Miracle-dz/UniFAR-FTUs huggingface dataset:
SUNDEW: An Ensemble of Predictors for Case-Sensitive Detection of Malware

—Malware programs are diverse, with varying objectives, functionalities, and threat levels ranging from mere pop-ups to ﬁnancial losses. Consequently, their run-time footprints across the system differ, impacting the optimal data source (Network, Operating system (OS), Hardware) and features that are instrumental to malware detection. Further, the variations in threat levels of malware classes affect the user requirements for detection. Thus, the optimal tuple of (cid:104) data - source , features , user - requirements (cid:105) is different for each malware class, impacting the state-of-the-art detection solutions that are agnostic to these subtle differences. This paper presents SUNDEW, a framework to detect malware classes using their optimal tuple of (cid:104) data - source , features , user - requirements (cid:105) . SUNDEW uses an ensemble of specialized predictors, each trained with a particular data source (network, OS, and hardware) and tuned for features and requirements of a speciﬁc class. While the specialized ensemble with a holistic view across the system improves detection, aggregating the independent conﬂicting inferences from the different predictors is challenging. SUNDEW resolves such conﬂicts with a hierarchical aggregation considering the threat-level, noise in the data sources, and prior domain knowledge. We evaluate SUNDEW on a real-world dataset of over 10,000 malware samples from 8 classes. It achieves an F1-Score of one for most classes, with an average of 0.93 and a limited performance overhead of 1 . 5% .
Theoretical error estimates for computing the matrix logarithm by Padé-type approximants

In this article, we focus on the error that is committed when computing the matrix logarithm using the Gauss--Legendre quadrature rules. These formulas can be interpreted as Pad\'e approximants of a suitable Gauss hypergeometric function. Empirical observation tells us that the convergence of these quadratures becomes slow when the matrix is not close to the identity matrix, thus suggesting the usage of an inverse scaling and squaring approach for obtaining a matrix with this property. The novelty of this work is the introduction of error estimates that can be used to select a priori both the number of Legendre points needed to obtain a given accuracy and the number of inverse scaling and squaring to be performed. We include some numerical experiments to show the reliability of the estimates introduced.
Accelerated Distributed Projected Gradient Descent for Convex Optimization with Clique-wise Coupled Constraints

This paper addresses a distributed convex optimization problem with a class of coupled constraints, which arise in a multi-agent system composed of multiple communities modeled by cliques. First, we propose a fully distributed gradient-based algorithm with a novel operator inspired by the convex projection, called the clique-based projection. Next, we scrutinize the convergence properties for both diminishing and fixed step sizes. For diminishing ones, we show the convergence to an optimal solution under the assumptions of the smoothness of an objective function and the compactness of the constraint set. Additionally, when the objective function is strongly monotone, the strict convergence to the unique solution is proved without the assumption of compactness. For fixed step sizes, we prove the non-ergodic convergence rate of O(1/k) concerning the objective residual under the assumption of the smoothness of the objective function. Furthermore, we apply Nesterov's acceleration method to the proposed algorithm and establish the convergence rate of O(1/k^2). Numerical experiments illustrate the effectiveness of the proposed method.
Vocal Breath Sound Based Gender Classification

Voiced speech signals such as continuous speech are known to have acoustic features such as pitch(F0), and formant frequencies(F1, F2, F3) which can be used for gender classification. However, gender classification studies using non-speech signals such as vocal breath sounds have not been explored as they lack typical gender-specific acoustic features. In this work, we explore whether vocal breath sounds encode gender information and if so, to what extent it can be used for automatic gender classification. In this study, we explore the use of data-driven and knowledge-based features from vocal breath sounds as well as the classifier complexity for gender classification. We also explore the importance of the location and duration of breath signal segments to be used for automatic classification. Experiments with 54.23 minutes of male and 51.83 minutes of female breath sounds reveal that knowledge-based features, namely MFCC statistics, with low-complexity classifier perform comparably to the data-driven features with classifiers of higher complexity. Breath segments with an average duration of 3 seconds are found to be the best choice irrespective of the location which avoids the need for breath cycle boundary annotation.
Interactive Context-Aware Network for RGB-T Salient Object Detection

Salient object detection (SOD) focuses on distinguishing the most conspicuous objects in the scene. However, most related works are based on RGB images, which lose massive useful information. Accordingly, with the maturity of thermal technology, RGB-T (RGB-Thermal) multi-modality tasks attain more and more attention. Thermal infrared images carry important information which can be used to improve the accuracy of SOD prediction. To accomplish it, the methods to integrate multi-modal information and suppress noises are critical. In this paper, we propose a novel network called Interactive Context-Aware Network (ICANet). It contains three modules that can effectively perform the cross-modal and cross-scale fusions. We design a Hybrid Feature Fusion (HFF) module to integrate the features of two modalities, which utilizes two types of feature extraction. The Multi-Scale Attention Reinforcement (MSAR) and Upper Fusion (UF) blocks are responsible for the cross-scale fusion that converges different levels of features and generate the prediction maps. We also raise a novel Context-Aware Multi-Supervised Network (CAMSNet) to calculate the content loss between the prediction and the ground truth (GT). Experiments prove that our network performs favorably against the state-of-the-art RGB-T SOD methods.
Hardware optimized parity check gates for superconducting surface codes

Error correcting codes use multi-qubit measurements to realize fault-tolerant quantum logic steps. In fact, the resources needed to scale-up fault-tolerant quantum computing hardware are largely set by this task. Tailoring next-generation processors for joint measurements, therefore, could result in improvements to speed, accuracy, or cost—accelerating the development large-scale quantum computers. Here, we motivate such explorations by analyzing an unconventional surface code based on multi-body interactions between superconducting transmon qubits. Our central consideration, Hardware Optimized Parity (HOP) gates, achieves stabilizer-type measurements through simultaneous multi-qubit conditional phase accumulation. Despite the multi-body ef-fects that underpin this approach, our estimates of logical faults suggest that this design can be at least as robust to realistic noise as conventional designs. We show a higher threshold of 1 . 25 × 10 − 3 compared to the standard code’s 0 . 79 × 10 − 3 . However, in the HOP code the logical error rate decreases more slowly with decreasing physical error rate. Our results point to a fruitful path forward towards extending gate-model platforms for error correction at the dawn of its empirical development.
Bounding Box Priors for Cell Detection with Point Annotations

The size of an individual cell type, such as a red blood cell, does not vary much among humans. We use this knowledge as a prior for classifying and detecting cells in images with only a few ground truth bounding box annotations, while most of the cells are annotated with points. This setting leads to weakly semi-supervised learning. We propose replacing points with either stochastic (ST) boxes or bounding box predictions during the training process. The proposed "mean-IOU" ST box maximizes the overlap with all the boxes belonging to the sample space with a class-specific approximated prior probability distribution of bounding boxes. Our method trains with both box- and point-labelled images in conjunction, unlike the existing methods, which train first with box- and then point-labelled images. In the most challenging setting, when only 5% images are box-labelled, quantitative experiments on a urine dataset show that our one-stage method outperforms two-stage methods by 5.56 mAP. Furthermore, we suggest an approach that partially answers "how many box-labelled annotations are necessary? " before training a machine learning model. The code is available at https://github.com/hariagr/bbox_prior
Weaving paper strips for designing of general curved surface with geometrical elasticity

This study proposes 'amigami' as a new method of creating a general curved surface. It conducts the shape optimization of weaving paper strips based on the theory of nonlinear elasticity on Riemannian manifolds. The target surface is split into small curved strips by cutting the medium along with its coordinates, and each strip is embedded into a flat paper sheet to minimize a strain energy functional due to the in-plane deformation. The weak form equilibrium equation is derived from a Lie derivative with the virtual displacement vector field, and the equation is solved numerically using the Galerkin method with a non-uniform B-spline manifold. As a demonstration, we made catenoid and helicoid surfaces which are made by waving 54 paper strips. The papercraft reminds us of the isometric transformation from the catenoid to the helicoid and vice versa. We also provide strain estimates for paper strips with rigorous mathematical proof. This estimating process is a generalization of the classical beam theory of Euler-Bernoulli to a modern geometrical elasticity.
Assessing passive scalar dynamics in bubble-induced turbulence using direct numerical simulations

Abstract By using direct numerical simulations (DNS) of bubbly flows with passive scalars, we show a transition in the scalar spectra from a $k^{-5/3}$ to a $k^{-3}$ scaling with the wavenumber $k$, in contrast with those of single-phase isotropic turbulence. For cases with a mean scalar gradient in the horizontal direction, the scalar spectrum decays faster than $k^{-3}$ at high wavenumbers. While the $k^{-3}$ scaling is well established in the bubbly flow velocity spectrum, the scalar spectrum behaviour is not fully understood. We find that the transition length scale of the scalar spectra is comparable to or below the bubble diameter and decreases with the molecular diffusivity of the scalar in the liquid. We use DNS to compute the scalar spectrum budget and show that the scalar fluctuations are produced by the mean scalar gradient at length scales above the bubble diameter, contrary to the velocity fluctuations. At length scales below the bubble diameter, the net scalar transfer scales as $k^{-1}$ inducing the $k^{-3}$ scaling of the scalar spectra. This finding is consistent with the hypothesis proposed by Dung et al. (J. Fluid Mech., vol. 958, 2023, p. A5) about the physical mechanism behind the $k^{-3}$ scaling. We also show dependencies of the bubble suspension's convective scalar diffusivity on the gas volume fraction and molecular diffusivity that differ based on the direction of the mean scalar gradient. For a mean scalar gradient in the vertical direction, we find and qualitatively explain a significant effect of the molecular diffusivity in the gas on the convective scalar diffusivity.
Quantifying continuity of characteristics of composite quantum systems

We describe the most general form of the Alicki–Fannes–Winter method for obtaining uniform continuity bounds for characteristics of composite quantum systems and consider several modifications of this method, which make it applicable to a wide class of characteristics in both finite-dimensional and infinite-dimensional cases. We present uniform continuity bounds for the most important characteristics of composite quantum systems. Along with the results obtained earlier by various authors, we describe a number of new results proved by the proposed methods. In particular, we obtain new continuity bounds for the quantum discord, one-way classical correlation and its regularization in finite-dimensional and infinite-dimensional bipartite quantum systems.
MaskedSpeech: Context-aware Speech Synthesis with Masking Strategy

Humans often speak in a continuous manner which leads to coherent and consistent prosody properties across neighboring utterances. However, most state-of-the-art speech synthesis systems only consider the information within each sentence and ignore the contextual semantic and acoustic features. This makes it inadequate to generate high-quality paragraph-level speech which requires high expressiveness and naturalness. To synthesize natural and expressive speech for a paragraph, a context-aware speech synthesis system named MaskedSpeech is proposed in this paper, which considers both contextual semantic and acoustic features. Inspired by the masking strategy in the speech editing research, the acoustic features of the current sentence are masked out and concatenated with those of contextual speech, and further used as additional model input. The phoneme encoder takes the concatenated phoneme sequence from neighboring sentences as input and learns fine-grained semantic information from contextual text. Furthermore, cross-utterance coarse-grained semantic features are employed to improve the prosody generation. The model is trained to reconstruct the masked acoustic features with the augmentation of both the contextual semantic and acoustic features. Experimental results demonstrate that the proposed MaskedSpeech outperformed the baseline system significantly in terms of naturalness and expressiveness.
Towards Improved Learning in Gaussian Processes: The Best of Two Worlds

Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common choices for approximate inference are Expectation Propagation (EP) and Variational Inference (VI), which have complementary strengths and weaknesses. While VI's lower bound to the marginal likelihood is a suitable objective for inferring the approximate posterior, it does not automatically imply it is a good learning objective for hyperparameter optimization. We design a hybrid training procedure where the inference leverages conjugate-computation VI and the learning uses an EP-like marginal likelihood approximation. We empirically demonstrate on binary classification that this provides a good learning objective and generalizes better.
Studying neutrino oscillations at DUNE through dynamical Lorentz symmetry breaking in four-Majorana fermion model

We study the impact of the dynamical Lorentz symmetry breaking induced by the auxiliary gauge fields of neutrino on the oscillations probability at DUNE. The DLSB introduces an alternative energy-momentum relation of the neutrinos and thus results in a new oscillation probability. We extend the previously proposed four-Majorana fermion model that gives rise to DLSB after the type II seesaw mechanism by considering the electron neutrino forward scattering when passing through a medium. Moreover, we incorporate the three-flavor neutrino states, which introduce the CP-violation term inside the oscillation probability. The impact of DLSB parameters around the order of $10^{-2}-10^{-3}$, which are at a strong coupling regime, on the oscillation probability is found to be measurable at DUNE within the 20 years through $\nu_e$ and $\overline{\nu}_e$ disappearance signals. We also compare the predicted spectra of the DLSB oscillations and the oscillation with the CP-violating term equal to $\pi/2$ to conclude that the presence of DLSB would increase the systematic uncertainty for the measurement of CP-violation at DUNE.
The adhesion model as a field theory for cosmological clustering

The adhesion model has been proposed in the past as an improvement of the Zel'dovich approximation, providing a good description of the formation of the cosmic web. We recast the model as a field theory for cosmological large scale structure, adding a stochastic force to account for power generated from very short, highly non-linear scales that is uncorrelated with the initial power spectrum. The dynamics of this Stochastic Adhesion Model (SAM) is reminiscent of the well known Kardar-Parisi-Zhang equation with the difference that the viscosity and the noise spectrum are time dependent. Choosing the viscosity proportional to the growth factor D restricts the form of noise spectrum through a 1-loop renormalization argument. For this choice, the SAM field theory is renormalizable to one loop. We comment on the suitability of this model for describing the non-linear regime of the CDM power spectrum and its utility as a relatively simple approach to cosmological clustering.
Reference Governor Design in the Presence of Uncertain Polynomial Constraints

Reference governors are add-on schemes that are used to modify trajectories to prevent controlled dynamical systems from violating input or state constraints, and so are playing an increasingly important role in aerospace, robotic, and other applications. Here we present a novel reference governor design for systems whose polynomial constraints depend on unknown bounded parameters. This is a significant departure from earlier treatments of reference governors, where the constraints were linear or known, because here we transfer the uncertainties into the constraints instead of having them in the closed loop dynamics, which greatly simplifies the task of determining future evolution of the constraints. Unlike our earlier treatment of reference governors with polynomial constraints which transformed the constraints into linear ones that depend on an augmented state of the system, here we transform the constraints into linear ones that depend on both the system’s state and uncertain parameters. Convexity allows us to estimate the maximal output admissible set for an uncertain pre-stabilized linear system. We show that it is sufficient to only consider extreme values of the uncertain parameters when computing and propagating the polynomial constraints. We illustrate our method using an uncertain longitudinal dynamics for civilian aircraft which is controlled using a disturbance compensation method and needs to satisfy input and state constraints, and where our reference governor method ensures that safety constraints are always satisfied.
Chemical modification of silicene

Silicene is a two-dimensional material, which is composed of a single layer of silicon atoms with sp2-sp3 mixed hybridization. The sp2-sp3 mixed hybridization renders silicene excellent reactive ability, facilitating the chemical modification of silicene. It has been demonstrated that chemical modification effectively enables the tuning of the properties of silicene. We now review all kind of chemical modification methods for silicene including hydrogenation, halogenation, organic surface modification, oxidation, doping and alloying. The effects of these chemical modification methods on the geometrical, electronic, optical and magnetic properties of silicene are discussed. The potential applications of chemically modified silicene in a variety of fields such as electronics, optoelectronics and magnetoelectronics are introduced. We finally envision future work on the chemical modification of silicene for the sake of further advancing the development of silicene.
A Riemannian Genuine Measure of Entanglement for Pure States

While several measures exist for entanglement of multipartite pure states, a true entanglement measure for mixed states still eludes us. A deeper study of the geometry of quantum states may be the way to address this issue, on which context we come up with a measure for pure states based on a geodesic distance on the space of quantum states. Our measure satisfies all the desirable properties of a ``Genuine Measure of Entanglement"(GME), and in comparison with some of the other existing measures, shows better smoothness and discriminance.
Requirements Quality vs Process and Stakeholders' Well-being: A Case of a Nordic Bank

Requirements are key artefacts to describe the intended purpose of a software system. The quality of requirements is crucial for deciding what to do next, impacting the development process's effectiveness and efficiency. However, we know very little about the connection between practitioners' perceptions regarding requirements quality and its impact on the process or the feelings of the professionals involved in the development process. Objectives: This study investigates: i) How software development practitioners define requirements quality, ii) how the perceived quality of requirements impact process and stakeholders' well-being, and iii) what are the causes and potential solutions for poor-quality requirements. Method: This study was performed as a descriptive interview study at a sub-organization of a Nordic bank that develops its own web and mobile apps. The data collection comprises interviews with 20 practitioners, including requirements engineers, developers, testers, and newly employed developers, with five interviewees from each group. Results: The results show that different roles have different views on what makes a requirement good quality. Participants highlighted that, in general, they experience negative emotions, more work, and overhead communication when they work with requirements they perceive to be of poor quality. The practitioners also describe positive effects on their performance and positive feelings when they work with requirements that they perceive to be good.
Phase contrast imaging of Bose condensed clouds

Phase contrast imaging is used to observe Bose-Einstein condensates (BECs) at finite temperature in situ. The imaging technique is used to accurately derive the absolute phase shift of a probe laser beam due to both the condensate and the thermal cloud. The accuracy of the method is enhanced by using the periodicity of the intensity signal as a function of the accumulated phase. The measured density profiles can be described using a two relevant parameter fit, in which only the chemical potential and the temperature are to be determined. This allows us to directly compare the measured density profiles to different mean-field models in which the interaction between the condensed and thermal atoms is taken into account to various degrees.