搜索结果：Haram

High-harmonic generation (HHG) in solids has typically been explored in transparent dielectrics and semiconductors. Metals have long been dismissed due to their strong reflectivity at infrared wavelengths. Here, we demonstrate HHG from silver - a noble metal - using few-cycle near-infrared laser pulses at near-normal incidence. Our results show that sub-cycle electron dynamics within the material's penetration depth can drive high-order harmonics, challenging the prevailing notion that metals are unsuited for infrared-driven strong-field processes. Despite silver's high reflectivity and large free-electron density, we observe nonperturbative harmonics extending into the extreme ultraviolet (up to 20 eV). Moreover, silver's multi-shot damage threshold proves surprisingly high (30 TW/cm^2) - comparable to large-bandgap dielectrics like magnesium oxide - thereby enabling intense strong-field processes in a metallic environment. Measuring the orientation dependence of the emitted harmonics reveals that the process arises from coherent electron dynamics in the crystal lattice, rather than from a plasma-driven mechanism. Time-dependent density-matrix simulations based on maximally locali

Suicidal risk detection in adolescents is a critical challenge, yet existing methods rely on language-specific models, limiting scalability and generalization. This study introduces a novel language-agnostic framework for suicidal risk assessment with large language models (LLMs). We generate Chinese transcripts from speech using an ASR model and then employ LLMs with prompt-based queries to extract suicidal risk-related features from these transcripts. The extracted features are retained in both Chinese and English to enable cross-linguistic analysis and then used to fine-tune corresponding pretrained language models independently. Experimental results show that our method achieves performance comparable to direct fine-tuning with ASR results or to models trained solely on Chinese suicidal risk-related features, demonstrating its potential to overcome language constraints and improve the robustness of suicidal risk assessment.

Speech-based AI models are emerging as powerful tools for detecting depression and the presence of Post-traumatic stress disorder (PTSD), offering a non-invasive and cost-effective way to assess mental health. However, these models often struggle with gender bias, which can lead to unfair and inaccurate predictions. In this study, our study addresses this issue by introducing a domain adversarial training approach that explicitly considers gender differences in speech-based depression and PTSD detection. Specifically, we treat different genders as distinct domains and integrate this information into a pretrained speech foundation model. We then validate its effectiveness on the E-DAIC dataset to assess its impact on performance. Experimental results show that our method notably improves detection performance, increasing the F1-score by up to 13.29 percentage points compared to the baseline. This highlights the importance of addressing demographic disparities in AI-driven mental health assessment.

High-harmonic generation (HHG) is a strong-field phenomenon that is sensitive to the attosecond dynamics of tunnel ionization and coherent transport of electron-hole pairs in solids. While the foundations of solid HHG have been established, a deep understanding into the nature of decoherence on sub-cycle timescales remains elusive. Furthermore, there is a growing need for tools to control ionization at the nanoscale. Here, we study HHG in silicon along a crystalline-to-amorphous (c-Si to a-Si) structural phase transition and observe a dramatic reshaping of the spectrum, with enhanced lower-order harmonic yield accompanied by quenching of the higher-order harmonics. Modelling the real-space quantum dynamics links our observations to a giant enhancement (>250 times) of tunnel ionization yield in the amorphous phase and a disorder-induced decoherence that damps the electron-hole polarization over approximately six lattice sites. HHG spectroscopy also reveals remnant order that was not apparent with conventional probes. Finally, we observe a rapid and targeted non-resonant laser annealing of amorphous silicon islands. Our results offer a unique insight into attosecond decoherence in

We consider the compressible Euler equation with a Coriolis term and prove a lower bound on the time of existence of solutions in terms of the speed of rotation, sound speed and size of the initial data. Along the way, we obtain precise dispersive decay estimates for the linearized equation. In the incompressible limit, this improves current bounds for the incompressible Euler-Coriolis system as well.

We investigate and quantify the effect of stratification on the stability time of a stably stratified rest state for the 2D inviscid Boussinesq system on $\mathbb{R}^2$. As an important consequence, we obtain stability of the steady state starting from an $\varepsilon$-sized initial perturbation of Sobolev regularity $H^{3^+}$ on a timescale $\mathcal{O}(\varepsilon^{-4/3})$. In our setting, stratification induces dispersion and at the core of our approach are inhomogeneous Strichartz estimates used to control nonlinear contributions. This allows to keep only $L^2-$based regularity assumptions on the initial perturbation, whereas previous works impose additional localizations to achieve this timescale. We prove the analogous result for the related dispersive SQG equation.

Coupled electronic and nuclear motions govern chemical reactions, yet disentangling their interplay during bond rupture remains challenging. Here we follow the light-induced fragmentation of Br$_2$ using a coincidence-based multi-messenger approach. A UV pulse prepares the dissociative state, and strong-field ionization probes the evolving system. Coincident measurement of three-dimensional photoion and photoelectron momenta provides real-time access to both the instantaneous internuclear separation and the accompanying reorganization of the electronic structure, allowing us to determine the timescale of bond breaking. We find that electronic rearrangement concludes well before the nuclei reach the bond-breaking distance, revealing a hierarchy imposed by electron-nuclear coupling. Supported by semiclassical modelling, the results show that the stretched Br$_2$ molecule behaves as a two-centre interferometer in which the loss of coherence between atomic centres encodes the coupled evolution of electrons and nuclei. Our work establishes a general framework for imaging ultrafast electron-nuclear dynamics in molecules.

We prove that the solutions to the 3D Navier-Stokes equation with constant rotation exist globally for small axisymmetric initial data, where the smallness is uniform with respect to the viscosity $ν\in [0,\infty)$. This expands the work by Guo, Pausader, and Widmayer \cite{GPW} which showed the global axisymmetric stability of rotation for 3D incompressible Euler's equation, to the viscous case, but for a single threshold that works for arbitrary viscosity. This is achieved by suitably adapting the dispersive framework established in \cite{GPW} to the Navier-Stokes setting.

We consider the spherical Sherrington-Kirkpatrick model of spin glass with sparse interaction, where the interactions between most of the pairs of the spin variables are possibly zero. With suitable normalization, we prove that the limiting free energy does not depend on the sparsity whereas the fluctuation of the free energy does. We also prove that both in the high- and the low-temperature regimes the fluctuation of the free energy converges in distribution to Gaussian distributions of same order when the sparsity is on a certain level, but their variances are different.

Transport systems are vulnerable to disruption. This is particularly true in Africa, where there are large areas with few highways and heightened risk of violence. Here we attempt to estimate the costs of violent events on African transport in order to understand the way that it may be limiting integration between regions. In the absence of detailed data on trade or migration, we quantify the cost of violence by relating observed incidents to imputed spatial interaction between cities. We produce indices representing the expected intensity of violent events $μ$ and the expected strength of interaction $ν$ between cities in the African interurban network. We estimate the intensity of conflict in a city and, considering the network of all highways on the continent, use a gravity model to generate flows between pairs of cities. We systematically compare $μ$ to $ν$ and classify areas according to their combined impact and intensity. Results show that certain cities and roads in the network contain outsize risk to Africa's transportation infrastructure. These cities have a high propensity for subsequent violence against civilians, and given their role in the network, they also substanti

As multimedia content often contains noise from intrinsic defects of digital devices, image denoising is an important step for high-level vision recognition tasks. Although several studies have developed the denoising field employing advanced Transformers, these networks are too momory-intensive for real-world applications. Additionally, there is a lack of research on lightweight denosing (LWDN) with Transformers. To handle this, this work provides seven comparative baseline Transformers for LWDN, serving as a foundation for future research. We also demonstrate the parts of randomly cropped patches significantly affect the denoising performances during training. While previous studies have overlooked this aspect, we aim to train our baseline Transformers in a truly fair manner. Furthermore, we conduct empirical analyses of various components to determine the key considerations for constructing LWDN Transformers. Codes are available at https://github.com/rami0205/LWDN.

Although many recent works have made advancements in the image restoration (IR) field, they often suffer from an excessive number of parameters. Another issue is that most Transformer-based IR methods focus only on either local or global features, leading to limited receptive fields or deficient parameter issues. To address these problems, we propose a lightweight IR network, Reciprocal Attention Mixing Transformer (RAMiT). It employs our proposed dimensional reciprocal attention mixing Transformer (D-RAMiT) blocks, which compute bi-dimensional (spatial and channel) self-attentions in parallel with different numbers of multi-heads. The bi-dimensional attentions help each other to complement their counterpart's drawbacks and are then mixed. Additionally, we introduce a hierarchical reciprocal attention mixing (H-RAMi) layer that compensates for pixel-level information losses and utilizes semantic information while maintaining an efficient hierarchical structure. Furthermore, we revisit and modify MobileNet V1 and V2 to attach efficient convolutions to our proposed components. The experimental results demonstrate that RAMiT achieves state-of-the-art performance on multiple lightweigh

The limited availability of non-native speech datasets presents a major challenge in automatic speech recognition (ASR) to narrow the performance gap between native and non-native speakers. To address this, the focus of this study is on the efficient incorporation of the L2 phonemes, which in this work refer to Korean phonemes, through articulatory feature analysis. This not only enables accurate modeling of pronunciation variants but also allows for the utilization of both native Korean and English speech datasets. We employ the lattice-free maximum mutual information (LF-MMI) objective in an end-to-end manner, to train the acoustic model to align and predict one of multiple pronunciation candidates. Experimental results show that the proposed method improves ASR accuracy for Korean L2 speech by training solely on L1 speech data. Furthermore, fine-tuning on L2 speech improves recognition accuracy for both L1 and L2 speech without performance trade-offs.

Bluetooth Basic Rate/Enhanced Data Rate (BR/EDR) is a wireless technology used in billions of devices. Recently, several Bluetooth fuzzing studies have been conducted to detect vulnerabilities in Bluetooth devices, but they fall short of effectively generating malformed packets. In this paper, we propose L2FUZZ, a stateful fuzzer to detect vulnerabilities in Bluetooth BR/EDR Logical Link Control and Adaptation Protocol (L2CAP) layer. By selecting valid commands for each state and mutating only the core fields of packets, L2FUZZ can generate valid malformed packets that are less likely to be rejected by the target device. Our experimental results confirmed that: (1) L2FUZZ generates up to 46 times more malformed packets with a much less packet rejection ratio compared to the existing techniques, and (2) L2FUZZ detected five zero-day vulnerabilities from eight real-world Bluetooth devices.

Current methods of voter identification, especially in India, are highly primitive and error-prone, depending on verification by (mostly) sight, by highly trusted election officials. This paper attempts to provide a trustless and zero-knowledge method of voter identification, while simultaneously reducing error. It also proposes a method for vote verification, that is, ensuring that the vote cast by a legal voter is registered as cast and tallied as registered. While numerous methods of zero-knowledge identification are available in the literature, very few of those are implementable on a large scale and subject to the type of constraints that are present, eg., in India. This paper attempts to provide a solution which, while preserving the integrity of the available methods, will also be more scalable and cost-effective.

In this paper we present a number of methods (manual, semi-automatic and automatic) for tracking individual targets in high density crowd scenes where thousand of people are gathered. The necessary data about the motion of individuals and a lot of other physical information can be extracted from consecutive image sequences in different ways, including optical flow and block motion estimation. One of the famous methods for tracking moving objects is the block matching method. This way to estimate subject motion requires the specification of a comparison window which determines the scale of the estimate. In this work we present a real-time method for pedestrian recognition and tracking in sequences of high resolution images obtained by a stationary (high definition) camera located in different places on the Haram mosque in Mecca. The objective is to estimate pedestrian velocities as a function of the local density.The resulting data of tracking moving pedestrians based on video sequences are presented in the following section. Through the evaluated system the spatio-temporal coordinates of each pedestrian during the Tawaf ritual are established. The pilgrim velocities as function of

While some studies have proven that Swin Transformer (Swin) with window self-attention (WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad regions when reconstructing high-resolution images due to a limited receptive field. In addition, many deep learning SR methods suffer from intensive computations. To address these problems, we introduce the N-Gram context to the low-level vision with Transformers for the first time. We define N-Gram as neighboring local windows in Swin, which differs from text analysis that views N-Gram as consecutive characters or words. N-Grams interact with each other by sliding-WSA, expanding the regions seen to restore degraded pixels. Using the N-Gram context, we propose NGswin, an efficient SR network with SCDP bottleneck taking multi-scale outputs of the hierarchical encoder. Experimental results show that NGswin achieves competitive performance while maintaining an efficient structure when compared with previous leading methods. Moreover, we also improve other Swin-based SR methods with the N-Gram context, thereby building an enhanced model: SwinIR-NG. Our improved SwinIR-NG outperforms the current best lightweight