracle-dz/UniFAR-FTUs huggingface dataset:
Vnode: Low-overhead Transparent Tracing of Node.js-based Microservice Architectures

Tracing serves as a key method for evaluating the performance of microservices-based architectures, which are renowned for their scalability, resource efficiency, and high availability. Despite their advantages, these architectures often pose unique debugging challenges that necessitate trade-offs, including the burden of instrumentation overhead. With Node.js emerging as a leading development environment recognized for its rapidly growing ecosystem, there is a pressing need for innovative performance debugging approaches that reduce the telemetry data collection efforts and the overhead incurred by the environment’s instrumentation. In response, we introduce a new approach designed for transparent tracing and performance debugging of microservices in cloud settings. This approach is centered around our newly developed Internal Transparent Tracing and Context Reconstruction (ITTCR) technique. ITTCR is adept at correlating internal metrics from various distributed trace files to reconstruct the intricate execution contexts of microservices operating in a Node.js environment. Our method achieves transparency by directly instrumenting the Node.js virtual machine, enabling the collection and analysis of trace events in a transparent manner. This process facilitates the creation of visualization tools, enhancing the understanding and analysis of microservice performance in cloud environments. Compared to other methods, our approach incurs an overhead of approximately 5% on the system for the trace collection infrastructure while exhibiting minimal utilization of system resources during analysis execution. Experiments demonstrate that our technique scales well with very large trace files containing huge numbers of events and performs analyses in very acceptable timeframes.
Does El Niño intensity matter for California precipitation?

The sensitivity of California precipitation to El Niño intensity is investigated by applying a multimodel ensemble of historical climate simulations to estimate how November–April precipitation probability distributions vary across three categorizations of El Niño intensity. Weak and moderate El Niño events fail to appreciably alter wet or dry risks across northern and central California, though odds for wet conditions increase across southern California during moderate El Niño. Significant increases in wet probabilities occur during strong El Niño events across the entire state. In California's main northern watershed regions, simulations indicate an 85% chance of greater than normal precipitation and a 50% probability of at least 125% of normal. Our results indicate that both the statewide average and the spatial distribution of California precipitation are sensitive to El Niño intensity. Forecasts of El Niño intensity would thus contribute to improved situational awareness for California water planning and related water resource impacts.
Measurement of (anti)alpha production in central Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV

In this letter, measurements of (anti)alpha production in central (0$-$10%) Pb$-$Pb collisions at a center-of-mass energy per nucleon$-$nucleon pair of $\sqrt{s_{\rm NN}}$ = 5.02 TeV are presented, including the first measurement of an antialpha transverse-momentum spectrum. Owing to its large mass, (anti)alpha production yields and transverse-momentum spectra are of particular interest because they provide a stringent test of particle production models. The averaged antialpha and alpha spectrum is included into a common blast-wave fit with lighter particles, indicating that the (anti)alpha also participates in the collective expansion of the medium created in the collision. A blast-wave fit including only protons, (anti)alpha, and other light nuclei results in a similar flow velocity as the fit that includes all particles. A similar flow velocity, but a significantly larger kinetic freeze-out temperature is obtained when only protons and light nuclei are included in the fit. The coalescence parameter $B_4$ is well described by calculations from a statistical hadronization model but significantly underestimated by calculations assuming nucleus formation via coalescence of nucleons. Similarly, the (anti)alpha-to-proton ratio is well described by the statistical hadronization model. On the other hand, coalescence calculations including approaches with different implementations of the (anti)alpha substructure tend to underestimate the data.
High-performance cVEP-BCI under minimal calibration

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.
Open Set Dandelion Network for IoT Intrusion Detection

As Internet of Things devices become widely used in the real world, it is crucial to protect them from malicious intrusions. However, the data scarcity of IoT limits the applicability of traditional intrusion detection methods, which are highly data-dependent. To address this, in this paper we propose the Open-Set Dandelion Network (OSDN) based on unsupervised heterogeneous domain adaptation in an open-set manner. The OSDN model performs intrusion knowledge transfer from the knowledge-rich source network intrusion domain to facilitate more accurate intrusion detection for the data-scarce target IoT intrusion domain. Under the open-set setting, it can also detect newly-emerged target domain intrusions that are not observed in the source domain. To achieve this, the OSDN model forms the source domain into a dandelion-like feature space in which each intrusion category is compactly grouped and different intrusion categories are separated, i.e., simultaneously emphasising inter-category separability and intra-category compactness. The dandelion-based target membership mechanism then forms the target dandelion. Then, the dandelion angular separation mechanism achieves better inter-category separability, and the dandelion embedding alignment mechanism further aligns both dandelions in a finer manner. To promote intra-category compactness, the discriminating sampled dandelion mechanism is used. Assisted by the intrusion classifier trained using both known and generated unknown intrusion knowledge, a semantic dandelion correction mechanism emphasises easily-confused categories and guides better inter-category separability. Holistically, these mechanisms form the OSDN model that effectively performs intrusion knowledge transfer to benefit IoT intrusion detection. Comprehensive experiments on several intrusion datasets verify the effectiveness of the OSDN model, outperforming three state-of-the-art baseline methods by \(16.9\% \) . The contribution of each OSDN constituting component, the stability and the efficiency of the OSDN model are also verified.
ViP-Mixer: A Convolutional Mixer for Video Prediction

Video prediction aims to predict future frames from a video's previous content. Existing methods mainly process video data where the time dimension mingles with the space and channel dimensions from three distinct angles: as a sequence of individual frames, as a 3D volume in spatiotemporal coordinates, or as a stacked image where frames are treated as separate channels. Most of them generally focus on one of these perspectives and may fail to fully exploit the relationships across different dimensions. To address this issue, this paper introduces a convolutional mixer for video prediction, termed ViP-Mixer, to model the spatiotemporal evolution in the latent space of an autoencoder. The ViP-Mixers are stacked sequentially and interleave feature mixing at three levels: frames, channels, and locations. Extensive experiments demonstrate that our proposed method achieves new state-of-the-art prediction performance on three benchmark video datasets covering both synthetic and real-world scenarios.
Advances in the Research on Returning Farmland to Forestry or Pasture in China and the General Approaches

The background and history of Returning Farmland to Forestry or Pasture (RFFP) both in China and foreign countries were studied. The advances in the research on thoughts and strategies, hill-closure for forestry, models and technologies, management circumstance in RFFP were carried out. Based on the experiments in over 20 stations and on-the-spot observation, the strategy that took hill-closure for forestry firstly and the artificial methods secondly was put forward as the general approaches for China's RFFP.
Vital Signs Estimation Using a 26 GHz Multi-Beam Communication Testbed

This paper presents a novel pipeline for vital sign monitoring using a 26 GHz multi-beam communication testbed. In context of Joint Communication and Sensing (JCAS), the advanced communication capability at millimeter-wave bands is comparable to the radio resource of radars and is promising to sense the surrounding environment. Being able to communicate and sense the vital sign of humans present in the environment will enable new vertical services of telecommunication, i.e., remote health monitoring. The proposed processing pipeline leverages spatially orthogonal beams to estimate the vital sign - breath rate and heart rate - of single and multiple persons in static scenarios from the raw Channel State Information samples. We consider both monostatic and bistatic sensing scenarios. For monostatic scenario, we employ the phase time-frequency calibration and Discrete Wavelet Transform to improve the performance compared to the conventional Fast Fourier Transform based methods. For bistatic scenario, we use K-means clustering algorithm to extract multi-person vital signs due to the distinct frequency-domain signal feature between single and multi-person scenarios. The results show that the estimated breath rate and heart rate reach below 2 beats per minute (bpm) error compared to the reference captured by on-body sensor for the single-person monostatic sensing scenario with body-transceiver distance up to 2 m, and the two-person bistatic sensing scenario with BS-UE distance up to 4 m. The presented work does not optimize the OFDM waveform parameters for sensing; it demonstrates a promising JCAS proof-of-concept in contact-free vital sign monitoring using mmWave multi-beam communication systems.
Flat Minima in Linear Estimation and an Extended Gauss Markov Theorem

We consider the problem of linear estimation, and establish an extension of the Gauss-Markov theorem, in which the bias operator is allowed to be non-zero but bounded with respect to a matrix norm of Schatten type. We derive simple and explicit formulas for the optimal estimator in the cases of Nuclear and Spectral norms (with the Frobenius case recovering ridge regression). Additionally, we analytically derive the generalization error in multiple random matrix ensembles, and compare with Ridge regression. Finally, we conduct an extensive simulation study, in which we show that the cross-validated Nuclear and Spectral regressors can outperform Ridge in several circumstances.
Graphene-perovskite fibre photodetectors

The integration of optoelectronic devices, such as transistors and photodetectors (PDs), into wearables and textiles is of great interest for applications such as healthcare and physiological monitoring. These require flexible/wearable systems adaptable to body motions, thus materials conformable to non-planar surfaces, and able to maintain performance under mechanical distortions. Here, we prepare fibre PDs combining rolled graphene layers and photoactive perovskites. Conductive fibres ($\sim$500$\Omega$/cm) are made by rolling single layer graphene (SLG) around silica fibres, followed by deposition of a dielectric layer (Al$_{2}$O$_{3}$ and parylene C), another rolled SLG as channel, and perovskite as photoactive component. The resulting gate-tunable PDs have response time$\sim$5ms, with an external responsivity$\sim$22kA/W at 488nm for 1V bias. The external responsivity is two orders of magnitude higher and the response time one order of magnitude faster than state-of-the-art wearable fibre based PDs. Under bending at 4mm radius, up to$\sim$80\% photocurrent is maintained. Washability tests show$\sim$72\% of initial photocurrent after 30 cycles, promising for wearable applications.
Assessment of Transmission-level Fault Impacts on 3-phase and 1-phase Distribution IBR Operation

The widespread deployment of Inverter-based Resources (IBRs) renders distribution systems susceptible to transmission-level faults. This paper presents a comprehensive analysis of the impact of transmission-level faults on 3-phase and 1-phase distribution IBR operation. Using a real-time transmission and distribution (T&D) co-simulation platform, we simulate both symmetrical and unsymmetrical faults at increasing electrical distances within the transmission system while evaluating IBR tripping across various phases and locations at the distribution level. The IBR power-to-load ratios (PLRs) at 50%, 100% and 300% are considered to emulate low, medium, and high IBR conditions. Our results indicate that, while 1-phase and 2-phase faults typically trigger fewer IBR trips when compared to 3-phase faults, a significant power imbalance arises from the tripping of 1-phase IBRs on the affected phases. This imbalance can result in severe power quality problems and unintended equipment trips. This suggests that there may be a need for designing fault-ride-through mechanisms tailored to 1-phase IBRs for mitigating the phase-imbalance caused by unbalanced faults.
Dazed & Confused: A Large-Scale Real-World User Study of reCAPTCHAv2

Since about 2003, captchas have been widely used as a barrier against bots, while simultaneously annoying great multitudes of users worldwide. As their use grew, techniques to defeat or bypass captchas kept improving, while captchas themselves evolved in terms of sophistication and diversity, becoming increasingly difficult to solve for both bots and humans. Given this long-standing and still-ongoing arms race, it is important to investigate usability, solving performance, and user perceptions of modern captchas. In this work, we do so via a large-scale (over 3, 600 distinct users) 13-month real-world user study and post-study survey. The study, conducted at a large public university, was based on a live account creation and password recovery service with currently prevalent captcha type: reCAPTCHAv2. Results show that, with more attempts, users improve in solving checkbox challenges. For website developers and user study designers, results indicate that the website context directly influences (with statistically significant differences) solving time between password recovery and account creation. We consider the impact of participants' major and education level, showing that certain majors exhibit better performance, while, in general, education level has a direct impact on solving time. Unsurprisingly, we discover that participants find image challenges to be annoying, while checkbox challenges are perceived as easy. We also show that, rated via System Usability Scale (SUS), image tasks are viewed as"OK", while checkbox tasks are viewed as"good". We explore the cost and security of reCAPTCHAv2 and conclude that it has an immense cost and no security. Overall, we believe that this study's results prompt a natural conclusion: reCAPTCHAv2 and similar reCAPTCHA technology should be deprecated.
Geometric Properties of Real Gromov-Witten Invariants

We collect geometric properties of the all-genus real Gromov-Witten theory and provide updates on its development since its introduction in 2015. We bring attention to a modification of the original construction of this theory which is applicable in a broader setting, obtain properties of the orientations of the moduli spaces of real maps under this modification which had previously been obtained for the original construction, and compare such orientations obtained via the two constructions when both constructions are applicable. In particular, the orientation of the moduli space of real maps from a disjoint union of domains may not be the product orientation of the moduli spaces of real maps from its components, a possibility overlooked in our past work.
New $\mu$ Forces From $\nu_\mu$ Sources

Accelerator-based experiments reliant on charged pion and kaon decays to produce muon-neutrino beams also deliver an associated powerful flux of muons. Therefore, these experiments can additionally be sensitive to light new particles that preferentially couple to muons and decay to visible final states on macroscopic length scales. Such particles are produced through rare 3-body meson decays in the decay pipe or via muon scattering in the beam dump, and decay in a downstream detector. To demonstrate the potential of this search strategy, we recast existing MiniBooNE and MicroBooNE studies of neutral pion production in neutrino-induced neutral-current scattering ($\nu_\mu N \to \nu_\mu N \pi^0,~\pi^0\rightarrow \gamma\gamma$) to place new leading limits on light ($<2m_\mu$) muon-philic scalar particles that decay to diphotons through loops of virtual muons. Our results exclude scalars of mass between 10 and 60 MeV in which this scenario resolves the muon $g-2$ anomaly. We also make projections for the sensitivity of SBND to these models and provide a road map for future neutrino experiments to perform dedicated searches for muon-philic forces.
Improving Real Estate Appraisal with POI Integration and Areal Embedding

Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizing the necessity for a comprehensive, data-driven approach to feature selection. Secondly, we integrate road-network-based Areal Embedding to enhance spatial understanding for real estate appraisal. We first propose a revised method for POI feature extraction, and discuss the impact of each POI for house price appraisal. Then we present the Areal embedding-enabled Masked Multihead Attention-based Spatial Interpolation for House Price Prediction (AMMASI) model, an improvement upon the existing ASI model, which leverages masked multi-head attention on geographic neighbor houses and similar-featured houses. Our model outperforms current baselines and also offers promising avenues for future optimization in real estate appraisal methodologies.
Progr\`es r\'ecents sur les repr\'esentations supercuspidales

Let $G$ be a reductive group over a nonarchimedean local field $F$. In the quest for a classification of irreducible smooth representations of $G$, it is critical to understand the case of supercuspidal representations -- those whose matrix coefficients are compactly supported modulo the center. Progress in understanding these representations has been continuous over the past fifty years. In"tame"cases where the residual characteristic of $F$ is big enough for $G$, J.-K. Yu described in 2001 a general construction of supercuspidal representations, building on a large body of work. But recent developments have made the general picture much more complete and much clearer. For instance, the work of J. Fintzen, T. Kaletha and L. Spice provides (in the tame case) a classification of supercuspidal representations, an explicit formula for"almost all"their characters, and an explicit construction of a local Langlands correspondence for supercuspidal $L$-packets. While the basic constructions involve Bruhat--Tits buildings and representations of finite groups, the resulting character formulas and the description of $L$-packets have striking parallels with the case of real groups
SENetV2: Aggregated dense layer for channelwise and global representations

Convolutional Neural Networks (CNNs) have revolutionized image classification by extracting spatial features and enabling state-of-the-art accuracy in vision-based tasks. The squeeze and excitation network proposed module gathers channelwise representations of the input. Multilayer perceptrons (MLP) learn global representation from the data and in most image classification models used to learn extracted features of the image. In this paper, we introduce a novel aggregated multilayer perceptron, a multi-branch dense layer, within the Squeeze excitation residual module designed to surpass the performance of existing architectures. Our approach leverages a combination of squeeze excitation network module with dense layers. This fusion enhances the network's ability to capture channel-wise patterns and have global knowledge, leading to a better feature representation. This proposed model has a negligible increase in parameters when compared to SENet. We conduct extensive experiments on benchmark datasets to validate the model and compare them with established architectures. Experimental results demonstrate a remarkable increase in the classification accuracy of the proposed model.
Balanced metrics for extremal K\"ahler metrics and Fano manifolds

The first three sections of this paper are a survey of the author's work on balanced metrics and stability notions in algebraic geometry. The last section is devoted to proving the well-known result that a geodesically convex function on a complete Riemannian manifold admits a critical point if and only if its asymptotic slope at infinity is positive, where we present a proof which relies only on the Hopf--Rinow theorem and extends to locally compact complete length metric spaces.