Miracle-dz/UniFAR-FTUs huggingface dataset:
RobustiQ: A Robust ANN Search Method for Billion-scale Similarity Search on GPUs

GPU-based methods represent state-of-the-art in approximate nearest neighbor (ANN) search, as they are scalable (billion-scale), accurate (high recall) as well as efficient (sub-millisecond query speed). Faiss, the representative GPU-based ANN system, achieves considerably faster query speed than the representative CPU-based systems. The query accuracy of Faiss critically depends on the number of indexing regions, which in turn is dependent on the amount of available memory. At the same time, query speed deteriorates dramatically with the increase in the number of partition regions. Thus, it can be observed that Faiss suffers from a lack of robustness, that the fine-grained partitioning of datasets is achieved at the expense of search speed, and vice versa. In this paper, we introduce a new GPU-based ANN search method, Robust Quantization (RobustiQ), that addresses the robustness limitations of existing GPU-based methods in a holistic way. We design a novel hierarchical indexing structure using vector and bilayer line quantization. This indexing structure, together with our indexing and encoding methods, allows RobustiQ to avoid the need for maintaining a large lookup table, hence reduces not only memory consumption but also query complexity. Our extensive evaluation on two public billion-scale benchmark datasets, SIFT1B and DEEP1B, shows that RobustiQ consistently obtains 2-3 × speedup over Faiss while achieving better query accuracy for different codebook sizes. Compared to the best CPU-based ANN systems, RobustiQ achieves even more pronounced average speedups of 51.8 × and 11 × respectively.
EB-Boost: Medium-Long Term Forecasting Method of Supplies of Shared E-Cars in Smart Scenic Spots

At present, shared e-cars have become an important part of traffic in scenic spots. While shared e-cars bring convenience to tourists, they also exert a certain influence on the management of the traffic environment in scenic spots. Nowadays, the threshold for shared e-cars is low, and all business operators occupy the market share of the scenic spots and put the shared-cars on the market without plans. This behavior takes up the limited space, exacerbating the congestion of scenic space. Therefore, predicting the amount of shared e-cars to be supplied by a scenic spot in the near future accurately is of great significance for major business entities to do resource scheduling, reduce operating costs, and create an open and coordinated traffic environment in smart scenic spots. However, the traditional time-series prediction models such as AutoRegressive and Moving Average Model, Holt-Winters, and Long ShortTerm Memory can only be used for short-term rough predictions, and cannot be available under some special circumstances such as holidays or rush hours. In our work, we proposed EB-Boost, an ensemble learning method using feature coding based on target. The EB-Boost studied from historical data such as weather data, timestamp data and business data and established probability model and learned relationship between features and targets. We used the data of shared e-cars operated by Roboy Technology company to build a model, and compared the prediction results of EB-Boost with traditional time-series algorithms and neural network algorithms. Finally, we also discuss the robustness of the model and the predictive effect of shared e-cars in other scenic spots.
Improving What Cross-Modal Retrieval Models Learn through Object-Oriented Inter- and Intra-Modal Attention Networks

Although significant progress has been made for cross-modal retrieval models in recent years, few have explored what those models truly learn and what makes one model superior to another. Start by training two state-of-the-art text-to-image retrieval models with adversarial text inputs, we investigate and quantify the importance of syntactic structure and lexical information in learning the joint visual-semantic embedding space for cross-modal retrieval. The results show that the retrieval power mainly comes from localizing and connecting the visual objects and their cross-modal counter-parts, the textual phrases. Inspired by this observation, we propose a novel model which employs object-oriented encoders along with inter- and intra-modal attention networks to improve inter-modal dependencies for cross-modal retrieval. In addition, we develop a new multimodal structure-preserving objective which additionally emphasizes intra-modal hard negative examples to promote intra-modal discrepancies. Extensive experiments show that the proposed approach outperforms the existing best method by a large margin (16.4% and 6.7% relatively with Recall@1 in the text-to-image retrieval task on the Flickr30K dataset and the MS-COCO dataset respectively).
An Implementable Accelerated Alternating Direction Method of Multipliers for Low-Rank Tensor Completion

Low-rank tensor completion has attracted much interest in many applications such as image processing, data mining and machine learning. A widely used method is to minimize the sum of nuclear norms of unfolding matrices of a tensor. In this paper, we study a accelerated alternating direction method of multipliers (ADMM) for solving the sum of nuclear norms of unfolding matrices of a tensor. The basic idea of accelerated ADMM is to incorporate a multistep acceleration scheme into the classical ADMM. We design efficient implementations of the algorithm and present its convergence result. Extensive numerical examples on both random and real world data are presented to validate the superiority of our proposed algorithm over class ADMM.
Privacy-Preserving Searchable Encryption Framework for Permissioned Blockchain Networks

Enabling keyword search directly over the data stored on the blockchain is a desirable technique that can help in the effective utilization of the data while preserving the privacy. Searchable Encryption (SE) is a well-known technique that allows search queries over the encrypted Cloud data, however, existing solutions are based on the assumption of the Cloud Server being “trusted-but-curious” or “honest-but-curious”. This leads to a compelling case to use permissioned blockchain technology to ensure greater levels of security when the Cloud Server is malicious. The amalgamation of SE and permissioned blockchain empowers a client to place complete trust on the Cloud Server and the services it has to offer. This paper presents a novel privacy - preserving framework to facilitate keyword search over encrypted data stored on the blockchain network. The framework for the first time studies SE over a permissioned blockchain network i.e., Hyperledger-Fabric. The SE scheme is privacy-preserving as it is based on probabilistic trapdoors. As a result the framework guarantees prominent security and privacy gains.
Generating piecewise-regular code from irregular structures

Irregular data structures, as exemplified with sparse matrices, have proved to be essential in modern computing. Numerous sparse formats have been investigated to improve the overall performance of Sparse Matrix-Vector multiply (SpMV). But in this work we propose instead to take a fundamentally different approach: to automatically build sets of regular sub-computations by mining for regular sub-regions in the irregular data structure. Our approach leads to code that is specialized to the sparsity structure of the input matrix, but which does not need anymore any indirection array, thereby improving SIMD vectorizability. We particularly focus on small sparse structures (below 10M nonzeros), and demonstrate substantial performance improvements and compaction capabilities compared to a classical CSR implementation and Intel MKL IE's SpMV implementation, evaluating on 200+ different matrices from the SuiteSparse repository.
Cosmic ray acceleration in hydromagnetic flux tubes

We find that hydromagnetic flux tubes in back-flows in the lobes of radio galaxies offer a suitable environment for the acceleration of cosmic rays (CR) to ultra-high energies. We show that CR can reach the Hillas (1984) energy even if the magnetised turbulence in the flux tube is not sufficiently strong for Bohm diffusion to apply. First-order Fermi acceleration by successive weak shocks in a hydromagnetic flux tube is shown to be equivalent to second-order Fermi acceleration by strong turbulence.
Experimental simultaneous readout of the real and imaginary parts of the weak value

The weak value, the average result of a weak measurement, has proven useful for probing quantum and classical systems. Examples include the amplification of small signals, investigating quantum paradoxes, and elucidating fundamental quantum phenomena such as geometric phase. A key characteristic of the weak value is that it can be complex, in contrast to a standard expectation value. However, typically only either the real or imaginary component of the weak value is determined in a given experimental setup. Weak measurements can be used to, in a sense, simultaneously measure non-commuting observables. This principle was used in the direct measurement of the quantum wavefunction. However, the wavefunction's real and imaginary components, given by a weak value, are determined in different setups or on separate ensembles of systems, putting the procedure's directness in question. To address these issues, we introduce and experimentally demonstrate a general method to simultaneously read out both components of the weak value in a single experimental apparatus. In particular, we directly measure the polarization state of an ensemble of photons using weak measurement. With our method, each photon contributes to both the real and imaginary parts of the weak-value average. On a fundamental level, this suggests that the full complex weak value is a characteristic of each photon measured.
Symmetry of Positive Solutions for the Fractional Schr$ \ddot{\textrm{o}}$dinger Equations with Choquard-type Nonlinearities

This paper deals with the following fractional Schr$ \ddot{\textrm{o}}$dinger equations with Choquard-type nonlinearities \begin{equation*} \left\{\begin{array}{r@{\ \ }c@{\ \ }ll} (-\Delta)^{\frac{\alpha}{2}}u + u - C_{n,-\beta} \,(|x|^{\beta-n}\ast u^{p})\, u^{p-1}& = &0 & \mbox{in}\ \ \mathbb{R}^{n}\,, \\[0.05cm] u & > & 0 & \mbox{on}\ \ \mathbb{R}^{n}, \end{array}\right. \end{equation*} where $ 0< \alpha,\beta < 2, 1\leq p <\infty \,\,and\,\, n\geq 2. $ First we construct a decay result at infinity and a narrow region principle for related equations. Then we establish the radial symmetry of positive solutions for the above equation with the generalized direct method of moving planes.
Cross-Modal Interaction Networks for Query-Based Moment Retrieval in Videos

Query-based moment retrieval aims to localize the most relevant moment in an untrimmed video according to the given natural language query. Existing works often only focus on one aspect of this emerging task, such as the query representation learning, video context modeling or multi-modal fusion, thus fail to develop a comprehensive system for further performance improvement. In this paper, we introduce a novel Cross-Modal Interaction Network (CMIN) to consider multiple crucial factors for this challenging task, including (1) the syntactic structure of natural language queries; (2) long-range semantic dependencies in video context and (3) the sufficient cross-modal interaction. Specifically, we devise a syntactic GCN to leverage the syntactic structure of queries for fine-grained representation learning, propose a multi-head self-attention to capture long-range semantic dependencies from video context, and next employ a multi-stage cross-modal interaction to explore the potential relations of video and query contents. The extensive experiments demonstrate the effectiveness of our proposed method.
CBSigIDS: Towards Collaborative Blockchained Signature-Based Intrusion Detection

Intrusion detection systems (IDSs) are one of the most important security mechanisms that help identify various attacks. To enhance the detection performance of a single IDS, collaborative intrusion detection systems or networks (CIDSs or CIDNs) are often implemented in practical organizations, which encourage a set of IDS nodes to exchange information like alarms and signatures with each other. However, due to the distributed nature, malicious nodes within such collaborative network are able to generate untruthful signatures and share to others. This may significantly degrade the effectiveness and efficiency of detection. Recently, blockchain technology has received much attention from both academia and industry, which can provide a verifiable manner for distributed architectures without the need of a trusted intermediary. In this work, our motivation is thus to develop CBSigIDS, a generic framework of collaborative blockchained signature-based IDSs, which utilizes blockchains to help incrementally update a trusted signature database for different IDS nodes in a collaborative network. In the evaluation, our results show that blockchain technology can indeed help enhance the robustness and effectiveness of signature-based IDSs under adversarial scenarios via building a trusted signature database.
Lipschitz stability estimate and reconstruction of Lamé parameters in linear elasticity

In this paper, we consider the inverse problem of recovering an isotropic elastic tensor from the Neumann-to-Dirichlet map. To this end, we prove a Lipschitz stability estimate for Lamé parameters with certain regularity assumptions. In addition, we assume that the Lamé parameters belong to a known finite subspace with a priori known bounds and that they fulfil a monotonicity property. The proof relies on a monotonicity result combined with the techniques of localized potentials. To numerically solve the inverse problem, we propose a Kohn-Vogelius-type cost functional over a class of admissible parameters subject to two boundary value problems. The reformulation of the minimization problem via the Neumann-to-Dirichlet operator allows us to obtain the optimality conditions by using the Fréchet differentiability of this operator and its inverse. The reconstruction is then performed by means of an iterative algorithm based on a quasi-Newton method. Finally, we give and discuss several numerical examples.
Statistical solutions of hyperbolic systems of conservation laws: numerical approximation

Statistical solutions are time-parameterized probability measures on spaces of integrable functions, which have been proposed recently as a framework for global solutions and uncertainty quantification for multi-dimensional hyperbolic system of conservation laws. By combining high-resolution finite volume methods with a Monte Carlo sampling procedure, we present a numerical algorithm to approximate statistical solutions. Under verifiable assumptions on the finite volume method, we prove that the approximations, generated by the proposed algorithm, converge in an appropriate topology to a statistical solution. Numerical experiments illustrating the convergence theory and revealing interesting properties of statistical solutions are also presented.
Real-time Electric Vehicle Classification for Electric Charging and Parking System Using Pre-trained Convolutional Neural Network

Deep learning can be applied on vehicle image classification diverse purposes such as real-time vehicle recognition, vehicle license plate character recognition, vehicle logo identification, and vehicle color classification. We categorize electric vehicle model images according to electric vehicle types and classify each image using Convolutional Neural Network for electric vehicle parking and charging system. The suggested model can support optimization problem of charging equipment installation and usage by recognizing the electric vehicle type real-time basis at the entrance of parking or charging system and directing each vehicle where to park and charge as each has different charge method and needs different charging equipment. The model is built using transfer learning, that is, we pre-train the network with large dataset compensating the small amount of main dataset and then fine-tune the network with our electric vehicle type dataset. We perform 10-fold experiments and achieve final test accuracy of 77.6%.
How to diagnose hantavirus infections and detect them in rodents and insectivores

Hantaviruses are carried by rodents and insectivores in which they cause persistent and generally asymptomatic infections. Several hantaviruses can infect humans and many of them cause either haemorrhagic fever with renal syndrome (HFRS) in Eurasia or hantavirus cardiopulmonary syndrome (HCPS) in the Americas. In humans hantavirus infections are diagnosed using IgM‐capture tests but also by RT‐PCR detection of viral RNA. For detection of hantavirus infections in rodents and insectivores, serology followed by immunoblotting of, for example, lung tissue, and RT‐PCR detection of viral RNA may be used, and if of interest followed by sequencing and virus isolation. For sero/genotyping of hantavirus infections in humans and carrier animals neutralisation tests/RNA sequencing are required. Hantaviruses are prime examples of emerging and re‐emerging infections and it seems likely that many new hantaviruses will be detected in the near future. Copyright © 2008 John Wiley & Sons, Ltd.
Active Testing: Predicting and Confirming Concurrency Bugs for Concurrent and Distributed Memory Parallel Systems

Author(s): Park, Chang Seo | Advisor(s): Sen, Koushik | Abstract: Parallel and concurrent software sometimes exhibit incorrect behavior because ofunintended interference between different threads of execution. Common classesof concurrency bugs include data races, deadlocks, and atomicity violations.These bugs are often non-deterministic and hard to find without sophisticatedtools. We present Active Testing, a methodology to effectively find concurrencybugs that scales to large distributed memory parallel systems.Active Testing combines the coverage and predictive power of program analysiswith the familiarity of testing. It works in two phases: in the predictiveanalysis phase, a program is executed and monitored for potential concurrencybugs and in the testing phase, Active Testing re-executes the program whilecontrolling the thread schedules in an attempt to reproduce the bug predicted inthe first phase. We have implemented Active Testing for multi-threaded Java programs in theCalFuzzer framework. We have also developed UPC-Thrille, an Active Testing frameworkfor Unified Parallel C (UPC) programs written in the Single Program MultipleData (SPMD) programming model combined with the Partitioned Global Address Space(PGAS) memory model. We explain in detail the design decisions and optimizationsthat were necessary to scale Active Testing to thousands of cores. We presentextensions to UPC-Thrille that support hybrid memory models as well. We evaluate the effectiveness of Active Testing by running our tools on severalJava and UPC benchmarks, showing that it can predict and confirm realconcurrency bugs with low overhead. We demonstrate the scalability of ActiveTesting by running benchmarks with UPC-Thrille on large clusters with thousandsof cores.
Optimizing Rescheduling Intervals Through Using Multi-Armed Bandit Algorithms

Well scheduling in oil and gas production in a virtual enterprise is a distributed and online scheduling problem. For such a scheduling problem, planned schedules are subject to unexpected disruptions or under- or over- estimated completion times. To reduce the impact of these uncertain events, schedule revision is necessary to keep the current schedule feasible and optimal in productivity. However, even though frequent schedule revisions may maximize the number of well tasks, it can also increase machine setup and transportation costs. This indicates the necessity of designing a systematic strategy for determining when to carry out schedule revisions. There is no trivial solution to this problem. In this research, we propose an approach to rescheduling interval determination through using a reinforcement learning — multiarmed bandit model. A set of experiments is conducted in a multiagent simulation environment. The results of the experiment demonstrate the effectiveness of the proposed approach in detecting optimal rescheduling intervals.
On the Effectiveness of Laser Speckle Contrast Imaging and Deep Neural Networks for Detecting Known and Unknown Fingerprint Presentation Attacks

Fingerprint presentation attack detection (FPAD) is becoming an increasingly challenging problem due to the continuous advancement of attack techniques, which generate "realistic-looking" fake fingerprint presentations. Recently, laser speckle contrast imaging (LSCI) has been introduced as a new sensing modality for FPAD. LSCI has the interesting characteristic of capturing the blood flow under the skin surface. Toward studying the importance and effectiveness of LSCI for FPAD, we conduct a comprehensive study using different patch-based deep neural network architectures. Our studied architectures include 2D and 3D convo-lutional networks as well as a recurrent network using long short-term memory (LSTM) units. The study demonstrates that strong FPAD performance can be achieved using LSCI. We evaluate the different models over a new large dataset. The dataset consists of 3743 bona fide samples, collected from 335 unique subjects, and 218 presentation attack samples, including six different types of attacks. To examine the effect of changing the training and testing sets, we conduct a 3-fold cross validation evaluation. To examine the effect of the presence of an unseen attack, we apply a leave-one-attack out strategy. The FPAD classification results of the networks, which are separately optimized and tuned for the temporal and spatial patch-sizes, indicate that the best performance is achieved by LSTM.
Insights into formation scenarios of massive early-type galaxies from spatially resolved stellar population analysis in CALIFA

We perform spatially resolved stellar population analysis for a sample of 69 early-type galaxies (ETGs) from the CALIFA integral field spectroscopic survey, including 48 ellipticals and 21 S0’s. We generate and quantitatively characterize profiles of light-weighted mean stellar age and metallicity within ≲2Re, as a function of radius and stellar-mass surface density μ*. We study in detail the dependence of profiles on galaxies’ global properties, including velocity dispersion σe, stellar mass, morphology. ETGs are universally characterized by strong, negative metallicity gradients ($\sim \! -0.3\, \text{dex}$ per Re) within 1Re, which flatten out moving towards larger radii. A quasi-universal local μ*–metallicity relation emerges, which displays a residual systematic dependence on σe, whereby higher σe implies higher metallicity at fixed μ*. Age profiles are typically U-shaped, with minimum around 0.4 Re, asymptotic increase to maximum ages beyond $\sim 1.5\, $Re, and an increase towards the centre. The depth of the minimum and the central increase anticorrelate with σe. A possible qualitative interpretation of these observations is a two-phase scenario. In the first phase, dissipative collapse occurs in the inner 1Re, establishing a negative metallicity gradient. The competition between the outside-in quenching due to feedback-driven winds and some form of inside-out quenching, possibly caused by central AGN feedback or dynamical heating, determines the U-shaped age profiles. In the second phase, the accretion of ex-situ stars from quenched and low-metallicity satellites shapes the flatter stellar population profiles in the outer regions.