Miracle-dz/UniFAR-FTUs huggingface dataset:
Theft and Misuse Protection for Anonymous Credentials

Today, many service providing organisations require their users to authenticate before enabling them to use their services. A widely used system for this purpose is the so called usernamepassword system. The most important advantages of this system are the ease of use, the experience in implementing and the acceptance of users. However, this system has some severe disadvantages. The first lies in the fact that many users have a vast amount of accounts. As an example there are such accounts for email, online auctions, gaming and online banking. Trying to reduce the entropy they need to store in their brain, they tend to choose the same username-password combination for authentication with different organisations. This is particularly relevant if services with different security levels, such as a bank account and an email account, are accessed with the same authentication information. Federated identity management allows a user to acquire a credential with certified attributes. He only needs to store the credential in a safe place and can authenticate using the credential. The authentication process transparently uses public key cryptography and therefore offers much better security compared to the username-password approach. Another advantage of federated identity management is the possibility to performing authentication across borders of different domains (e.g., companies). An anonymous credential system, such as idemix, can be extended such that it implements the concept of identity federation. It uses not only credentials with certified attributes but guarantees that the identity of each user will be protected. The privacy protection reaches even further, as the actions of a user with a credential are not linkable. As an example, a user can authenticate several times with one company using the same credential and still the organisation will not gain any information apart from the fact that an eligible user has accessed its system. Such a far-reaching privacy protection has disadvantages, too. Credentials are issued as digital information which enables users to share them arbitrarily. Moreover, the credential can be duplicated by malicious software without the owner even noticing. In other words, anonymous credential systems do neither prevent users from sharing their credentials nor can they detect if several copies of one credential are used extensively by different users. This may hinder the large-scale adoption of anonymous credential systems. In this thesis, we will first present an overview of several techniques to cope with credential misuse, such as sharing or theft, in anonymous credential systems. A comparison of the techniques will be the basis for the choice of an effective misuse prevention mechanism. We will then describe how two concrete techniques — K-show credentials and hardware-bound credentials — have been implemented into the idemix system. The result is a system offering strong privacy combined with effective sharing and theft prevention.
The Accretion Process in the DQ Tau Binary System

Mass accretion from the circumstellar disk onto the protostar is a fundamental process during star formation. Measuring the mass accretion rate is particularly challenging for stars belonging to binary systems, because it is often difficult to discriminate which component is accreting. DQ Tau is an almost equal-mass spectroscopic binary system where the components orbit each other every 15.8 days. The system is known to display pulsed accretion, i.e., the periodic modulation of the accretion by the components on eccentric orbit. We present multi-epoch ESO/Very Large Telescope X-Shooter observations of DQ Tau, with the aim of determining which component of this system is the main accreting source. We use the absorption lines in the spectra to determine the radial velocity of the two components, and measure the continuum veiling as a function of wavelength and time. We fit the observed spectra with nonaccreting templates to correct for the photospheric and chromospheric contribution. In the corrected spectra, we study in detail the profiles of the emission lines and calculate mass accretion rates for the system as a function of orbital phase. In accordance with previous findings, we detect elevated accretion close to periastron. We measure the accretion rate as varying between 10−8.5 and 10−7.3 M ⊙ yr−1. The emission line profiles suggest that both stars are actively accreting, and the dominant accretor is not always the same component, varying in a few orbits.
Purely local growth of a quasicrystal

Self-assembly is the process in which the components of a system, whether molecules, polymers, or macroscopic particles, are organized into ordered structures as a result of local interactions between the components themselves, without exterior guidance. In this paper, we speak about the self-assembly of aperiodic tilings. Aperiodic tilings serve as a mathematical model for quasicrystals - crystals that do not have any translational symmetry. Because of the specific atomic arrangement of these crystals, the question of how they grow remains open. In this paper, we state the theorem regarding purely local and deterministic growth of Golden-Octagonal tilings. Showing, contrary to the popular belief, that local growth of aperiodic tilings is possible.
Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems

Emerging edge applications require both a fast response latency and complex processing. This is infeasible with-out expensive hardware that can process complex operations-such as object detection-within a short time. Many approach this problem by addressing the complexity of the models-via model compression, pruning and quantization-or compressing the input. In this paper, we propose a different perspective when addressing the performance challenges. Croesus is a multi-stage approach to edge-cloud systems that provides the ability to find the balance between accuracy and performance. Croesus consists of two stages (that can be generalized to multiple stages): an initial and a final stage. The initial stage performs the compu-tation in real-time using approximate/best-effort computation at the edge. The final stage performs the full computation at the cloud, and uses the results to correct any errors made at the initial stage. In this paper, we demonstrate the implications of such an approach on a video analytics use-case and show how multi-stage processing yields a better balance between accuracy and performance. Moreover, we study the safety of multi-stage transactions via two proposals: multi-stage serializability (MS-SR) and multi-stage invariant confluence with Apologies (MS-IA).
VDPC: Variational Density Peak Clustering Algorithm

Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.
Learning a microlocal prior for limited-angle tomography

Limited-angle tomography is a highly ill-posed linear inverse problem. It arises in many applications, such as digital breast tomosynthesis. Reconstructions from limited-angle data typically suffer from severe stretching of features along the central direction of projections, leading to poor separation between slices perpendicular to the central direction. In this paper, a new method is introduced, based on machine learning and geometry, producing an estimate for interfaces between regions of different X-ray attenuation. The estimate can be presented on top of the reconstruction, indicating more reliably the separation between features. The method uses directional edge detection, implemented using complex wavelets and enhanced with morphological operations. By using convolutional neural networks, the visible part of the singular support is first extracted and then extended to the full domain, filling in the parts of the singular support that would otherwise be hidden due to the lack of measurement directions.
Limiting Spectral Distribution of High-dimensional Hayashi-Yoshida Estimator of Integrated Covariance Matrix

In this paper, the estimation of the Integrated Covariance matrix from high-frequency data, for high dimensional stock price process, is considered. The Hayashi-Yoshida covolatility estimator is an improvement over Realized covolatility for asynchronous data and works well in low dimensions. However it becomes inconsistent and unreliable in the high dimensional situation. We study the bulk spectrum of this matrix and establish its connection to the spectrum of the true covariance matrix in the limiting case where the dimension goes to infinity. The results are illustrated with simulation studies in finite, but high, dimensional cases. An application to real data with tick-by-tick data on 50 stocks is presented.
Bird Species Classification And Acoustic Features Selection Based on Distributed Neural Network with Two Stage Windowing of Short-Term Features

Identification of bird species from audio records is one of the challenging tasks due to the existence of multiple species in the same recording, noise in the background, and long-term recording. Besides, choosing a proper acoustic feature from audio recording for bird species classification is another problem. In this paper, a hybrid method is represented comprising both traditional signal processing and a deep learning-based approach to classify bird species from audio recordings of diverse sources and types. Besides, a detailed study with 34 different features helps to select the proper feature set for classification and analysis in real-time applications. Moreover, the proposed deep neural network uses both acoustic and temporal feature learning. The proposed method starts with detecting voice activity from the raw signal, followed by extracting short-term features from the processed recording using 50 ms (with 25ms overlapping) time windows. Later, the short-term features are reshaped using second stage (non-overlapping) windowing to be trained through a distributed 2D Convolutional Neural Network (CNN) that forwards the output features to a Long and Short Term Memory (LSTM) Network. Then a final dense layer classifies the bird species. For the 10 class classifier, the highest accuracy achieved was 90.45\% for a feature set consisting of 13 Mel Frequency Cepstral Coefficients (MFCCs) and 12 Chroma Vectors. The corresponding specificity and AUC scores are 98.94\% and 94.09\%, respectively.
Applications of Gaussian Mutation for Self Adaptation in Evolutionary Genetic Algorithms

In recent years, optimization problems have become increasingly more prevalent due to the need for more powerful computational methods. With the more recent advent of technology such as artificial intelligence, new metaheuristics are needed that enhance the capabilities of classical algorithms. More recently, researchers have been looking at Charles Darwin’s theory of natural selection and evolution as a means of enhancing current approaches using machine learning. In 1960, the first genetic algorithm was developed by John H. Holland and his students (Holland, 1975). We explore the mathematical intuition and implications of the genetic algorithm in developing systems capable of evolving using Gaussian mutation.
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents

Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable latent space, and are slow at generation. On the other hand, standard Variational Autoencoders (VAEs) typically have access to a low-dimensional latent space but exhibit poor sample quality. We present DiffuseVAE, a novel generative framework that integrates VAE within a diffusion model framework, and leverage this to design novel conditional parameterizations for diffusion models. We show that the resulting model equips diffusion models with a low-dimensional VAE inferred latent code which can be used for downstream tasks like controllable synthesis. The proposed method also improves upon the speed vs quality tradeoff exhibited in standard unconditional DDPM/DDIM models (for instance, FID of 16.47 vs 34.36 using a standard DDIM on the CelebA-HQ-128 benchmark using T=10 reverse process steps) without having explicitly trained for such an objective. Furthermore, the proposed model exhibits synthesis quality comparable to state-of-the-art models on standard image synthesis benchmarks like CIFAR-10 and CelebA-64 while outperforming most existing VAE-based methods. Lastly, we show that the proposed method exhibits inherent generalization to different types of noise in the conditioning signal. For reproducibility, our source code is publicly available at https://github.com/kpandey008/DiffuseVAE.
Gas distribution from clusters to filaments in IllustrisTNG

Matter distribution in the environment of galaxy clusters, from their cores to their connected cosmic ﬁlaments, must in principle be related to the underlying cluster physics and its evolutionary state. We aim to investigate how radial and azimuthal distribution of gas is a ﬀ ected by cluster environments and how it can be related to cluster-mass assembly history. We ﬁrst analysed the radial physical properties of gas (velocity, temperature, and density) around 415 galaxy cluster environments from IllustrisTNG simulations at z = 0 (TNG300-1). Whereas hot plasma is virialised inside clusters ( < R 200 ), the dynamics of a warm, hot, intergalactic medium (WHIM) can be separated in two regimes: accumulating and slowly infalling gas at cluster peripheries ( ∼ R 200 ) and fast infalling motions outside clusters ( > 1 . 5 R 200 ). The azimuthal distribution of dark matter (DM), hot, and warm gas phases is secondly statistically probed by decomposing their 2D distribution in harmonic space. Inside clusters, the azimuthal symmetries of DM and hot gas trace cluster structural properties well. These include their centre o ﬀ sets, substructure fractions, and elliptical shapes. Beyond cluster-virialised regions, we ﬁnd that WHIM gas follows the azimuthal distribution of DM, thus tracing cosmic ﬁlament patterns. Azimuthal symmetries of hot and warm gas distribution are ﬁnally shown to be imprints of cluster mass assembly history, strongly correlated with the formation time, mass accretion rate, and dynamical state of clusters. The azimuthal mode decomposition of 2D gas distribution is a promising probe to assess the 3D physical and cluster properties up to their connected cosmic ﬁlaments.
Succinct Differentiation of Disparate Boosting Ensemble Learning Methods for Prognostication of Polycystic Ovary Syndrome Diagnosis

Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of Polycystic Ovary Syndrome also known as PCOS is an emerging problem in women aged from 15 to 49. Diagnosing this disorder by using various Boosting Ensemble Methods is something we have presented in this paper. A detailed and compendious differentiation between Adaptive Boost, Gradient Boosting Machine, XGBoost and CatBoost with their respective performance metrics highlighting the hidden anomalies in the data and its effects on the result is something we have presented in this paper. Metrics like Confusion Matrix, Precision, Recall, F1 Score, FPR, RoC Curve and AUC have been used in this paper.
A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels

In open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of data. Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning. This paper formalizes various open-world learning problems including open-world learning without labels. These open-world problems can be addressed with modifications to known elements, we present a new framework that enables agents to combine various modules for novelty-detection, novelty-characterization, incremental learning, and instance management to learn new classes from a stream of unlabeled data in an unsupervised manner, survey how to adapt a few state-of-the-art techniques to fit the framework and use them to define seven baselines for performance on the open-world learning without labels problem. We then discuss open-world learning quality and analyze how that can improve instance management. We also discuss some of the general ambiguity issues that occur in open-world learning without labels.
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning

Data poisoning for reinforcement learning has historically focused on general performance degradation, and targeted attacks have been successful via perturbations that involve control of the victim's policy and rewards. We introduce an insidious poisoning attack for reinforcement learning which causes agent misbehavior only at specific target states - all while minimally modifying a small fraction of training observations without assuming any control over policy or reward. We accomplish this by adapting a recent technique, gradient alignment, to reinforcement learning. We test our method and demonstrate success in two Atari games of varying difficulty.
Time-dependent Duhamel renormalization method with multiple conservation and dissipation laws

The time dependent spectral renormalization (TDSR) method was introduced by Cole and Musslimani as a novel way to numerically solve initial boundary value problems. An important and novel aspect of the TDSR scheme is its ability to incorporate physics in the form of conservation laws or dissipation rate equations. However, the method was limited to include a single conserved or dissipative quantity. The present work significantly extends the computational features of the method with the (i) incorporation of multiple conservation laws and/or dissipation rate equations, (ii) ability to enforce versatile boundary conditions, and (iii) higher order time integration strategy. The TDSR method is applied on several prototypical evolution equations of physical significance. Examples include the Korteweg–de Vries, multi-dimensional nonlinear Schrödinger and the Allen–Cahn equations.
$T \overline{T}$-deformed modular forms

: Certain objects of conformal ﬁeld theory, for example partition functions on the rectangle and the torus, and one-point functions on the torus, are either invariant or transform simply under the modular group, properties which should be preserved under the T T deformation. The formulation and proof of this statement in fact extents to more general functions such as T T deformed modular and Jacobi forms. We show that the deformation acts simply on their Mellin transform, multiplying it by a universal entire function. Finally we show that Maass forms on the torus are eigenfunctions of the T T deformation.
Evaluation and comparison of SEA torque controllers in a unified framework

Series elastic actuators (SEA) with their inherent compliance offer a safe torque source for robots that are interacting with various environments, including humans. These applications have high requirements for the SEA torque controllers, both in the torque response as well as interaction behavior with its environment. To differentiate state of the art torque controllers, this work introduces a unifying theoretical and experimental framework that compares controllers based on their torque transfer behavior, their apparent impedance behavior, and especially the passivity of the apparent impedance (i.e., their interaction stability) as well as their sensitivity to sensor noise. We compare classical SEA control approaches such as cascaded PID controllers and full state feedback controllers with advanced controllers using disturbance observers, acceleration feedback and adaptation rules. Simulations and experiments demonstrate the trade-off between stable interactions, high bandwidths and low noise levels. Based on these trade-offs, an application-specific controller can be designed and tuned, based on desired interaction with the respective environment.
Subspace modeling for fast and high-sensitivity X-ray chemical imaging

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TXM-XANES imaging technique, by combining full field transmission X-ray microscopy (TXM) and X-ray absorption near edge structure (XANES), has been an emerging tool which operates by acquiring a series of microscopy images with multi-energy X-rays and fitting to obtain the chemical map. Its capability, however, is limited by the poor signal-to-noise ratios due to the system errors and low exposure illuminations for fast acquisition. In this work, by exploiting the intrinsic properties and subspace modeling of the TXM-XANES imaging data, we introduce a simple and robust denoising approach to improve the image quality, which enables fast and high-sensitivity chemical imaging. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method.
FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity

Deploying various deep learning (DL) models efficiently has boosted the research on DL compilers. The difficulty of generating optimized tensor codes drives DL compiler to ask for the auto-tuning approaches, and the increasing demands require increasing auto-tuning efficiency and quality. Currently, the DL compilers partition the input DL models into several subgraphs and leverage the auto-tuning to find the optimal tensor codes of these subgraphs. However, existing auto-tuning approaches usually regard subgraphs as individual ones and overlook the similarities across them, and thus fail to exploit better tensor codes under limited time budgets. We propose FamilySeer, an auto-tuning framework for DL compilers that can generate better tensor codes even with limited time budgets. FamilySeer exploits the similarities and differences among subgraphs can organize them into subgraph families, where the tuning of one subgraph can also improve other subgraphs within the same family. The cost model of each family gets more purified training samples generated by the family and becomes more accurate so that the costly measurements on real hardware can be replaced with the lightweight estimation through cost model. Our experiments show that FamilySeer can generate model codes with the same code performance more efficiently than state-of-the-art auto-tuning frameworks.