Miracle-dz/UniFAR-FTUs huggingface dataset:
Experiments on Turkish ASR with Self-Supervised Speech Representation Learning

While the Turkish language is listed among low-resource languages, literature on Turkish automatic speech recognition (ASR) is relatively old. In this report, we present our findings on Turkish ASR with speech representation learning using HUBERT. We investigate pre-training HUBERT for Turkish with large-scale data curated from online resources. We pre-train our model using 6,500 hours of speech data from YouTube. The results show that the models are not ready for commercial use since they are not robust against disturbances that typically occur in real-world settings such as variations in accents, slang, background noise and interference. We analyze typical errors and the limitations of the models for use in commercial settings.
Playing Safe, Ten Years Later

We consider two-player games over graphs and give tight bounds on the memory size of strategies ensuring safety objectives. More specifically, we show that the minimal number of memory states of a strategy ensuring a safety objective is given by the size of the maximal antichain of left quotients with respect to language inclusion. This result holds for all safety objectives without any regularity assumptions. We give several applications of this general principle. In particular, we characterize the exact memory requirements for the opponent in generalized reachability games, and we prove the existence of positional strategies in games with counters.
Do DALL-E and Flamingo Understand Each Other?

The field of multimodal research focusing on the comprehension and creation of both images and text has witnessed significant strides. This progress is exemplified by the emergence of sophisticated models dedicated to image captioning at scale, such as the notable Flamingo model and text-to-image generative models, with DALL-E serving as a prominent example. An interesting question worth exploring in this domain is whether Flamingo and DALL-E understand each other. To study this question, we propose a reconstruction task where Flamingo generates a description for a given image and DALL-E uses this description as input to synthesize a new image. We argue that these models understand each other if the generated image is similar to the given image. Specifically, we study the relationship between the quality of the image reconstruction and that of the text generation. We find that an optimal description of an image is one that gives rise to a generated image similar to the original one. The finding motivates us to propose a unified framework to finetune the text-to-image and image-to-text models. Concretely, the reconstruction part forms a regularization loss to guide the tuning of the models. Extensive experiments on multiple datasets with different image captioning and image generation models validate our findings and demonstrate the effectiveness of our proposed unified framework. As DALL-E and Flamingo are not publicly available, we use Stable Diffusion and BLIP in the remaining work. Project website: https://dalleflamingo.github. io.
Reinforcement learning optimization of the charging of a Dicke quantum battery

Quantum batteries are energy-storing devices, governed by quantum mechanics, that promise high charging performance thanks to collective effects. Due to its experimental feasibility, the Dicke battery - which comprises $N$ two-level systems coupled to a common photon mode - is one of the most promising designs for quantum batteries. Here, we use reinforcement learning to optimize the charging process of a Dicke battery either by modulating the coupling strength, or the system-cavity detuning. We find that the extractable energy (ergotropy) and quantum mechanical energy fluctuations (charging precision) can be greatly improved with respect to standard charging strategies. Notably, the collective speedup of the charging time can be preserved even when nearly fully charging the battery.
Quantum Computing for Molecular Biology **

Molecular biology and biochemistry interpret microscopic processes in the living world in terms of molecular structures and their interactions, which are quantum mechanical by their very nature. Whereas the theoretical foundations of these interactions are well established, the computational solution of the relevant quantum mechanical equations is very hard. However, much of molecular function in biology can be understood in terms of classical mechanics, where the interactions of electrons and nuclei have been mapped onto effective classical surrogate potentials that model the interaction of atoms or even larger entities. The simple mathematical structure of these potentials offers huge computational advantages; however, this comes at the cost that all quantum correlations and the rigorous many‐particle nature of the interactions are omitted. In this work, we discuss how quantum computation may advance the practical usefulness of the quantum foundations of molecular biology by offering computational advantages for simulations of biomolecules. We not only discuss typical quantum mechanical problems of the electronic structure of biomolecules in this context, but also consider the dominating classical problems (such as protein folding and drug design) as well as data‐driven approaches of bioinformatics and the degree to which they might become amenable to quantum simulation and quantum computation.
Design interpretable experience of dynamical feed forward machine learning model for forecasting NASDAQ

National Association of Securities Dealers Automated Quotations(NASDAQ) is an American stock exchange based. It is one of the most valuable stock economic indices in the world and is located in New York City \cite{pagano2008quality}. The volatility of the stock market and the influence of economic indicators such as crude oil, gold, and the dollar in the stock market, and NASDAQ shares are also affected and have a volatile and chaotic nature \cite{firouzjaee2022lstm}. In this article, we have examined the effect of oil, dollar, gold, and the volatility of the stock market in the economic market, and then we have also examined the effect of these indicators on NASDAQ stocks. Then we started to analyze the impact of the feedback on the past prices of NASDAQ stocks and its impact on the current price. Using PCA and Linear Regression algorithm, we have designed an optimal dynamic learning experience for modeling these stocks. The results obtained from the quantitative analysis are consistent with the results of the qualitative analysis of economic studies, and the modeling done with the optimal dynamic experience of machine learning justifies the current price of NASDAQ shares.
Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning

This work presents a machine learning approach to optimize the energy efficiency (EE) in a multi-cell wireless network. This optimization problem is non-convex and its global optimum is difficult to find. In the literature, either simple but suboptimal approaches or optimal methods with high complexity are proposed. In contrast, we propose an unsupervised machine learning framework to approach the global optimum. While the neural network (NN) training takes moderate time, application with the trained model requires very low computational complexity. In particular, we introduce a novel objective function based on stochastic actions to solve the non-convex optimization problem. Besides, we design a dedicated NN architecture SINRnet for the power allocation problems in the interference channel that is permutation-equivariant. We encode our domain knowledge into the NN design and shed light into the black box of machine learning. Training and testing results show that the proposed method without supervision and with reasonable computational effort achieves an EE close to the global optimum found by the branch-and-bound algorithm and outperform the successive convex approximation (SCA) algorithm. Hence, the proposed approach balances between computational complexity and performance.
Automating Vehicle SOA Threat Analysis Using a Model-Based Methodology

: This article proposes automated methods for threat analysis using a model-based engineering methodology that provides precise guarantees with respect to safety goals. This is accomplished by proposing an intruder model for automotive SOA which together with the system architecture and the loss scenarios identiﬁed by safety analysis are used as input for computing assets, impact rating, damage/threat scenarios, and attack paths. To validate the proposed methodology, we developed a faithful model of the autonomous driving functions of the Apollo framework, a widely used open source autonomous driving stack. The proposed machinery automatically enumerates several attack paths on Apollo, including attack paths not reported in the literature.
Unfitted Trefftz discontinuous Galerkin methods for elliptic boundary value problems

We propose a new geometrically unfitted finite element method based on discontinuous Trefftz ansatz spaces. Trefftz methods allow for a reduction in the number of degrees of freedom in discontinuous Galerkin methods, thereby, the costs for solving arising linear systems significantly. This work shows that they are also an excellent way to reduce the number of degrees of freedom in an unfitted setting. We present a unified analysis of a class of geometrically unfitted discontinuous Galerkin methods with different stabilisation mechanisms to deal with small cuts between the geometry and the mesh. We cover stability and derive a-priori error bounds, including errors arising from geometry approximation for the class of discretisations for a model Poisson problem in a unified manner. The analysis covers Trefftz and full polynomial ansatz spaces, alike. Numerical examples validate the theoretical findings and demonstrate the potential of the approach.
Finetuning for Sarcasm Detection with a Pruned Dataset

Sarcasm is a form of irony that involves saying or writing something that is opposite or opposite to what one really means, often in a humorous or mocking way. It is often used to mock or mock someone or something, or to be humorous or amusing. Sarcasm is usually conveyed through tone of voice, facial expressions, or other forms of nonverbal communication, but it can also be indicated by the use of certain words or phrases that are typically associated with irony or humor. Sarcasm detection is difﬁcult because it relies on context and non-verbal cues. It can also be culturally speciﬁc, subjective and ambiguous. In this work, we ﬁne-tune the RoBERTa based sarcasm detection model presented in Abaskohi et al. [2022] to get to within 0.02 F1 of the state-of-the-art (Hercog et al. [2022]) on the iSarcasm dataset (Oprea and Magdy [2019]). This performance is achieved by augmenting iSarcasm with a pruned version of the Self Annotated Reddit Corpus (SARC) (Khodak et al. [2017]). Our pruned version is 100 times smaller than the subset of SARC used to train the state-of-the-art model. Our code is available at: https://github.com/priyank96/dataset-pruning-sarcasm-detection
Depth analysis of variational quantum algorithms for the heat equation

Variational quantum algorithms are a promising tool for solving partial differential equations. The standard approach for its numerical solution are finite difference schemes, which can be reduced to the linear algebra problem. We consider three approaches to solve the heat equation on a quantum computer. Using the direct variational method we minimize the expectation value of a Hamiltonian with its ground state being the solution of the problem under study. Typically, an exponential number of Pauli products in the Hamiltonian decomposition does not allow for the quantum speed up to be achieved. The Hadamard test based approach solves this problem, however, the performed simulations do not evidently prove that the ansatz circuit has a polynomial depth with respect to the number of qubits. The ansatz tree approach exploits an explicit form of the matrix what makes it possible to achieve an advantage over classical algorithms. In our numerical simulations with up to $n=11$ qubits, this method reveals the exponential speed up.
Orientation dynamics of two-dimensional concavo-convex bodies

We study the orientation dynamics of two-dimensional concavo-convex solid bodies more dense than the fluid through which they fall under gravity. We show that the orientation dynamics of the body, quantified in terms of the angle $\phi$ relative to the horizontal, undergoes a transcritical bifurcation at a Reynolds number $Re_{c}^{(1)}$, and a subcritical pitchfork bifurcation at a Reynolds number $Re_{c}^{(2)}$. For $Re<Re_{c}^{(1)}$, the concave-downwards orientation of $\phi=0$ is unstable and bodies overturn into the $\phi=\pi$ orientation. For $Re_{c}^{(1)}<Re<Re_{c}^{(2)}$, the falling body has two stable equilibria at $\phi=0\text{ and }\phi=\pi$ for steady descent. For $Re>Re_{c}^{(2)}$, the concave-downwards orientation of $\phi=0$ is again unstable, and bodies that start concave-downwards exhibit overstable oscillations about the unstable fixed point, eventually tumbling into the stable $\phi=\pi$ orientation. The $Re_{c}^{(2)}\approx15$ at which the subcritical pitchfork bifurcation occurs is distinct from the $Re$ for the onset of vortex shedding, which causes the $\phi=\pi$ equilibrium to also become unstable, with bodies fluttering about $\phi=\pi$. The complex orientation dynamics of irregularly shaped bodies evidenced here are relevant in a wide range of settings, from the tumbling of hydrometeors to settling of mollusk shells.
From Hubble to Snap Parameters: A Gaussian Process Reconstruction

By using recent H(z) and SNe Ia data, we reconstruct the evolution of kinematic parameters H(z), q(z), jerk and snap, using a model-independent, non-parametric method, namely, the Gaussian Processes. Throughout the present analysis, we have allowed for a spatial curvature prior, based on Planck 18 constraints. In the case of SNe Ia, we modify a python package (GaPP) in order to obtain the reconstruction of the fourth derivative of a function, thereby allowing us to obtain the snap from comoving distances. Furthermore, using a method of importance sampling, we combine H(z) and SNe Ia reconstructions in order to find joint constraints for the kinematic parameters. We find for the current values of the parameters: H0 = 67.2 ± 6.2 km/s/Mpc, $q_0 = -0.54^{+0.06}_{-0.05}$, $j_0=0.94^{+0.20}_{-0.18}$, $s_0=-0.62^{+0.26}_{-0.25}$ at 1σ c.l. We find that these reconstructions are compatible with the predictions from flat ΛCDM model, at least for 2σ confidence intervals.
DAS: Neural Architecture Search via Distinguishing Activation Score

Neural Architecture Search (NAS) is an automatic technique that can search for well-performed architectures for a specific task. Although NAS surpasses human-designed architecture in many fields, the high computational cost of architecture evaluation it requires hinders its development. A feasible solution is to directly evaluate some metrics in the initial stage of the architecture without any training. NAS without training (WOT) score is such a metric, which estimates the final trained accuracy of the architecture through the ability to distinguish different inputs in the activation layer. However, WOT score is not an atomic metric, meaning that it does not represent a fundamental indicator of the architecture. The contributions of this paper are in three folds. First, we decouple WOT into two atomic metrics which represent the distinguishing ability of the network and the number of activation units, and explore better combination rules named (Distinguishing Activation Score) DAS. We prove the correctness of decoupling theoretically and confirmed the effectiveness of the rules experimentally. Second, in order to improve the prediction accuracy of DAS to meet practical search requirements, we propose a fast training strategy. When DAS is used in combination with the fast training strategy, it yields more improvements. Third, we propose a dataset called Darts-training-bench (DTB), which fills the gap that no training states of architecture in existing datasets. Our proposed method has 1.04$\times$ - 1.56$\times$ improvements on NAS-Bench-101, Network Design Spaces, and the proposed DTB.
Spin-lattice interaction parameters from first principles: Theory and implementation

A scheme is presented to calculate on a first-principles level the spin-lattice coupling (SLC) parameters needed to perform combined molecular-spin dynamics (MSD) simulations. By treating changes to the spin configuration and atomic positions on the same level, closed expressions for the atomic SLC parameters could be derived in a coherent way up to any order. The properties of the SLC parameters are discussed considering separately the symmetric and antisymmetric parts of the SLC tensor. The changes due to atomic displacements of the spin-spin exchange coupling (SSC) parameters estimated using the SLC parameters are compared with the SSC parameters calculated for an embedded cluster with the central atom displaced, demonstrating good agreement of these results. Moreover, this allows to study the impact of different SLC contributions, linear and quadratic with respect to displacements, on the properties of the modified SSC parameters. In addition, we represent an approach to calculate the site-diagonal SLC parameters characterizing local magnetic anisotropy induced by a lattice distortion, which is a counterpart of the approach based on magnetic torque used for the investigations of magneto-crystalline anisotropy (MCA) as well as for calculations of the MCA constants. In particular, the dependence of the induced magnetic torque on different types of atomic displacements is analyzed.
The Low-Energy Module (LEM): Development of a CubeSat Spectrometer for Sub-MeV Particles and Gamma-Ray Burst Detection

Accurate flux measurement of low energy charged particles, trapped in the magnetosphere, is necessary for Space Weather characterization and to study the coupling between the lithosphere and magnetosphere, allowing the investigation of the correlations between seismic events and particle precipitation from Van Allen Belts. In this work, the project of a CubeSat space spectrometer, the Low Energy Module (LEM), is shown. The detector will be able to perform an event-based measurement of energy, arrival direction, and composition of low-energy charged particles down to 0.1 MeV. Moreover, thanks to a CdZnTe mini-calorimeter, the LEM spectrometer also allows photon detection in the sub-MeV range, joining the quest for the investigation of the nature of Gamma Ray Bursts. The particle identification of the LEM relies on the $\Delta E - E$ technique performed by thin silicon detectors. This multipurpose spectrometer will fit within a 10x10x10 $\text{cm}^3$ CubeSat frame and it will be constructed as a joining project between the University of Trento, FBK, and INFN-TIFPA. To fulfil the size and mass requirements an innovative approach, based on active particle collimation, was designed for the LEM, this avoids heavy/bulky passive collimators of previous space detectors. In this paper, we will present the LEM geometry, its detection concept, and the results from the developed GEANT4 simulation.
Dynamics of $\vartheta$-solitons in the HPD state of superfluid $^3$He-B

In this note we present numerical study of spin dynamics of $\vartheta$-soliton in Homogeneously precessing domain (HPD) in superfluid $^3$He-B. The soliton consists of a small heavy core and long tails, in 1D simulation we observe their mutual motion. We also discuss mass density and tension of the soliton membrane and its 3D oscillations in a cylindrical cell.
Partitions into semiprimes

Let $\mathbb{P}$ denote the set of primes and $\mathcal{N}\subset \mathbb{N}$ be a set with arbitrary weights attached to its elements. Set $\mathfrak{p}_{\mathcal{N}}(n)$ to be the restricted partition function which counts partitions of $n$ with all its parts lying in $\mathcal{N}$. By employing a suitable variation of the Hardy-Littlewood circle method we provide the asymptotic formula of $\mathfrak{p}_{\mathcal{N}}(n)$ for the set of semiprimes $\mathcal{N} = \{p_1 p_2 : p_1, p_2 \in \mathbb{P}\}$ in different set-ups (counting factors, repeating the count of factors, and different factors). In order to deal with the minor arc, we investigate a double Weyl sum over prime products and find its corresponding bound thereby extending some of the results of Vinogradov on partitions. We also describe a methodology to find the asymptotic partition $\mathfrak{p}_{\mathcal{N}}(n)$ for general weighted sets $\mathcal{N}$ by assigning different strategies for the major, non-principal major, and minor arcs. Our result is contextualized alongside other recent results in partition asymptotics.
An Optical Incoherent Correlator

This paper will discuss a technique for cross-correlating a one-dimensional input signal with a library of stored reference signals simultaneously. The technique involves the linear scanning of a temporally-modulated image of a photographic reference mask across a temporally-integrating read-out device. First, a physical feeling for the technique is given, followed by a mathematical analysis, a description of a breadboard system now in operation, a simple experiment to demonstrate the correlators features, and a mention of some alternate configurations desirable for specific applications.
General solution for the electro-optic effect

Perturbation theory for the plane-wave eigenvector equation of crystal optics is developed. General expressions for the refractive-index changes that result from a general homogeneous dielectric perturbation are found for isotropic solids, uniaxial crystals, and biaxial crystals. The expressions may be applied directly to the electro-optic effect for any direction of light propagation, for either state of light polarization, and for any direction of electric-field application. They are also useful for the photoelastic effect when the elastic perturbation is homogeneous.
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks

In the era of Internet of Things (IoT), network-wide anomaly detection is a crucial part of monitoring IoT networks due to the inherent security vulnerabilities of most IoT devices. Principal Components Analysis (PCA) has been proposed to separate network traffics into two disjoint subspaces corresponding to normal and malicious behaviors for anomaly detection. However, the privacy concerns and limitations of devices’ computing resources compromise the practical effectiveness of PCA. We propose a federated PCA learning using Grassmann manifold optimization, which coordinates IoT devices to aggregate a joint profile of normal network behaviors for anomaly detection. First, we introduce a privacy-preserving federated PCA framework to simultaneously capture the profile of various IoT devices’ traffic. Then, we investigate the alternating direction method of multipliers gradient-based learning on the Grassmann manifold to guarantee fast training and low detecting latency with limited computational resources. Finally, we show that the computational complexity of the Grassmann manifold-based algorithm is satisfactory for hardware-constrained IoT devices. Empirical results on the NSL-KDD dataset demonstrate that our method outperforms baseline approaches.