Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking Benchmarks for Cross-modal Image-text Retrieval

Image-text retrieval, as a fundamental and important branch of information retrieval, has attracted extensive research attentions. The main challenge of this task is cross-modal semantic understanding and matching. Some recent works focus more on fine-grained cross-modal semantic matching. With the prevalence of large scale multimodal pretraining models, several state-of-the-art models (e.g. X-VLM) have achieved near-perfect performance on widely-used image-text retrieval benchmarks, i.e. MSCOCO-Test-5K and Flickr30K-Test-1K. In this paper, we review the two common benchmarks and observe that they are insufficient to assess the true capability of models on fine-grained cross-modal semantic matching. The reason is that a large amount of images and texts in the benchmarks are coarse-grained. Based on the observation, we renovate the coarse-grained images and texts in the old benchmarks and establish the improved benchmarks called MSCOCO-FG and Flickr30K-FG. Specifically, on the image side, we enlarge the original image pool by adopting more similar images. On the text side, we propose a novel semi-automatic renovation approach to refine coarse-grained sentences into finer-grained ones with little human effort. Furthermore, we evaluate representative image-text retrieval models on our new benchmarks to demonstrate the effectiveness of our method. We also analyze the capability of models on fine-grained semantic comprehension through extensive experiments. The results show that even the state-of-the-art models have much room for improvement in fine-grained semantic understanding, especially in distinguishing attributes of close objects in images. Our code and improved benchmark datasets are publicly available1 which we hope will inspire further in-depth research on cross-modal retrieval.
All-optical diode structure based on asymmetrical coupling by a micro-cavity and FP cavity at two sides of photonic crystal waveguide

A high efficiency all-optical diode based on photonic crystal (PC) waveguide has been proposed and numerically investigated by finite-difference time-domain (FDTD) method. The structure is asymmetrically coupled by a micro-cavity containing nonlinear Kerr medium and a FP cavity at sides of PC waveguide. Because of interference between two cavities, Fano peak and FP peak can both appear in transmission spectra and unidirectional transmission can be achieved. The working wavelength can set between two peaks and near to the Fano peak. For forward launch with suitable light intensity, nonlinear Kerr effect of micro-cavity can been excited. It will result in red shift of Fano peak and achieving forward transmission. But for backward launch, a stronger incidence light is needed to the excite Kerr effect due to the design of asymmetric structure. This design has many advantages, including high maximum transmittance, high transmittance contrast ratio, low power threshold, short response time, and ease of integration.
AI Product Security: A Primer for Developers

Not too long ago, AI security used to mean the research and practice of how AI can empower cybersecurity, that is, AI for security. Ever since Ian Goodfellow and his team popularized adversarial attacks on machine learning, security for AI became an important concern and also part of AI security. It is imperative to understand the threats to machine learning products and avoid common pitfalls in AI product development. This article is addressed to developers, designers, managers and researchers of AI software products.
An Introduction to Transformers

The transformer is a neural network component that can be used to learn useful representations of sequences or sets of data-points. The transformer has driven recent advances in natural language processing, computer vision, and spatio-temporal modelling. There are many introductions to transformers, but most do not contain precise mathematical descriptions of the architecture and the intuitions behind the design choices are often also missing. Moreover, as research takes a winding path, the explanations for the components of the transformer can be idiosyncratic. In this note we aim for a mathematically precise, intuitive, and clean description of the transformer architecture. We will not discuss training as this is rather standard. We assume that the reader is familiar with fundamental topics in machine learning including multi-layer perceptrons, linear transformations, softmax functions and basic probability.
A numerical method for the stability analysis of linear age-structured models with nonlocal diffusion

We numerically address the stability analysis of linear age-structured population models with nonlocal diffusion, which arise naturally in describing dynamics of infectious diseases. Compared to Laplace diffusion, models with nonlocal diffusion are more challenging since the associated semigroups have no regularizing properties in the spatial variable. Nevertheless, the asymptotic stability of the null equilibrium is determined by the spectrum of the infinitesimal generator associated to the semigroup. We propose a numerical method to approximate the leading part of this spectrum by first reformulating the problem via integration of the age-state and then by discretizing the generator combining a spectral projection in space with a pseudospectral collocation in age. A rigorous convergence analysis proving spectral accuracy is provided in the case of separable model coefficients. Results are confirmed experimentally and numerical tests are presented also for the more general instance.
A Survey of Prevent and Detect Access Control Vulnerabilities

Broken access control is one of the most common security vulnerabilities in web applications. These vulnerabilities are the major cause of many data breach incidents, which result in privacy concern and revenue loss. However, preventing and detecting access control vulnerabilities proactively in web applications could be difficult. Currently, these vulnerabilities are actively detected by bug bounty hunters post-deployment, which creates attack windows for malicious access. To solve this problem proactively requires security awareness and expertise from developers, which calls for systematic solutions. This survey targets to provide a structured overview of approaches that tackle access control vulnerabilities. It firstly discusses the unique feature of access control vulnerabilities, then studies the existing works proposed to tackle access control vulnerabilities in web applications, which span the spectrum of software development from software design and implementation, software analysis and testing, and runtime monitoring. At last we discuss the open problem in this field.
Prediction of a roton-type feature in warm dense hydrogen

In a recent Letter [T. Dornheim \textit{et al.}, Phys. Rev. Lett. \textbf{121}, 255001 (2018)], it was predicted on the basis of \textit{ab initio} quantum Monte Carlo simulations that, in a uniform electron gas, the peak $\omega_0$ of the dynamic structure factor $S(q,\omega)$ exhibits an unusual non-monotonic wave number dependence, where $d\omega_0/dq<0$, at intermediate $q$, under strong coupling conditions. This effect was subsequently explained by the pair alignment of electrons %at an intermediate range of wave numbers [T. Dornheim \textit{et al.}, Comm. Phys. \textbf{5}, 304 (2022)]. Here we predict that this non-monotonic dispersion resembling the roton-type behavior known from superfluids should be observable in a dense, partially ionized hydrogen plasma. Based on a combination of path integral Monte Carlo simulations and linear response results for the density response function, we present the approximate range of densities, temperatures and wave numbers and make predictions for possible experimental observations.
Nonlocal Lagrangian formalism

This thesis aims to study nonlocal Lagrangians with a finite and an infinite number of degrees of freedom. We obtain an extension of Noether's theorem and Noether's identities for such Lagrangians. We then set up a Hamiltonian formalism for them. Furthermore, we show that $r^{\rm th}$-order Lagrangians can be treated as a particular case, and the expected results are recovered. Finally, the method developed is applied to different examples: nonlocal harmonic oscillator, $p$-adic particle, $p$-adic open string field, and electrodynamics of dispersive media.
PROJECTIVE MONOMIAL

Let A = { a 0 , . , a n − 1 } be a ﬁnite set of n ≥ 4 non-negative relatively prime integers such that 0 = a 0 < a 1 < · · · < a n − 1 = d . The s -fold sumset of A is the set sA of integers that contains all the sums of s elements in A . On the other hand, given an inﬁnite ﬁeld k , one can associate to A the projective monomial curve C A parametrized by A ,
Continuous-Time Analog Filters for Audio Edge Intelligence: Review on Circuit Designs [Feature]

Edge audio devices can reduce data bandwidth requirements by pre-processing input speech on the device before transmission to the cloud. As edge devices are required to ensure always-on operation, their stringent power constraints pose several design challenges and force IC designers to look for solutions that use low standby power. One promising bio-inspired approach is to combine the continuous-time analog filter channels with a small memory footprint deep neural network that is trained on edge tasks such as keyword spotting, thereby allowing all blocks to be embedded in an IC. This paper reviews the historical background of the continuous-time analog filter circuits that have been used as feature extractors for current edge audio devices. Starting from the interpretation of a basic biquad filter as a two-integrator-loop topology, we introduce the progression in the design of secondorder low-pass and band-pass filters ranging from OTA-based to source-follower-based architectures. We also derive and analyze the small-signal transfer function and discuss their usage in edge audio applications.
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images

Incorporating deep-learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts. In such critical situations, the UAV’s ability to promptly comprehend the crisis and optimally utilize its limited power and processing resources to narrow down search areas is crucial. Therefore, developing an efficient and lightweight method for scene classification is of utmost importance. However, current approaches tend to prioritize accuracy on benchmark datasets at the expense of computational efficiency. To address this shortcoming, we introduce the Wider ATTENTION EfficientNet (WATT-EffNet), a novel method that achieves higher accuracy with a more lightweight architecture compared to the baseline EfficientNet. The WATT-EffNet leverages width-wise incremental feature modules and attention mechanisms over width-wise features to ensure the network structure remains lightweight. We evaluate our method on a UAV-based aerial disaster image classification dataset and demonstrate that it outperforms the baseline by up to 10.6% in terms of classification accuracy and 38.3% in terms of computing efficiency as measured by floating-point operations per second (FLOPS). Additionally, we conduct an ablation study to investigate the effect of varying the width of WATT-EffNet on accuracy and computational efficiency. Our code is available at https://github.com/TanmDL/WATT-EffNet.
PowerGAN: A Machine Learning Approach for Power Side‐Channel Attack on Compute‐in‐Memory Accelerators

Analog compute‐in‐memory (CIM) systems are promising candidates for deep neural network (DNN) inference acceleration. However, as the use of DNNs expands, protecting user input privacy has become increasingly important. Herein, a potential security vulnerability is identified wherein an adversary can reconstruct the user's private input data from a power side‐channel attack even without knowledge of the stored DNN model. An attack approach using a generative adversarial network is developed to achieve high‐quality data reconstruction from power leakage measurements. The analyses show that the attack methodology is effective in reconstructing user input data from power leakage of the analog CIM accelerator, even at large noise levels and after countermeasures. To demonstrate the efficacy of the proposed approach, an example of CIM inference of U‐Net for brain tumor detection is attacked, and the original magnetic resonance imaging medical images can be successfully reconstructed even at a noise level of 20% standard deviation of the maximum power signal value. This study highlights a potential security vulnerability in emerging analog CIM accelerators and raises awareness of needed safety features to protect user privacy in such systems.
Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization

This article presents a leak localization methodology based on state estimation and learning. The first is handled by an interpolation scheme, whereas dictionary learning is considered for the second stage. The novel proposed interpolation technique exploits the physics of the interconnections between hydraulic heads of neighboring nodes in water distribution networks. Additionally, residuals are directly interpolated instead of hydraulic head values. The results of applying the proposed method to a well-known case study (Modena) demonstrated the improvements of the new interpolation method with respect to a state-of-the-art approach, both in terms of interpolation error (considering state and residual estimation) and posterior localization.
An Accelerated Proximal Alternating Direction Method of Multipliers for Optimal Decentralized Control of Uncertain Systems

To ensure the system stability of the $\bf{\mathcal{H}_{2}}$-guaranteed cost optimal decentralized control problem (ODC), an approximate semidefinite programming (SDP) problem is formulated based on the sparsity of the gain matrix of the decentralized controller. To reduce data storage and improve computational efficiency, the SDP problem is vectorized into a conic programming (CP) problem using the Kronecker product. Then, a proximal alternating direction method of multipliers (PADMM) is proposed to solve the dual of the resulted CP. By linking the (generalized) PADMM with the (relaxed) proximal point algorithm, we are able to accelerate the proposed PADMM via the Halpern fixed-point iterative scheme. This results in a fast convergence rate for the Karush-Kuhn-Tucker (KKT) residual along the sequence generated by the accelerated algorithm. Numerical experiments further demonstrate that the accelerated PADMM outperforms both the well-known CVXOPT and SCS algorithms for solving the large-scale CP problems arising from $\bf{\mathcal{H}_{2}}$-guaranteed cost ODC problems.
Gradient-Based Distributed Controller Design Over Directed Networks

In this study, we propose a design methodology of distributed controllers for multi-agent systems on a class of directed interaction networks by extending the gradient-flow method. Although the gradient-flow method is a common design tool for distributed controllers, it is inapplicable to directed networks. First, we demonstrate how to construct a distributed controller for systems over a class of time-invariant directed graphs. Subsequently, we establish better convergence properties and performance enhancement than the conventional gradient-flow method. To illustrate its application in time-varying networks, we address the dynamic matching problem of two distinct groups of agents with different sensing ranges. This problem is a novel coordination task that involves pairing agents from two distinct groups to achieve a convergence of the paired agents' states to the same value. Accordingly, we apply the proposed method to this problem and provide sufficient conditions for successful matching. Lastly, numerical examples for systems on both time-invariant and time-varying networks demonstrate the effectiveness of the proposed method.
ION-ACOUSTIC SOLITONS IN ELECTRON POSITRON NONTHERMAL PLASMA

Electron-positron plasmas are believed to exist in the early universe, in active galactic nuclei, and in pulsar magnetospheres. Most of the astrophysical plasmas usually contain ions as well in addition to electrons and positrons. In this work, the ion-acoustic solitons are investigated in three-compone nt plasmas, whose constituents are inertial ions, nonthermal electrons, and Boltzmannian positrons. The properties of stationary structures are studied by pseudopotential approach, which is valid for large amplitude. It is found that the conditions which support the existence of solitons are altered.
Simplest fidelity-estimation method for graph states with depolarizing noise

Graph states are entangled states useful for several quantum information processing tasks such as measurement-based quantum computation and quantum metrology. As the size of graph states realized in experiments increases, it becomes more essential to devise efficient methods estimating the fidelity between the ideal graph state and an experimentally-realized actual state. Any efficient fidelity-estimation method, in general, must use multiple experimental settings, i.e., needs to switch between at least two measurements. Recently, it has been shown that a single measurement is sufficient if the noise can be modeled as the phase-flip error. Since the bit-flip error should also occur in several experiments, it is desired to extend this simplest method to noise models that include phase and bit-flip errors. However, it seems to be nontrivial because their result strongly depends on properties of the phase-flip error. In this paper, by analyzing effects of the bit-flip error on stabilizer operators of graph states, we achieve the extension to the depolarizing noise, which is a major noise model including phase and bit-flip errors. We also numerically evaluate our simplest method for noise models interpolating between the phase-flip and depolarizing noises.
Protection Against Graph-Based False Data Injection Attacks on Power Systems

Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. Recent research has focused on developing GSP-based methods for state estimation, attack detection, and topology identification using the representation of the power system voltages as smooth graph signals. Within this framework, efficient methods have been developed for detecting false data injection (FDI) attacks, which until now were perceived as non-smooth with respect to the graph Laplacian matrix. Consequently, these methods may not be effective against smooth FDI attacks. In this paper, we propose a graph FDI (GFDI) attack that minimizes the Laplacian-based graph total variation (TV) under practical constraints. We present the GFDI attack as the solution for a non-convex constrained optimization problem. The solution to the GFDI attack problem is obtained through approximating it using $\ell_1$ relaxation. A series of quadratic programming problems that are classified as convex optimization problems are solved to obtain the final solution. We then propose a protection scheme that identifies the minimal set of measurements necessary to constrain the GFDI output to a high graph TV, thereby enabling its detection by existing GSP-based detectors. Our numerical simulations on the IEEE-57 and IEEE-118 bus test cases reveal the potential threat posed by well-designed GSP-based FDI attacks. Moreover, we demonstrate that integrating the proposed protection design with GSP-based detection can lead to significant hardware cost savings compared to previous designs of protection methods against FDI attacks.
Supersymmetry in spin glass dynamics

The spin glass dynamic equations can be written in a way that makes the super- symmetry associated with such a Langevin process explicit. In such a framework the fluctuation- dissipation relations and time homogeinity properties are implicit in the symmetries of the action functional. The spin glass phase transition can be discussed in terms of supersymmetry break- ing. The superspace notation appears naturally. In this notation the dynamics is expressed in terms of a single superspace function Q, and the dynamic problem bears a striking formal similarity with its static replica-treatment counterpart. In particular, this similarity can be used to show explicitely that the supersymmetric dynamical solution and the replica symmetric solutions yield the same static results for a wide range of models.
Soft-input soft-output decoding of linear block codes based on ordered statistics

In this paper, reduced-complexity soft-input soft-output decoding of linear block codes is considered. The near-optimum low complexity soft decision decoding algorithm based on ordered statistics of Fossorier and Lin (1995) is modified so that soft outputs are delivered by the decoder. This algorithm performs nearly as well as the max-log-MAP decoding algorithm since in most cases, only the soft outputs corresponding to the least reliable bits may differ. For good (N,K,d/sub H/) block codes of length N/spl les/128, dimension K and minimum Hamming distance dH/spl les/4K, the corresponding decoding complexity is O((N-K)(K+1)n(d/sub H/,K)) real operations with n(d/sub H/,K)=/spl Sigma//sub i=0/([d/sub H//4])(/sub i//sup K/). Consequently, this algorithm allows to achieve practically the same error performance as the max-log-MAP decoding algorithm in iterative or concatenated systems with block codes for which the associated trellis complexity is simply too large for implementation of trellis based decoding algorithms.
N=1 SCFTs from F-theory on Orbifolds

We study four-dimensional superconformal field theories living on the worldvolume of $D3$ branes probing minimally-supersymmetric F-theory backgrounds, focusing on the case of orbi-orientifold setups with and without 7-branes. We observe that these theories are closely related to compactifications of six-dimensional $\mathcal{N}=(1,0)$ theories on a torus with flux, where the flux quanta is mapped in Type IIB to the defining data of the orbifold group. We analyze the cases of class $\mathcal{S}_k$ theories as well as of compactifications of the E-string and of orbi-instanton theories. We also classify $\mathcal{S}$-fold configurations in F-theory preserving minimal supersymmetry in four dimensions and their mass deformations.
Deep Attention Unet: A Network Model with Global Feature Perception Ability

Remote sensing image segmentation is a specific task of remote sensing image interpretation. A good remote sensing image segmentation algorithm can provide guidance for environmental protection, agricultural production, and urban construction. This paper proposes a new type of UNet image segmentation algorithm based on channel self attention mechanism and residual connection called . In my experiment, the new network model improved mIOU by 2.48% compared to traditional UNet on the FoodNet dataset. The image segmentation algorithm proposed in this article enhances the internal connections between different items in the image, thus achieving better segmentation results for remote sensing images with occlusion.