Miracle-dz/UniFAR-FTUs huggingface dataset:
A Jump Ornstein–Uhlenbeck Bridge Based on Energy-Optimal Control and Its Self-Exciting Extension

We study a version of the Ornstein–Uhlenbeck bridge driven by a spectrally-positive subordinator. Our formulation is based on a Linear-Quadratic control subject to a singular terminal condition. The Ornstein–Uhlenbeck bridge, we develop, is written as a limit of the obtained optimally controlled processes, and is shown to admit an explicit expression. Its extension with self-excitement is also considered. The terminal condition is confirmed to be satisfied by the obtained process both analytically and numerically. The methods are also applied to a streamflow regulation problem using a real-life dataset.
Approximate representability of finite abelian group actions on the Razak-Jacelon algebra

Let $A$ be a simple separable nuclear monotracial C$^*$-algebra, and let $\alpha$ be an outer action of a finite abelian group $\Gamma$ on $A$. In this paper, we show that $\alpha\otimes \mathrm{id}_{\mathcal{W}}$ on $A\otimes\mathcal{W}$ is approximately representable if and only if the characteristic invariant of $\tilde{\alpha}$ is trivial, where $\mathcal{W}$ is the Razak-Jacelon algebra and $\tilde{\alpha}$ is the induced action on the injective II$_1$ factor $\pi_{\tau_{A}}(A)^{''}$. As an application of this result, we classify such actions up to conjugacy and cocycle conjugacy. In particular, we show the following: Let $A$ and $B$ be simple separable nuclear monotracial C$^*$-algebras, and let $\alpha$ and $\beta$ be outer actions of a finite abelian group $\Gamma$ on $A$ and $B$, respectively. Assume that the characteristic invariants of $\tilde{\alpha}$ and $\tilde{\beta}$ are trivial. Then $\alpha\otimes \mathrm{id}_{\mathcal{W}}$ and $\beta\otimes \mathrm{id}_{\mathcal{W}}$ are conjugate (resp. cocycle conjugate) if and only if $\tilde{\alpha}$ on $\pi_{\tau_{A}}(A)^{''}$ and $\tilde{\beta}$ on $\pi_{\tau_{B}}(B)^{''}$ are conjugate (resp. cocycle conjugate). We also construct the model actions.
Zoom lens design using liquid lenses for achromatic and spherical aberration corrected target

This paper proposes a zoom lens system based on liquid lenses. The system design consists of a fixed lens and four two-chamber liquid lenses. The chambers are filled with different liquids. The design principle is analyzed. Optimization method is used to get the optimal solution. With the solution, the proposed zoom lens system can obtain achromatic and spherical aberration corrected images in a particular zooming range, and no moving element is needed in the proposed zoom lens system. By choosing proper liquids, it can be designed to work within particular working regions with improved optical performance. We give the detailed simulation examples. The results indicate that the proposed design can help to improve the optical performances of zoom lens systems.
Certified Everlasting Secure Collusion-Resistant Functional Encryption, and More

We study certified everlasting secure functional encryption (FE) and many other cryptographic primitives in this work. Certified everlasting security roughly means the following. A receiver possessing a quantum cryptographic object can issue a certificate showing that the receiver has deleted the cryptographic object and information included in the object was lost. If the certificate is valid, the security is guaranteed even if the receiver becomes computationally unbounded after the deletion. Many cryptographic primitives are known to be impossible (or unlikely) to have information-theoretical security even in the quantum world. Hence, certified everlasting security is a nice compromise (intrinsic to quantum). In this work, we define certified everlasting secure versions of FE, compute-and-compare obfuscation, predicate encryption (PE), secret-key encryption (SKE), public-key encryption (PKE), receiver non-committing encryption (RNCE), and garbled circuits. We also present the following constructions: - Adaptively certified everlasting secure collusion-resistant public-key FE for all polynomial-size circuits from indistinguishability obfuscation and one-way functions. - Adaptively certified everlasting secure bounded collusion-resistant public-key FE for NC1 circuits from standard PKE. - Certified everlasting secure compute-and-compare obfuscation from standard fully homomorphic encryption and standard compute-and-compare obfuscation - Adaptively (resp., selectively) certified everlasting secure PE from standard adaptively (resp., selectively) secure attribute-based encryption and certified everlasting secure compute-and-compare obfuscation. - Certified everlasting secure SKE and PKE from standard SKE and PKE, respectively. - Certified everlasting secure RNCE from standard PKE. - Certified everlasting secure garbled circuits from standard SKE.
A New Approximation Algorithm for Minimum-Weight $(1,m)$--Connected Dominating Set

Consider a graph with nonnegative node weight. A vertex subset is called a CDS (connected dominating set) if every other node has at least one neighbor in the subset and the subset induces a connected subgraph. Furthermore, if every other node has at least $m$ neighbors in the subset, then the node subset is called a $(1,m)$CDS. The minimum-weight $(1,m)$CDS problem aims at finding a $(1,m)$CDS with minimum total node weight. In this paper, we present a new polynomial-time approximation algorithm for this problem with approximation ratio $2H(\delta_{\max}+m-1)$, where $\delta_{\max}$ is the maximum degree of the given graph and $H(\cdot)$ is the Harmonic function, i.e., $H(k)=\sum_{i=1}^k \frac{1}{i}$.
VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge

This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge was to evaluate how well state-of-the-art speaker recognition systems can diarise and recognise speakers from speech obtained"in the wild". The challenge consisted of: (i) the provision of publicly available speaker recognition and diarisation data from YouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a public challenge and hybrid workshop held at INTERSPEECH 2022. We describe the four tracks of our challenge along with the baselines, methods, and results. We conclude with a discussion on the new domain-transfer focus of VoxSRC-22, and on the progression of the challenge from the previous three editions.
THE CENTRAL LIMIT THEOREM FOR NONHOMOGENEOUS U-STATISTICS OF FINITELY DEPENDENT RANDOM VARIABLES

In this paper the notion of homogeneous vector-valued U-statistic is introduced and a central limit theorem is proved for such statistics of a sequence of finitely dependent random variables, which generalizes analogous results for ordinary U-statistics. The convergence of finite-dimensional distributions of some random processes generated by U-statistics is also studied.Bibliography: 5 titles.
Axion star explosions: A new source for axion indirect detection

If dark matter is composed of axions, then axion stars form in the cores of dark matter halos. These stars are unstable above a critical mass, decaying to radio photons that heat the intergalactic medium, offering a new channel for axion indirect detection. We recently provided the first accurate calculation of the axion decay rate due to axion star mergers. In this work we show how existing data concerning the CMB optical depth leads to strong constraints on the axion photon coupling in the mass range $10^{-14}\,{\rm eV}\lesssim m_a\lesssim 10^{-8}\,{\rm eV}$. Axion star decays lead to efficient reionization of the intergalactic medium during the dark ages. By comparing this non-standard reionization with Planck legacy measurements of the Thompson optical width, we show that couplings in the range $10^{-14}\,{\rm GeV}^{-1} \lesssim g_{a\gamma\gamma} \lesssim 10^{-10}\,{\rm GeV}^{-1}$ are excluded for our benchmark model of axion star abundance. Future measurements of the 21cm emission of neutral hydrogen at high redshift could improve this limit by an order of magnitude or more, providing complementary indirect constraints on axion dark matter in parameter space also targeted by direct detection haloscopes.
Uncertainty-Aware Reward-based Deep Reinforcement Learning for Intent Analysis of Social Media Information

Due to various and serious adverse impacts of spreading fake news, it is often known that only people with malicious intent would propagate fake news. However, it is not necessarily true based on social science studies. Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches. To this end, we propose an intent classification framework that can best identify the correct intent of fake news. We will leverage deep reinforcement learning (DRL) that can optimize the structural representation of each tweet by removing noisy words from the input sequence when appending an actor to the long short-term memory (LSTM) intent classifier. Policy gradient DRL model (e.g., REINFORCE) can lead the actor to a higher delayed reward. We also devise a new uncertainty-aware immediate reward using a subjective opinion that can explicitly deal with multidimensional uncertainty for effective decision-making. Via 600K training episodes from a fake news tweets dataset with an annotated intent class, we evaluate the performance of uncertainty-aware reward in DRL. Evaluation results demonstrate that our proposed framework efficiently reduces the number of selected words to maintain a high 95\% multi-class accuracy.
Stepdown SLOPE for Controlled Feature Selection

Sorted L-One Penalized Estimation (SLOPE) has shown the nice theoretical property as well as empirical behavior recently on the false discovery rate (FDR) control of high-dimensional feature selection by adaptively imposing the non-increasing sequence of tuning parameters on the sorted L1 penalties. This paper goes beyond the previous concern limited to the FDR control by considering the stepdown-based SLOPE in order to control the probability of k or more false rejections (k-FWER) and the false discovery proportion (FDP). Two new SLOPEs, called k-SLOPE and F-SLOPE, are proposed to realize k-FWER and FDP control respectively, where the stepdown procedure is injected into the SLOPE scheme. For the proposed stepdown SLOPEs, we establish their theoretical guarantees on controlling k-FWER and FDP under the orthogonal design setting, and also provide an intuitive guideline for the choice of regularization parameter sequence in much general setting. Empirical evaluations on simulated data validate the effectiveness of our approaches on controlled feature selection and support our theoretical findings.
A residual stress characterization method of a small diameter wire by matter removal

Standard spring design is based on the assumption that there is no residual stress in the wire. This assumption can be quite strong for small wires that undergo a significant wire drawing during the manufacturing process. The main objective of this work is to design and test a method for characterizing residual stresses in small diameter drawn wires. The basic principle consists in removing a part of the material and then measuring the associated displacements. This material removal was simulated by finite elements and experimented in real situation. The first results are encouraging because they seem to show that the residual stresses in the wire studied are significant, and justify our investigations.
On fields inspired with the polar HSV -- RGB theory of Colour

A three-polar, cf. T. Gregor, J. Halu\v{s}ka, Lexicographical ordering and field operations in the complex plane. Stud. Mat. 41(2014), 123--133. , $HSV-RGB$ Colour space $\triangle$ was introduced and studied. It was equipped with operations of addition, subtraction, multiplication, and (partially) division. Achromatic Grey Hues form an ideal $\mathfrak{S}$. Factorizing $\triangle$ by the ideal $\mathfrak{S}$, we obtain a field $\triangle | \mathfrak{S}$. An element (i.e an individual Colour) in $\triangle | \mathfrak{S}$ is a triplet of three triangular coefficients. The set of all triangular coefficients is a subset of a semi-field of parabolic-complex functions. For the parabolic-complex number set, cf.~A. A. Harkin--J. B. Harkin, Geometry of general complex numbers. Mathematics magazine, 77(2004), 118--129.
Understanding the effect of varying amounts of replay per step

Model-based reinforcement learning uses models to plan, where the predictions and policies of an agent can be improved by using more computation without additional data from the environment, thereby improving sample efficiency. However, learning accurate estimates of the model is hard. Subsequently, the natural question is whether we can get similar benefits as planning with model-free methods. Experience replay is an essential component of many model-free algorithms enabling sample-efficient learning and stability by providing a mechanism to store past experiences for further reuse in the gradient computational process. Prior works have established connections between models and experience replay by planning with the latter. This involves increasing the number of times a mini-batch is sampled and used for updates at each step (amount of replay per step). We attempt to exploit this connection by doing a systematic study on the effect of varying amounts of replay per step in a well-known model-free algorithm: Deep Q-Network (DQN) in the Mountain Car environment. We empirically show that increasing replay improves DQN's sample efficiency, reduces the variation in its performance, and makes it more robust to change in hyperparameters. Altogether, this takes a step toward a better algorithm for deployment.
Statistics of lensing by clusters of galaxies – I. Giant arcs

The statistical properties of gravitational lensing by rich distant clusters of galaxies are investigated. The sources of giant luminous arcs are assumed to be moderately luminous spirals similar to those found in deep blue redshift surveys. Arc properties (numbers, axis ratios, etc.) are found to depend strongly on the adopted cluster density profiles, and hence provide sharp constraints on the models for matter distribution in clusters. Giant luminous arcs should be present in nearly half of the extremely rich clusters (L x ≥ 9 × 10 44 erg s −1 or σ v ≥ 1300 km s −1 ), if described by the r 1/4 law, with a lower incidence by a factor of ∼ 4 for a modified Hubble profile
Electron transport and electron density inside one-dimensional disordered conductors: an analysis of the electronic-levels contribution

We consider the problem of electron transport along a one-dimensional disordered multiple-scattering conductor, and study the electron density for all the electronic levels. A model is proposed for the reduced density matrix of the system placed between two reservoirs at different chemical potentials, and the statistical-mechanical expectation value of the electron density is evaluated. An ensemble average is computed over disordered configurations. We compare its predictions with computer simulations. We find that the contribution of low-lying levels is very different from that of the high-lying ones studied in the past. Going down in energy, the wave function penetrates ever less inside the sample. For high-lying levels, this is interpreted in terms of localization from disorder. For low-lying levels, this interpretation gradually gives way to an understanding in terms of the increasing reflection produced by each scatterer, which is seen by the electron as a higher and higher -- and hence impenetrable -- potential barrier. Indeed, the local-density-of-states, LDOS, is gradually depleted in the interior of the system, since the wave function is ever smaller inside. The problem studied here is also of interest in electromagnetic, thermal, and acoustic transport in disordered systems.
pykanto: A python library to accelerate research on wild bird song

Studying the vocalisations of wild animals can be a challenge due to the limitations of traditional computational methods, which often are time‐consuming and lack reproducibility. Here, I present pykanto, a new software package that provides a set of tools to build, manage, and explore large sound databases. It can automatically find discrete units in animal vocalisations, perform semi‐supervised labelling of individual repertoires with a new interactive web app and feed data to deep learning models. pykanto can be used to streamline research on, for example, individual vocal signatures and acoustic similarity between individuals and populations. To demonstrate its capabilities, I put the library to the test on the vocalisations of male great tits in Wytham Woods, near Oxford, UK. The results show that the identities of individual birds can be accurately determined from their songs and that the use of pykanto improves the efficiency and reproducibility of the process.
UvA-DARE (Digital Academic Repository) Scientific imperatives, clinical implications, and theoretical underpinnings for the investigation of the relationship between genetic variables and patient-reported quality-of-life outcomes Sprangers,

Objectives There is emerging evidence for a genetic basis of patient-reported quality-of-life (QOL) outcomes that can ultimately be incorporated into clinical research and practice. Objectives are (1) to provide arguments for the timeliness of investigating the genetic basis of QOL given the scientific advances in genetics and patient-reported QOL research; (2) to describe the clinical implications of such investigations; (3) to present a theoretical foundation for investigating the genetic underpinnings of QOL; and (4) to describe a series of papers resulting from the GENEQOL Consortium that was established to move this work forward. Methods Discussion of scientific advances based on relevant literature. Results In genetics, technological advances allow for increases in speed and efficiency and decreases in costs in exploring the genetic underpinnings of disease processes, drug metabolism, treatment response, and survival. In patient-based research, advances yield empirically based and stringent approaches to measurement that are scientifically robust. Insights into the genetic basis of QOL will ultimately allow early identification of patients susceptible to QOL deficits and to target care. The Wilson and Cleary model for patient-reported outcomes was refined by incorporating the genetic underpinnings of QOL. Conclusions This series of papers provides a path for QOL and genetics researchers to work together to move this field forward and to unravel the intricate interplay of the genetic underpinnings of patient-reported QOL outcomes. The ultimate result will be a greater understanding of the process relating disease, patient, and doctor that will have the potential to lead to improved survival, QOL, and health services delivery.
Noise-Augmented $\ell_0$ Regularization of Tensor Regression with Tucker Decomposition

Tensor data are multi-dimension arrays. Low-rank decomposition-based regression methods with tensor predictors exploit the structural information in tensor predictors while significantly reducing the number of parameters in tensor regression. We propose a method named NA$_0$CT$^2$ (Noise Augmentation for $\ell_0$ regularization on Core Tensor in Tucker decomposition) to regularize the parameters in tensor regression (TR), coupled with Tucker decomposition. We establish theoretically that NA$_0$CT$^2$ achieves exact $\ell_0$ regularization in linear TR and generalized linear TR on the core tensor from the Tucker decomposition. To our knowledge, NA$_0$CT$^2$ is the first Tucker decomposition-based regularization method in TR to achieve $\ell_0$ in core tensor. NA$_0$CT$^2$ is implemented through an iterative procedure and involves two simple steps in each iteration -- generating noisy data based on the core tensor from the Tucker decomposition of the updated parameter estimate and running a regular GLM on noise-augmented data on vectorized predictors. We demonstrate the implementation of NA$_0$CT$^2$ and its $\ell_0$ regularization effect in both simulation studies and real data applications. The results suggest that NA$_0$CT$^2$ improves predictions compared to other decomposition-based TR approaches, with or without regularization and it also helps to identify important predictors though not designed for that purpose.
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker

Finding classifiers robust to adversarial examples is critical for their safe deployment. Determining the robustness of the best possible classifier under a given threat model for a given data distribution and comparing it to that achieved by state-of-the-art training methods is thus an important diagnostic tool. In this paper, we find achievable information-theoretic lower bounds on loss in the presence of a test-time attacker for multi-class classifiers on any discrete dataset. We provide a general framework for finding the optimal 0-1 loss that revolves around the construction of a conflict hypergraph from the data and adversarial constraints. We further define other variants of the attacker-classifier game that determine the range of the optimal loss more efficiently than the full-fledged hypergraph construction. Our evaluation shows, for the first time, an analysis of the gap to optimal robustness for classifiers in the multi-class setting on benchmark datasets.