Miracle-dz/UniFAR-FTUs huggingface dataset:
maskGRU: Tracking Small Objects in the Presence of Large Background Motions

We propose a recurrent neural network-based spatio-temporal framework named maskGRU for the detection and tracking of small objects in videos. While there have been many developments in the area of object tracking in recent years, tracking a small moving object amid other moving objects and actors (such as a ball amid moving players in sports footage) continues to be a difficult task. Existing spatio-temporal networks, such as convolutional Gated Recurrent Units (convGRUs), are difficult to train and have trouble accurately tracking small objects under such conditions. To overcome these difficulties, we developed the maskGRU framework that uses a weighted sum of the internal hidden state produced by a convGRU and a 3-channel mask of the tracked object's predicted bounding box as the hidden state to be used at the next time step of the underlying convGRU. We believe the technique of incorporating a mask into the hidden state through a weighted sum has two benefits: controlling the effect of exploding gradients and introducing an attention-like mechanism into the network by indicating where in the previous video frame the object is located. Our experiments show that maskGRU outperforms convGRU at tracking objects that are small relative to the video resolution even in the presence of other moving objects.
Clustering-based Partitioning for Large Web Graphs

Graph partitioning plays a vital role in distributed large-scale web graph analytics, such as pagerank and label propagation. The quality and scalability of partitioning strategy have a strong impact on such communication- and computation-intensive applications, since it drives the communication cost and the workload balance among distributed computing nodes. Recently, the streaming model shows promise in optimizing graph partitioning. However, existing streaming partitioning strategies either lack of adequate quality or fall short in scaling with a large number of partitions. In this work, we explore the property of web graph clustering and propose a novel restreaming algorithm for vertex-cut partitioning. We investigate a series of techniques, which are pipelined as three steps, streaming clustering, cluster partitioning, and partition transformation. More, these techniques can be adapted to a parallel mechanism for further acceleration of partitioning. Experiments on real datasets and real systems show that our algorithm outperforms state-of-the-art vertex-cut partitioning methods in large-scale web graph processing. Surprisingly, the runtime cost of our method can be an order of magnitude lower than that of one-pass streaming partitioning algorithms, when the number of partitions is large.
Multi-Fidelity Bayesian Experimental Design to Quantify Extreme-Event Statistics

In this work, we develop a multi-fidelity Bayesian experimental design framework to efficiently quantify the extreme-event statistics of an input-to-response (ItR) system with given input probability and expensive function evaluations. The key idea here is to leverage low-fidelity samples whose responses can be computed with a cost of a certain fraction of that for high-fidelity samples, in an optimized configuration to reduce the total computational cost. To accomplish this goal, we employ a multi-fidelity Gaussian process as the surrogate model of the ItR function, and develop a new acquisition based on which the optimized next sample can be selected in terms of its location in the sample space and the fidelity level. In addition, we develop an inexpensive analytical evaluation of the acquisition and its derivative, avoiding numerical integrations that are prohibitive for high-dimensional problems. The new method is tested in a bi-fidelity context for a series of synthetic problems with varying dimensions, low-fidelity model accuracy and computational costs. Comparing with the single-fidelity method and the bi-fidelity method with a pre-defined fidelity hierarchy, our method consistently shows the best (or among the best) performance for all the test cases. Finally, we demonstrate the superiority of our method in solving an engineering problem of estimating the extreme ship motion statistics in irregular waves, using computational fluid dynamics (CFD) with two different grid resolutions as the high and low fidelity models.
Optimization on Multiuser Physical Layer Security of Intelligent Reflecting Surface-Aided VLC

This letter investigates physical layer security in intelligent reflecting surface (IRS)-aided VL communication (VLC). Under the point source assumption, we first elaborate the system model in the scenario with multiple legitimate users and one eavesdropper, where the secrecy rate maximization problem is transformed into an assignment problem by objective function approximation. Then, an iterative Kuhn-Munkres algorithm is proposed to optimize the transformed problem, and its computational complexity is in the second-order form of the numbers of IRS units and transmitters. Moreover, numerical simulations are carried out to verify the approximation performance and the VLC secrecy rate improvement by IRS.
Parkour Spot ID: Feature Matching in Satellite and Street view images using Deep Learning

How to find places that are not indexed by Google Maps? We propose an intuitive method and framework to locate places based on their distinctive spatial features. The method uses satellite and street view images in machine vision approaches to classify locations. If we can classify locations, we just need to repeat for non-overlapping locations in our area of interest. We assess the proposed system in finding Parkour spots in the campus of Arizona State University. The results are very satisfactory, having found more than 25 new Parkour spots, with a rate of true positives above 60%.
CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution

Multi-touch attribution (MTA), aiming to estimate the contribution of each advertisement touchpoint in conversion journeys, is essential for budget allocation and automatically advertising. Existing methods first train a model to predict the conversion probability of the advertisement journeys with historical data and calculate the attribution of each touchpoint by using the results counterfactual predictions. An assumption of these works is the conversion prediction model is unbiased. It can give accurate predictions on any randomly assigned journey, including both the factual and counterfactual ones. Nevertheless, this assumption does not always hold as the user preferences act as the common cause for both ad generation and user conversion, involving the confounding bias and leading to an out-of-distribution (OOD) problem in the counterfactual prediction. In this paper, we define the causal MTA task and propose CausalMTA to solve this problem. It systemically eliminates the confounding bias from both static and dynamic perspectives and learn an unbiased conversion prediction model using historical data. We also provide a theoretical analysis to prove the effectiveness of CausalMTA with sufficient ad journeys. Extensive experiments on both synthetic and real data in Alibaba advertising platform show that CausalMTA can not only achieve better prediction performance than the state-of-the-art method but also generate meaningful attribution credits across different advertising channels.
Secure Information Flow Typing in LUSTRE

Synchronous reactive data flow is a paradigm that provides a high-level abstract programming model for embedded and cyber-physical systems, including the locally synchronous components of IoT systems. Security in such systems is severely compromised due to low-level programming, ill-defined interfaces and inattention to security classification of data. By incorporating a Denning-style lattice-based secure information flow framework into a synchronous reactive data flow language, we provide a framework in which correct-and-secure-by-construction implementations for such systems may be specified and derived. In particular, we propose an extension of the Lustre programming framework with a security type system. The novelty of our type system lies in a symbolic formulation of constraints over security type variables, in particular the treatment of node calls, which allows us to reason about secure flow with respect to any security class lattice. The main theorem is the soundness of our type system with respect to the co-inductive operational semantics of Lustre, which we prove by showing that well-typed programs exhibit non-interference. Rather than tackle the full language, we first prove the non-interference result for a well-behaved sub-language called"Normalised Lustre"(NLustre), for which our type system is far simpler. We then show that Bourke et al.'s semantics-preserving"normalisation"transformations from Lustre to NLustre are security-preserving as well. This preservation of security types by the normalisation transformations is a property akin to"subject reduction"but at the level of compiler transformations. The main result that well-security-typed Lustre programs are non-interfering follows from a reduction to our earlier result of non-interference for NLustre via the semantics-preservation (of Bourke et al.) and type preservation results.
ADAPTIVE CONTROL FOR PHB PRODUCTION

A new procedure for adaptive control design of PHB production by mixed culture is proposed. It consists of analytical derivation of the control low from unstructured model of the process. The process information includes on-line measurements of glucose and lactate concentrations in the reactor as well as unstructured model coefficients. Applicability of the proposed procedure is demonstrated by simulation investigations of open-loop control scheme, where the control input - glucose feed rate is calculated using different initial conditions of state variables and physico-chemical parameters.
Algorithm-Level Confidentiality for Average Consensus on Time-Varying Directed Graphs

Average consensus plays a key role in distributed networks, with applications ranging from time synchronization, information fusion, load balancing, to decentralized control. Existing average consensus algorithms require individual agents to exchange explicit state values with their neighbors, which leads to the undesirable disclosure of sensitive information in the state. In this paper, we propose a novel average consensus algorithm for time-varying directed graphs that can protect the confidentiality of a participating agent against other participating agents. The algorithm injects randomness in interaction to obfuscate information on the algorithm-level and can ensure information-theoretic privacy without the assistance of any trusted third party or data aggregator. By leveraging the inherent robustness of consensus dynamics against random variations in interaction, our proposed algorithm can also guarantee the accuracy of average consensus. The algorithm is distinctly different from differential-privacy based average consensus approaches which enable confidentiality through compromising accuracy in obtained consensus value. Numerical simulations confirm the effectiveness and efficiency of our proposed approach.
On the Cross-dataset Generalization in License Plate Recognition

Automatic License Plate Recognition (ALPR) systems have shown remarkable performance on license plates (LPs) from multiple regions due to advances in deep learning and the increasing availability of datasets. The evaluation of deep ALPR systems is usually done within each dataset; therefore, it is questionable if such results are a reliable indicator of generalization ability. In this paper, we propose a traditional-split versus leave-one-dataset-out experimental setup to empirically assess the cross-dataset generalization of 12 Optical Character Recognition (OCR) models applied to LP recognition on nine publicly available datasets with a great variety in several aspects (e.g., acquisition settings, image resolution, and LP layouts). We also introduce a public dataset for end-to-end ALPR that is the first to contain images of vehicles with Mercosur LPs and the one with the highest number of motorcycle images. The experimental results shed light on the limitations of the traditional-split protocol for evaluating approaches in the ALPR context, as there are significant drops in performance for most datasets when training and testing the models in a leave-one-dataset-out fashion.
Impact of Evolving Protocols and COVID-19 on Internet Traffic Shares

The rapid deployment of new Internet protocols over the last few years and the COVID-19 pandemic more recently (2020) has resulted in a change in the Internet traffic composition. Consequently, an updated microscopic view of traffic shares is needed to understand how the Internet is evolving to capture both such shorter- and longer-term events. Toward this end, we observe traffic composition at a research network in Japan and a Tier-1 ISP in the USA. We analyze the traffic traces passively captured at two inter-domain links: MAWI (Japan) and CAIDA (New York-Sao Paulo), which cover 100GB of data for MAWI traces and 4TB of data for CAIDA traces in total. We begin by studying the impact of COVID-19 on the MAWI link: We find a substantial increase in the traffic volume of OpenVPN and rsync, as well as increases in traffic volume from cloud storage and video conferencing services, which shows that clients shift to remote work during the pandemic. For traffic traces between March 2018 to December 2018, we find that the use of IPv6 is increasing quickly on the CAIDA monitor: The IPv6 traffic volume increases from 1.1% in March 2018 to 6.1% in December 2018, while the IPv6 traffic share remains stable in the MAWI dataset at around 9% of the traffic volume. Among other protocols at the application layer, 60%-70% of IPv4 traffic on the CAIDA link is HTTP(S) traffic, out of which two-thirds are encrypted;for the MAWI link, more than 90% of the traffic is Web, of which nearly 75% is encrypted. Compared to previous studies, this depicts a larger increase in encrypted Web traffic of up to a 3-to-1 ratio of HTTPS to HTTP. As such, our observations in this study further reconfirm that traffic shares change with time and can vary greatly depending on the vantage point studied despite the use of the same generalized methodology and analyses, which can also be applied to other traffic monitoring datasets.
Investigating Cargo Loss in Logistics Systems using Low-Cost Impact Sensors

Cargo loss/damage is a very common problem faced by almost any business with a supply chain arm, leading to major problems like revenue loss and reputation tarnishing. This problem can be solved by employing an asset and impact tracking solution. This would be more practical and effective for high-cost cargo in comparison to low-cost cargo due to the high costs associated with the sensors and overall solution. In this study, we propose a low-cost solution architecture that is scalable, user-friendly, easy to adopt and is viable for a large range of cargo and logistics systems. Taking inspiration from a real-life use case we solved for a client, we also provide insights into the architecture as well as the design decisions that make this a reality.
Deterministic Grover search with a restricted oracle

Grover’s quantum search algorithm provides a quadratic quantum advantage over classical algorithms across a broad class of unstructured search problems. The original protocol is probabilistic, returning the desired result with signiﬁcant probability on each query, but in general, requiring several iterations of the algorithm. We present a modiﬁed version to return the correct result with certainty without having user control over the quantum search oracle. Our deterministic, two-parameter “D2p” protocol utilizes generalized phase rotations replacing the phase inversions after a standard oracle query. The D2p protocol achieves a 100% success rate in no more than one additional iteration compared to the optimal number of steps as in the original Grover’s search enabling the same quadratic speed up. We also provide a visualization using the Bloch sphere for enhanced geometric intuition.
Cosmic rays from massive star clusters : A close look at Westerlund 1

We study the effect of cosmic ray (CR) acceleration in the massive compact star cluster Westerlund 1 in light of its recent detection in γ-rays. Recent observations reveal a 1/r radial distribution of the CR energy density. Here we theoretically investigate whether or not this profile can help to distinguish between (1) continuous CR acceleration in the star cluster stellar wind-driven shocks and (2) discrete CR acceleration in multiple supernovae shocks – which are often debated in the literature. Using idealized two-fluid simulations and exploring different acceleration sites and diffusion coefficients, we obtain the CR energy density profile and luminosity to find the best match for the γ-ray observations. We find that the inferred CR energy density profiles from observations of γ-ray luminosity and mass can be much different from the true radial profile. CR acceleration at either the cluster core region or the wind termination shock can explain the observations, if the diffusion coefficient is κcr ∼ 10 cm s−1 and a fraction of ≈ 10% − 20% of the shock power/post-shock pressure is deposited into the CR component. We also study the possibility of discrete supernovae (SN) explosions being responsible for CR acceleration and find that with an injection rate of 1 SN in every ∼ 0.03 Myr, one can explain the observed γ-ray profile. This multiple SN scenario is consistent with X-ray observations only if the thermal conductivity is close to the Spitzer value.
Charged lepton flavor violation from beyond standard model particles

We summarize the potential charged lepton flavor violation (LFV) from some representative beyond standard model (BSM) particles, i.e. the BSM neutral scalar H, doubly-charged scalar H±±, heavy neutrino N, heavy WR boson, and the Z′ boson. For the neutral scalar, doubly-charged scalar and Z′ boson, the LFV signals originate from the (effective) LFV couplings of these particles to the charged leptons, while for the heavy neutrino N and WR boson, the LFV effects are from flavor mixing in the neutrino sector. We consider current limits on these BSM particles and estimate their prospects in future experiments.
Kernel two-sample tests in high dimensions: interplay between moment discrepancy and dimension-and-sample orders

Motivated by the increasing use of kernel-based metrics for high-dimensional and large-scale data, we study the asymptotic behaviour of kernel two-sample tests when the dimension and sample sizes both diverge to infinity. We focus on the maximum mean discrepancy using an isotropic kernel, which includes maximum mean discrepancy with the Gaussian kernel and the Laplace kernel, and the energy distance as special cases. We derive asymptotic expansions of the kernel two-sample statistics, based on which we establish a central limit theorem under both the null hypothesis and the local and fixed alternatives. The new nonnull central limit theorem results allow us to perform asymptotic exact power analysis, which reveals a delicate interplay between the moment discrepancy that can be detected by the kernel two-sample tests and the dimension-and-sample orders. The asymptotic theory is further corroborated through numerical studies.
TFCN: Temporal-Frequential Convolutional Network for Single-Channel Speech Enhancement

Deep learning based single-channel speech enhancement tries to train a neural network model for the prediction of clean speech signal. There are a variety of popular network structures for single-channel speech enhancement, such as TCNN, UNet, WaveNet, etc. However, these structures usually contain millions of parameters, which is an obstacle for mobile applications. In this work, we proposed a light weight neural network for speech enhancement named TFCN. It is a temporal-frequential convolutional network constructed of dilated convolutions and depth-separable convolutions. We evaluate the performance of TFCN in terms of Short-Time Objective Intelligibility (STOI), perceptual evaluation of speech quality (PESQ) and a series of composite metrics named Csig, Cbak and Covl. Experimental results show that compared with TCN and several other state-of-the-art algorithms, the proposed structure achieves a comparable performance with only 93,000 parameters. Further improvement can be achieved at the cost of more parameters, by introducing dense connections and depth-separable convolutions with normal ones. Experiments also show that the proposed structure can work well both in causal and non-causal situations.
Augmentative eXplanation and the Distributional Gap of Conﬁdence Optimization Score

This paper introduces the Conﬁdence Optimization (CO) score to directly measure the contribution of heatmaps/saliency maps to the classiﬁcation performance of a model. Common heatmap generation methods used in the eXplainable Ar-tiﬁcial Intelligence (XAI) community are tested through a process we call the Augmentative eX-planation (AX). We ﬁnd a surprising gap in CO scores distribution on these heatmap methods. The gap potentially serves as a novel indicator for the correctness of deep neural network (DNN) prediction. We further introduces Generative AX (GAX) method to generate saliency maps capable of attaining high CO scores. Using GAX, we also qualitatively demonstrate the unintuitiveness of DNN architectures.
Sample complexity of learning parametric quantum circuits

Quantum computers hold unprecedented potentials for machine learning applications. Here, we prove that physical quantum circuits are probably approximately correct learnable on a quantum computer via empirical risk minimization: to learn a parametric quantum circuit with at most n c gates and each gate acting on a constant number of qubits, the sample complexity is bounded by O~(nc+1) . In particular, we explicitly construct a family of variational quantum circuits with O(n c+1) elementary gates arranged in a fixed pattern, which can represent all physical quantum circuits consisting of at most n c elementary gates. Our results provide a valuable guide for quantum machine learning in both theory and practice.
Cluster Stability Selection

Stability selection [Meinshausen and Bühlmann, 2010] makes any feature selection method more stable by returning only those features that are consistently selected across many subsamples. We prove (in what is, to our knowledge, the first result of its kind) that for data containing highly correlated proxies for an important latent variable, the lasso typically selects one proxy, yet stability selection with the lasso can fail to select any proxy, leading to worse predictive performance than the lasso alone. We introduce cluster stability selection, which exploits the practitioner’s knowledge that highly correlated clusters exist in the data, resulting in better feature rankings than stability selection in this setting. We consider several feature-combination approaches, including taking a weighted average of the features in each important cluster where weights are determined by the frequency with which cluster members are selected, which we show leads to better predictive models than previous proposals. We present generalizations of theoretical guarantees from Meinshausen and Bühlmann [2010] and Shah and Samworth [2012] to show that cluster stability selection retains the same guarantees. In summary, cluster stability selection enjoys the best of both worlds, yielding a sparse selected set that is both stable and has good predictive performance.