Miracle-dz/UniFAR-FTUs huggingface dataset:
A pairwise hydrodynamic theory for flow-induced particle transport in shear and pressure-driven flows

Abstract An exact pairwise hydrodynamic theory is developed for the flow-induced spatial distribution of particles in dilute polydisperse suspensions undergoing two-dimensional unidirectional flows, including shear and planar Poiseuille flows. Coupled diffusive fluxes and a drift velocity are extracted from a Boltzmann-like master equation. A boundary layer is predicted in regions where the shear rate vanishes with thickness set by the radii of the upstream collision cross-sections for pair interactions. An analysis of this region yields linearly vanishing drift velocities and non-vanishing diffusivities where the shear rate vanishes, thus circumventing the source of the singular particle distribution predicted by the usual models. Outside of the boundary layer, a power-law particle distribution is predicted with exponent equal to minus half the exponent of the local shear rate. Trajectories for particles with symmetry-breaking contact interactions (e.g. rough particles, permeable particles, emulsion drops) are analytically integrated to yield particle displacements given by quadratures of hard-sphere (or spherical drop) mobility functions. Using this analysis, stationary particle distributions are obtained for suspensions in Poiseuille flow. The scale for the particle distribution in monodisperse suspensions is set by the collision cross-section of the particles but its shape is almost universal. Results for polydisperse suspensions show size segregation in the central boundary layer with enrichment of smaller particles. Particle densities at the centreline scale approximately with the inverse square root of particle size. A superposition approximation reliably predicts the exact results over a broad range of parameters. The predictions agree with experiments in suspensions up to approximately 20 % volume fraction without fitting parameters.
Supersymmetry and Supergravity: Phenomenology and Grand Unification

A survey is given of supersymmetry and supergravity and their phenomenology. Some of the topics discussed are the basic ideas of global supersymmetry, the minimal supersymmetric Standard Model (MSSM) and its phenomenology, the basic ideas of local supersymmetry (supergravity), grand unification, supersymmetry breaking in supergravity grand unified models, radiative breaking of SU(2) {times} U(1), proton decay, cosmological constraints, and predictions of supergravity grand unified models. While the number of detailed derivations are necessarily limited, a sufficient number of results are given so that a reader can get a working knowledge of this field.
Siamese network target tracking algorithm fused with semantic feature network

Aiming at the situation that CFNet, the back propagation filter tracking algorithm based on the siamese network, is likely to cause the model drift tracking effect to decrease when it encounters the interference of similar objects or the background information is similar to the foreground target, a siamese network target tracking algorithm fused with semantic feature network is proposed. In image processing, through the deep network of deep convolutional neural network, rich semantic information can be extracted. These semantic information can cause similar interference, motion blur, severe target deformation, etc. In situations, it is very useful to identify the target. In the proposed algorithm, a semantic feature network is added to the original network structure of CFNet, which is complementary to the appearance feature network of CFNet. The training of the two feature networks is independent to maintain the heterogeneity of the two features and obtain their respective response maps. Later, the fusion is performed by calculating the confidence of the two response graphs, which improves the discriminative ability of algorithm. Tests show that, compared with the CFNet tracking algorithm, the proposed algorithm performs better in situations such as analog interference or the background information is similar to the foreground target.
A Comprehensive Review of the State-of-the-Art on Security and Privacy Issues in Healthcare

Currently, healthcare is critical environment in our society, which attracts attention to malicious activities and has caused an important number of damaging attacks. In parallel, the recent advancements in technologies, computing systems, and wireless communications are changing healthcare environment by adding different improvements and complexity to it. This article reviews the current state of the literature and provides a holistic view of cybersecurity in healthcare. With this purpose in mind, the article enumerates the main stakeholders and architecture implemented in the healthcare environment, as well as the main security issues (threats, attacks, etc.) produced in healthcare. In this context, this work maps the threats collected with a widely used knowledge-based framework, MITRE ATT&CK, building a contribution not seen so far. This article also enumerates the security mechanisms created to protect healthcare, identifying the principal research lines addressed in the literature, and listing the available public security-focused datasets used in machine-learning to provide security in the medical domain. To conclude, the research challenges that need to be addressed for future research works in this area are presented.
Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of attention lately due to its promise of reducing the computational energy, latency, as well as learning complexity in artificial neural networks. Taking inspiration from neuroscience, this interdisciplinary field performs a multi-stack optimization across devices, circuits, and algorithms by providing an end-to-end approach to achieving brain-like efficiency in machine intelligence. On one side, neuromorphic computing introduces a new algorithmic paradigm, known as Spiking Neural Networks (SNNs), which is a significant shift from standard deep learning and transmits information as spikes (“1” or “0”) rather than analog values. This has opened up novel algorithmic research directions to formulate methods to represent data in spike-trains, develop neuron models that can process information over time, design learning algorithms for event-driven dynamical systems, and engineer network architectures amenable to sparse, asynchronous, event-driven computing to achieve lower power consumption. On the other side, a parallel research thrust focuses on development of efficient computing platforms for new algorithms. Standard accelerators that are amenable to deep learning workloads are not particularly suitable to handle processing across multiple timesteps efficiently. To that effect, researchers have designed neuromorphic hardware that rely on event-driven sparse computations as well as efficient matrix operations. While most large-scale neuromorphic systems have been explored based on CMOS technology, recently, Non-Volatile Memory (NVM) technologies show promise toward implementing bio-mimetic functionalities on single devices. In this article, we outline several strides that neuromorphic computing based on spiking neural networks (SNNs) has taken over the recent past, and we present our outlook on the challenges that this field needs to overcome to make the bio-plausibility route a successful one.
10-GHz Tenerife cosmic microwave background observations at 8° resolution and their analysis using a new maximum entropy method

The complete set of data from the Tenerife 10-GHz (8° FWHM) twin-horn, drift scan experiment is described. These data are affected by both long-term atmospheric baseline drifts and short-term noise. A new maximum entropy procedure, utilizing the time invariance and spatial continuity of the astronomical signal, is used to achieve a clean separation of these effects from the astronomical signal, and to deconvolve the effects of the beam-switching. We use a fully positive/negative algorithm to produce two-dimensional maps of the intrinsic sky fluctuations. Known discrete sources and Galactic features are identified in the deconvolved map. The data from the 10-GHz experiment, after baseline subtraction with MEM, are then analysed using conventional techniques, and new constraints on Galactic emission are made.
Progress in Multivariate Cryptography: Systematic Review, Challenges, and Research Directions

Multivariate Public Key Cryptosystem (MPKC) seem to be promising toward future digital security even in the presence of quantum adversaries. MPKCs derive their security from the difficulty of solving a random system of multivariate polynomial equations over a finite field, which is known to be an NP-hard problem. This article aims at presenting a comprehensive survey that covers multivariate public key encryption and signature schemes specifically targeting toward security, efficiency, and parameter choice. The survey starts by giving an overview of the existing security challenges which include structural attacks such as MinRank attack, differential attack, and finding Gröbner basis for direct attack, and so on. Additionally, it discusses the necessary algorithms for the implementation of the multivariate schemes. This study also compares the promising multivariate encryption and signature schemes. The critical open challenges that are reviewed in this survey will serve as a single comprehensive source of information on multivariate encryption and signature schemes and a ready reference for researchers working in this rising area of public key cryptography.
User Perception of Recommendation Explanation: Are Your Explanations What Users Need?

As recommender systems become increasingly important in daily human decision-making, users are demanding convincing explanations to understand why they get the specific recommendation results. Although a number of explainable recommender systems have recently been proposed, there still lacks an understanding of what users really need in a recommendation explanation. The actual reason behind users’ intention to examine and consume (e.g., click and watch a movie) can be the window to answer this question and is named as self-explanation in this work. In addition, humans usually make recommendations accompanied by explanations, but there remain fewer studies on how humans explain and what we can learn from human-generated explanations. To investigate these questions, we conduct a novel multi-role, multi-session user study in which users interact with multiple types of system-generated explanations as well as human-generated explanations, namely peer-explanation. During the study, users’ intentions, expectations, and experiences are tracked in several phases, including before and after the users are presented with an explanation and after the content is examined. Through comprehensive investigations, three main findings have been made: First, we observe not only the positive but also the negative effects of explanations, and the impact varies across different types of explanations. Moreover, human-generated explanation, peer-explanation, performs better in increasing user intentions and helping users to better construct preferences, which results in better user satisfaction. Second, based on users’ self-explanation, the information accuracy is measured and found to be a major factor associated with user satisfaction. Some other factors, such as unfamiliarity and similarity, are also discovered and summarized. Third, through annotations of the information aspects used in the human-generated self-explanation and peer-explanation, patterns of how humans explain are investigated, including what information and how much information is utilized. In addition, based on the findings, a human-inspired explanation approach is proposed and found to increase user satisfaction, revealing the potential improvement of further incorporating more human patterns in recommendation explanations. These findings have shed light on the deeper understanding of the recommendation explanation and further research on its evaluation and generation. Furthermore, the collected data, including human-generated explanations by both the external peers and the users’ selves, will be released to support future research works on explanation evaluation.
Towards understanding thermal history of the Universe through direct and indirect detection of dark matter

We examine the question to what extent prospective detection of dark matter by direct and indirect- detection experiments could shed light on what fraction of dark matter was generated thermally via the freeze-out process in the early Universe. By simulating putative signals that could be seen in the near future and using them to reconstruct WIMP dark matter properties, we show that, in a model- independent approach this could only be achieved in a thin sliver of the parameter space. However, with additional theoretical input the hypothesis about the thermal freeze-out as the dominant mechanism for generating dark matter can potentially be verified. We illustrate this with two examples: an effective field theory of dark matter with a vector messenger and a higgsino or wino dark matter within the MSSM.
Submillimeter Emission from the Hot Molecular Jet HH 211

We observed the HH 211 jet in the submillimeter continuum and the CO (3-2) and SiO (8-7) transitions with the Submillimeter Array. The continuum source detected at the center of the outflow shows an elongated morphology, perpendicular to the direction of the outflow axis. The high-velocity emission of both molecules shows a knotty and highly collimated structure. The SiO (8-7) emission at the base of the outflow, close to the driving source, spans a wide range of velocities, from -20 up to 40 km s-1. This suggests that a wide-angle wind may be the driving mechanism of the HH 211 outflow. For distances ≥5'' (~1500 AU) from the driving source, emission from both transitions follows a Hubble-law behavior, with SiO (8-7) reaching higher velocities than CO (3-2) and being located upstream of the CO (3-2) knots. This indicates that the SiO (8-7) emission is likely tracing entrained gas very close to the primary jet, while the CO (3-2) is tracing less dense entrained gas. From the SiO (5-4) data of Hirano et al., we find that the SiO (8-7)/SiO (5-4) brightness temperature ratio along the jet decreases for knots far from the driving source. This is consistent with the density decreasing along the jet, from (3-10) × 106 cm-3 at 500 AU to (0.8-4) × 106 cm-3 at 5000 AU from the driving source.
The Likelihood Ratio Test for the Presence of Immunes in a Censored Sample

Models for populations with immune or cured individuals but with others subject to failure are important in many areas, such as medical statistics and criminology. One method of analysis of data from such populations involves estimating an immune proportion 1 − p and the parameter(s) of a failure distribution for those individuals subject to failure. We use the exponential distribution with parameter λ for the latter and a mixture of this distribution with a mass 1 − p at infinity to model the complete data. This paper develops the asymptotic theory of a test for whether an immune proportion is indeed present in the population, i.e., for H 0:p = 1. This involves testing at the boundary of the parameter space for p. We use a likelihood ratio test for H 0. and prove that minus twice the logarithm of the likelihood ratio has as an asymptotic distribution, not the chi-square distribution, but a 50–50 mixture of a chi-square distribution with 1 degree of freedom, and a point mass at 0. The result is proved und.
Strongly interacting matter in a finite volume

In this work we study the Polyakov-Quark-Meson model for N_f=2 and N_c=3 in a finite volume with the Functional Renormalization Group in a local potential approximation. We choose for the spatial momentum modes periodic and antiperiodic boundary conditions. Because of the lack of a zero mode in the case of antiperiodic boundary conditions we realize a clearly different behavior of the results. We solve the gap-equations for the Polyakov-loop-variable \Phi and its conjugate \Phi^* for different box sizes L as a function of the temperature $T$ and the chemical potential \mu. With these we calculate the pion decay constant and obtain the phase diagram and the pressure. We also study whether the results converge with increasing truncation order N and whether the finite volume results converge with increasing volume size to those of the infinite volume case. For the case of an infinite volume we further solve the gap-equations on all scales. Therefore, we include for the solution of the flow equation an indirect scale dependence of the Polyakov-loop-variable. With this we extend recent approaches which solve the gap-equations only at the infrared cutoff. We calculate with this several thermodynamic variables like the pressure and the trace anomaly. Additionally, we include a non-zero magnetic field and study the possibility of an inverse magnetic catalysis under variation of T_0 with the magnetic field.
Bounds on Instantaneous Nonlocal Quantum Computation

Instantaneous nonlocal quantum computation refers to a process in which spacelike separated parties simulate a nonlocal quantum operation on their joint systems through the consumption of pre-shared entanglement. To prevent a violation of causality, this simulation succeeds up to local errors that can only be corrected after the parties communicate classically with one another. However, this communication is non-interactive, and it involves just the broadcasting of local measurement outcomes. We refer to this operational paradigm as local operations and broadcast communication (LOBC) to distinguish it from the standard local operations and (interactive) classical communication (LOCC). In this paper, we show that an arbitrary two-qubit gate can be implemented by LOBC with <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-error using <inline-formula> <tex-math notation="LaTeX">${O}(\log (1/\epsilon))$ </tex-math></inline-formula> entangled bits (ebits). This offers an exponential improvement over the best known two-qubit protocols, whose ebit costs behave as <inline-formula> <tex-math notation="LaTeX">${O}(1/\epsilon)$ </tex-math></inline-formula>. We also consider the family of binary controlled gates on dimensions <inline-formula> <tex-math notation="LaTeX">${d}_{A}\otimes {d}_{B}$ </tex-math></inline-formula>. We find that any hermitian gate of this form can be implemented by LOBC using a single shared ebit. In sharp contrast, a lower bound of <inline-formula> <tex-math notation="LaTeX">$\log {d}_{B}$ </tex-math></inline-formula> ebits is shown in the case of generic (i.e. non-hermitian) gates from this family, even when <inline-formula> <tex-math notation="LaTeX">${d}_{A}=2$ </tex-math></inline-formula>. This demonstrates an unbounded gap between the entanglement costs of LOCC and LOBC gate implementation. Whereas previous lower bounds on the entanglement cost for instantaneous nonlocal computation restrict the minimum dimension of the needed entanglement, we bound its entanglement entropy. To our knowledge this is the first such lower bound of its kind.
The Chemical Compositions of the SRd Variable Stars. III. KK Aquilae, AG Aurigae, Z Aurigae, W Leo Minoris, and WW Tauri

Chemical compositions are derived from high‐resolution spectra for five field SRd variables. These supergiants not previously analyzed are shown to be metal poor: KK Aql with [Fe/H] = -1.2, AG Aur with [Fe/H] = -1.8, Z Aur with [Fe/H] = -1.4, W LMi with [Fe/H] = -1.1, and WW Tau with [Fe/H] = -1.1. Their compositions are, except for two anomalies, identical to within the measurement errors to the compositions of subdwarfs, subgiants, and less evolved giants of the same [Fe/H]. One anomaly is an s‐process enrichment for KK Aql, the first such enrichment reported for an SRd variable. The second and more remarkable anomaly is a strong lithium enrichment for W LMi, also a first for field SRd variables. The Li i λ6707 profile is not simply that of a photospheric line but includes strong absorption from redshifted gas, suggesting, perhaps, that lithium enrichment results from accretion of Li‐rich gas. This potential clue to lithium enrichment is discussed in light of various proposals for lithium synthesis in evolved stars.
Properties of the spin-liquid phase in the vicinity of the Lifshitz transition from Néel to spin-spiral state in frustrated magnets

Three decades ago Ioffe and Larkin pointed out a generic mechanism for the formation of a gapped spin liquid. In the case when a classical two-dimensional (2D) frustrated Heisenberg magnet undergoes a Lifshitz transition between a collinear N\'eel phase and a spin spiral phase, quantum effects usually lead to the development of a spin-liquid phase sandwiched between the N\'eel and spin spiral phases. In the present work, using field theory techniques, we study properties of this universal spin liquid phase. We examine the phase diagram near the Lifshitz point and calculate the positions of critical points, excitation spectra, and spin-spin correlations functions. We argue that the spin liquid in the vicinity of 2D Lifshitz point (LP) is similar to the gapped Haldane phase in integer-spin 1D chains. We also consider a specific example of a frustrated system with the spiral-N\'eel LP, the $J_1-J_3$ antiferromagnet on the square lattice that manifests the spin liquid behavior. We present numerical series expansion calculations for this model and compare results of the calculations with predictions of the developed field theory.
Robust Spread Spectrum Steganography Based on the Nonuniform DCT Characteristic

In existing image hiding Spread Spectrum (SS) algorithm a reduction of scaling parameter is employed to improve imperceptibility but may lead to an increase in secret information extraction errors. A robust SS steganography, based on the Nonuniform Discrete Cosine Transform (NDCT) characteristic, is proposed. If the secret information, embedded in the NDCT coefficient matrixes of every N×N host image, is a real symmetric matrix, the difference matrix between stego-image and host image is also a real symmetric matrix. Due to the NDCT characteristic above, based on some corresponding change of stego-image, the proposed robust SS algorithm can improve imperceptibility of stego-image and the scaling parameter was not reduced. Simulation results verify the feasibility and validity of the algorithm.