Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Convolutional Neural Network Model For Recognition of Fruits

Objectives: To develop a model for the automatic recognition of fruits utilizing deep learning techniques. Methods: We have designed a fruit classiﬁcation and recognition Model using Convolutional Neural Networks (CNN). We have used excellent quality ImageNet dataset of fruit images for evaluation purpose. It contains 9,130 images of 11 diﬀerent categories. The classiﬁcation is challenging as the images comprise diﬀerent fruits of the same color and shape, overlapped fruits, the background is not homogenous
Semantic Labeling of High-Resolution Images Using EfficientUNets and Transformers

Semantic segmentation necessitates approaches that learn high-level characteristics while dealing with enormous quantities of data. Convolutional neural networks (CNNs) can learn unique and adaptive features to achieve this aim. However, due to the large size and high spatial resolution of remote sensing images, these networks cannot efficiently analyze an entire scene. Recently, deep transformers have proven their capability to record global interactions between different objects in the image. In this article, we propose a new segmentation model that combines CNNs with transformers and show that this mixture of local and global feature extraction techniques provides significant advantages in remote sensing segmentation. In addition, the proposed model includes two fusion layers that are designed to efficiently represent multimodal inputs and outputs of the network. The input fusion layer extracts feature maps summarizing the relationship between image content and elevation maps [digital surface model (DSM)]. The output fusion layer uses a novel multitask segmentation strategy where class labels are identified using class-specific feature extraction layers and loss functions. Finally, a fast-marching method (FMM) is used to convert unidentified class labels into their closest known neighbors. Our results demonstrate that the proposed method improves segmentation accuracy compared with state-of-the-art techniques.
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation

Myopic exploration policies such as epsilon-greedy, softmax, or Gaussian noise fail to explore efficiently in some reinforcement learning tasks and yet, they perform well in many others. In fact, in practice, they are often selected as the top choices, due to their simplicity. But, for what tasks do such policies succeed? Can we give theoretical guarantees for their favorable performance? These crucial questions have been scarcely investigated, despite the prominent practical importance of these policies. This paper presents a theoretical analysis of such policies and provides the first regret and sample-complexity bounds for reinforcement learning with myopic exploration. Our results apply to value-function-based algorithms in episodic MDPs with bounded Bellman Eluder dimension. We propose a new complexity measure called myopic exploration gap, denoted by alpha, that captures a structural property of the MDP, the exploration policy and the given value function class. We show that the sample-complexity of myopic exploration scales quadratically with the inverse of this quantity, 1 / alpha^2. We further demonstrate through concrete examples that myopic exploration gap is indeed favorable in several tasks where myopic exploration succeeds, due to the corresponding dynamics and reward structure.
3-stage and 4-stage tests with deterministic stage sizes and non-iid data

Given a fixed-sample-size test that controls the error probabilities under two specific, but arbitrary, distributions, a 3-stage and two 4stage tests are proposed and analyzed. For each of them, a novel, concrete, non-asymptotic, non-conservative design is specified, which guarantees the same error control as the given fixed-sample-size test. Moreover, first-order asymptotic approximation are established on their expected sample sizes under the two prescribed distributions as the error probabilities go to zero. As a corollary, it is shown that the proposed multistage tests can achieve, in this asymptotic sense, the optimal expected sample size under these two distributions in the class of all sequential tests with the same error control. Furthermore, they are shown to be much more robust than Wald’s SPRT when applied to one-sided testing problems and the error probabilities under control are small enough. These general results are applied to testing problems in the iid setup and beyond, such as testing the correlation coefficient of a first-order autoregression, or the transition matrix of a finite-state Markov chain, and are illustrated in various numerical studies. MSC2020 subject classifications: Primary 62L05, 62L10.
QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization

Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs. Deploying such networks in use-cases constrained by computational requirements is often infeasible due to the large memory required by the full-precision model. Previous compact face recognition approaches proposed to design special compact architectures and train them from scratch using real training data, which may not be available in a real-world scenario due to privacy concerns. We present in this work the QuantFace solution based on low-bit precision format model quantization. QuantFace reduces the required computational cost of the existing face recognition models without the need for designing a particular architecture or accessing real training data. QuantFace introduces privacy-friendly synthetic face data to the quantization process to mitigate potential privacy concerns and issues related to the accessibility to real training data. Through extensive evaluation experiments on seven benchmarks and four network architectures, we demonstrate that QuantFace can successfully reduce the model size up to 5x while maintaining, to a large degree, the verification performance of the full-precision model without accessing real training datasets. All training codes are publicly available 1.
Dynamical triplet unraveling : A quantum Monte Carlo algorithm for reversible dynamics

We introduce a quantum Monte Carlo method to simulate the reversible dynamics of correlated many-body systems. Our method is based on the Laplace transform of the time-evolution operator which, as opposed to most quantum Monte Carlo methods, makes it possible to access the dynamics at longer times. The Monte Carlo trajectories are realised through a piece-wise stochastic-deterministic reversible evolution where free dynamics is interspersed with two-process quantum jumps. The dynamical sign problem is bypassed via the so-called deadweight approximation , which stabilizes the many-body phases at longer times. We benchmark our method by simulating spin excitation propagation in the XXZ model and dynamical conﬁnement in the quantum Ising chain, and show how to extract dynamical information from the Laplace representation.
Beating the fault-tolerance bound and security loopholes for Byzantine agreement with a quantum solution

Byzantine agreement, the underlying core of blockchain, aims to make every node in a decentralized network reach consensus. Classical Byzantine agreements unavoidably face two major problems. One is $1/3$ fault-tolerance bound, which means that the system to tolerate $f$ malicious players requires at least $3f+1$ players. The other is the security loopholes from its classical cryptography methods. Here, we propose a Byzantine agreement framework with unconditional security to break this bound with nearly $1/2$ fault tolerance due to multiparty correlation provided by quantum digital signatures. \textcolor{black}{It is intriguing that quantum entanglement is not necessary to break the $1/3$ fault-tolerance bound, and we show that weaker correlation, such as asymmetric relationship of quantum digital signature, can also work. } Our work strictly obeys two Byzantine conditions and can be extended to any number of players without requirements for multiparticle entanglement. We experimentally demonstrate three-party and five-party consensus for a digital ledger. Our work indicates the quantum advantage in terms of consensus problems and suggests an important avenue for quantum blockchain and quantum consensus networks.
Extracting Fast and Slow: User-Action Embedding with Inter-temporal Information

With the recent development of technology, data on de- tailed human temporal behaviors has become available. Many methods have been proposed to mine those human dynamic behavior data and revealed valuable insights for research and businesses. However, most methods analyze only sequence of actions and do not study the inter-temporal information such as the time intervals between actions in a holistic manner. While actions and action time intervals are interdependent, it is challenging to integrate them because they have differ- ent natures: time and action. To overcome this challenge, we propose a uniﬁed method that analyzes user actions with in- tertemporal information (time interval). We simultaneously embed the user’s action sequence and its time intervals to obtain a low-dimensional representation of the action along with intertemporal information. The paper demonstrates that the proposed method enables us to characterize user actions in terms of temporal context, using three real-world data sets. This paper demonstrates that explicit modeling of action sequences and inter-temporal user behavior information enable successful interpretable analysis.
Extended field-of-view speckle-correlation imaging by estimating autocorrelation

Imaging through scattering media is a longstanding issue in a wide range of applications, including biomedicine, security, and astronomy. Speckle-correlation imaging is promising for non-invasively seeing through scattering media by assuming shift-invariance of the scattering process called the memory effect. However, the memory effect is known to be severely limited when the medium is thick. Under such a scattering condition, speckle-correlation imaging is not practical because the correlation of the speckle decays, reducing the field of view. To address this problem, we present a method for expanding the field of view of single-shot speckle-correlation imaging through scattering media with a limited memory effect. We derive the imaging model under this scattering condition and its inversion for reconstructing the object. Our method simultaneously estimates both the object and the decay of the speckle correlation based on the gradient descent method. We numerically and experimentally demonstrate the proposed method by reconstructing point sources behind scattering media with a limited memory effect. In the demonstrations, our speckle-correlation imaging method with a minimal lensless optical setup realized a larger field of view compared with the conventional one. This study will make techniques for imaging through scattering media more practical in various fields.
Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator

Tyler's M-estimator is a well known procedure for robust and heavy-tailed covariance estimation. Tyler himself suggested an iterative fixed-point algorithm for computing his estimator however, it requires super-linear (in the size of the data) runtime per iteration, which maybe prohibitive in large scale. In this work we propose, to the best of our knowledge, the first Frank-Wolfe-based algorithms for computing Tyler's estimator. One variant uses standard Frank-Wolfe steps, the second also considers \textit{away-steps} (AFW), and the third is a \textit{geodesic} version of AFW (GAFW). AFW provably requires, up to a log factor, only linear time per iteration, while GAFW runs in linear time (up to a log factor) in a large $n$ (number of data-points) regime. All three variants are shown to provably converge to the optimal solution with sublinear rate, under standard assumptions, despite the fact that the underlying optimization problem is not convex nor smooth. Under an additional fairly mild assumption, that holds with probability 1 when the (normalized) data-points are i.i. d. samples from a continuous distribution supported on the entire unit sphere, AFW and GAFW are proved to converge with linear rates. Importantly, all three variants are parameter-free and use adaptive step-sizes.
Bayesian Optimization under Stochastic Delayed Feedback

Bayesian optimization (BO) is a widely-used sequential method for zeroth-order optimization of complex and expensive-to-compute black-box functions. The existing BO methods assume that the function evaluation (feedback) is available to the learner immediately or after a fixed delay. Such assumptions may not be practical in many real-life problems like online recommendations, clinical trials, and hyperparameter tuning where feedback is available after a random delay. To benefit from the experimental parallelization in these problems, the learner needs to start new function evaluations without waiting for delayed feedback. In this paper, we consider the BO under stochastic delayed feedback problem. We propose algorithms with sub-linear regret guarantees that efficiently address the dilemma of selecting new function queries while waiting for randomly delayed feedback. Building on our results, we also make novel contributions to batch BO and contextual Gaussian process bandits. Experiments on synthetic and real-life datasets verify the performance of our algorithms.
Quaternion variational integration for inertial maneuvering in a biomimetic UAV

Biological flying, gliding, and falling creatures are capable of extraordinary forms of inertial maneuvering: free-space maneuvering based on fine control of their multibody dynamics, as typified by the self-righting reflexes of cats. However, designing inertial maneuvering capability into biomimetic robots, such as biomimetic unmanned aerial vehicles (UAVs) is challenging. Accurately simulating this maneuvering requires numerical integrators that can ensure both singularity-free integration, and momentum and energy conservation, in a strongly coupled system – properties unavailable in existing conventional integrators. In this work, we develop a pair of novel quaternion variational integrators (QVIs) showing these properties, and demonstrate their capability for simulating inertial maneuvering in a biomimetic UAV showing complex multibody-dynamics coupling. Being quaternion-valued, these QVIs are innately singularity-free; and being variational, they can show excellent energy and momentum conservation properties. We explore the effect of variational integration order (left-rectangle vs. midpoint) on the conservation properties of integrator, and conclude that, in complex coupled systems in which canonical momenta may be time-varying, the midpoint integrator is required. The resulting midpoint QVI is well-suited to the analysis of inertial maneuvering in a biomimetic UAV – a feature that we demonstrate in simulation – and of other complex dynamical systems.
Unsupervised Novelty Detection Using Deep Autoencoders with Density Based Clustering

Novelty detection is a classification problem to identify abnormal patterns; therefore, it is an important task for applications such as fraud detection, fault diagnosis and disease detection. However, when there is no label that indicates normal and abnormal data, it will need expensive domain and professional knowledge, so an unsupervised novelty detection approach will be used. On the other hand, nowadays, using novelty detection on high dimensional data is a big challenge and previous research suggests approaches based on principal component analysis (PCA) and an autoencoder in order to reduce dimensionality. In this paper, we propose deep autoencoders with density based clustering (DAE-DBC); this approach calculates compressed data and error threshold from deep autoencoder model, sending the results to a density based cluster. Points that are not involved in any groups are not considered a novelty; the grouping points will be defined as a novelty group depending on the ratio of the points exceeding the error threshold. We have conducted the experiment by substituting components to show that the components of the proposed method together are more effective. As a result of the experiment, the DAE-DBC approach is more efficient; its area under the curve (AUC) is shown to be 13.5 percent higher than state-of-the-art algorithms and other versions of the proposed method that we have demonstrated.
Magnetic forces in paramagnetic fluids

An overview of the effect of a magnetic field gradient on fluids with linear magnetic susceptibilities is given. It is shown that two commonly encountered expressions, the magnetic field gradient force and the concentration gradient force for paramagnetic species in solution are equivalent for incompressible fluids. The magnetic field gradient and concentration gradient forces are approximations of the Kelvin force and Korteweg–Helmholtz force densities, respectively. The criterion for the appearance of magnetically induced convection is derived. Experimental work in which magnetically induced convection plays a role is reviewed.
Winning the CVPR'2022 AQTC Challenge: A Two-stage Function-centric Approach

Affordance-centric Question-driven Task Completion for Egocentric Assistant (AQTC) is a novel task which helps AI assistant learn from instructional videos and scripts and guide the user step-by-step. In this paper, we deal with the AQTC via a two-stage Function-centric approach, which consists of Question2Function Module to ground the question with the related function and Function2Answer Module to predict the action based on the historical steps. We evaluated several possible solutions in each module and obtained signiﬁcant gains compared to the given base-lines. Our code is available at https://github.com/ starsholic/LOVEU-CVPR22-AQTC .
Semantic preservation for a type directed translation scheme of Featherweight Go

Featherweight Go (FG) is a minimal core calculus that includes essential Go features such as overloaded methods and interface types. The most straightforward semantic description of the dynamic behavior of FG programs is to resolve method calls based on run-time type information. A more eﬃcient approach is to apply a type-directed translation scheme where interface-values are replaced by dictionaries that contain concrete method deﬁnitions. Thus, method calls can be resolved by a simple lookup of the method deﬁnition in the dictionary. Establishing that the target program obtained via the type-directed translation scheme preserves the semantics of the original FG program is an important task. To establish this property we employ logical relations that are indexed by types to relate source and target programs. We provide rigorous proofs and give a detailed discussion of the many subtle corners that we have encountered including the need for a step index due to recursive interfaces and method deﬁnitions.
COREQQA - A COmpliance REQuirements Understanding using Question Answering Tool

We introduce COREQQA, a tool for assisting requirements engineers in acquiring a better understanding of compliance requirements by means of automated Question Answering. Extracting compliance-related requirements by manually navigating through a legal document is both time-consuming and error-prone. COREQQA enables requirements engineers to pose questions in natural language about a compliance-related topic given some legal document, e.g., asking about data breach. The tool then automatically navigates through the legal document and returns to the requirements engineer a list of text passages containing the possible answers to the input question. For better readability, the tool also highlights the likely answers in these passages. The engineer can then use this output for specifying compliance requirements. COREQQA is developed using advanced large-scale language models from BERT's family. COREQQA has been evaluated on four legal documents. The results of this evaluation are briefly presented in the paper. The tool is publicly available on Zenodo (DOI: 10.5281/zenodo.6653514).
M&M Mix: A Multimodal Multiview Transformer Ensemble

This report describes the approach behind our winning solution to the 2022 Epic-Kitchens Action Recognition Challenge. Our approach builds upon our recent work, Multiview Transformer for Video Recognition (MTV), and adapts it to multimodal inputs. Our final submission consists of an ensemble of Multimodal MTV (M&M) models varying backbone sizes and input modalities. Our approach achieved 52.8% Top-1 accuracy on the test set in action classes, which is 4.1% higher than last year's winning entry.
Uncertainty and Bias of Cosmology and Astrophysical Population Model from Statistical Dark Sirens

Gravitational-wave (GW) radiation from a coalescing compact binary is a standard siren, as the luminosity distance of each event can be directly measured from the amplitude of the signal. One possibility to constrain cosmology using the GW siren is to perform statistical inference on a population of binary black hole (BBH) events. In essence, this statistical method can be viewed as follows. We can modify the shape of the distribution of observed BBH events by changing the cosmological parameters until it eventually matches the distribution constructed from an astrophysical population model, thereby allowing us to determine the cosmological parameters. In this work, we derive the Cramér–Rao bound for both cosmological parameters and those governing the astrophysical population model from this statistical dark siren method by examining the Fisher information contained in the event distribution. Our study provides analytical insights and enables fast yet accurate estimations of the statistical accuracy of dark siren cosmology. Furthermore, we consider the bias in cosmology due to unmodeled substructures in the merger rate and mass distribution. We find that a 1% deviation in the astrophysical model can lead to a more than 1% error in the Hubble constant. This could limit the accuracy of dark siren cosmology when there are more than 104 BBH events detected.
The Impact of Visibility on the Right to Opt-out of Sale under CCPA

—The California Consumer Protection Act (CCPA) gives users the right to opt-out of sale of their personal information, but prior work has found that opt-out mechanisms provided under this law result in very low opt-out rates. Privacy signals offer a solution for users who are aware of their rights and are willing to proactively take steps to enable privacy-enhancing tools, but this work ﬁndsthat many users are not aware of their rights under CCPA and that current opt-out rates are very low. We therefore explore an alternative approach to enhancing privacy under CCPA: increasing the visibility of opt-out of sale mechanisms. For this purpose, we design and implement CCPA Opt-out Assistant (COA) , a browser extension that automatically detects when websites sell personal information and presents users with a visible, standardized banner that links to the opt-out of sale mechanism for the website. We conduct an online user study with 54 participants that ﬁnds that these banners signiﬁcantly increases the rate at which users opt-out of sale of their personal information. Participants also report less difﬁculty opting-out and more satisfaction with opt-out mechanisms compared to the native mechanisms currently provided by websites. Our results suggest that effective privacy regulation depends on imposing clear, enforceable visibility standards, and that CCPA’s requirements for opt-out of sale mechanisms fall short.