Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional hydrodynamical CO5BOLD model atmospheres of red giant stars - I. Atmospheric structure of a giant located near the RGB tip

We investigate the character and role of convection in the atmosphere of a prototypical red giant located close to the red giant branch (RGB) tip with atmospheric parameters, Teff=3660K, log(g)=1.0, [M/H]=0.0. Differential analysis of the atmospheric structures is performed using the 3D hydrodynamical and 1D classical atmosphere models calculated with the CO5BOLD and LHD codes, respectively. All models share identical atmospheric parameters, elemental composition, opacities and equation-of-state. We find that the atmosphere of this particular red giant consists of two rather distinct regions: the lower atmosphere dominated by convective motions and the upper atmosphere dominated by wave activity. Convective motions form a prominent granulation pattern with an intensity contrast (~18%) which is larger than in the solar models (~15%). The upper atmosphere is frequently traversed by fast shock waves, with vertical and horizontal velocities of up to Mach ~2.5 and ~6.0, respectively. The typical diameter of the granules amounts to ~5Gm which translates into ~400 granules covering the whole stellar surface. The turbulent pressure in the giant model contributes up to ~35% to the total (i.e., gas plus turbulent) pressure which shows that it cannot be neglected in stellar atmosphere and evolutionary modeling. However, there exists no combination of the mixing-length parameter and turbulent pressure that would allow to satisfactorily reproduce the 3D temperature-pressure profile with 1D atmosphere models based on a standard formulation of mixing-length theory.
BASALISC: Programmable Asynchronous Hardware Accelerator for BGV Fully Homomorphic Encryption

—Fully Homomorphic Encryption (FHE) allows for secure computation on encrypted data. Unfortunately, huge memory size, computational cost and bandwidth requirements limit its practically. We present BASALISC, an architecture family of hardware accelerators that aims to substantially accelerate FHE computations in the cloud. BASALISC is the first to implement the BGV scheme supporting fully-packed bootstrapping – the noise removal capability necessary to support arbitrary-depth computation. We propose a generalized version of bootstrapping that can be implemented directly in our hardware, instantiated with Montgomery multipliers that save 46% in silicon area and 40% in power
A Consistency Constraint-Based Approach to Coupled State Constraints in Distributed Model Predictive Control

In this paper, we present a distributed model predictive control (DMPC) scheme for dynamically decoupled systems which are subject to state constraints, coupling state constraints and input constraints. In the proposed control scheme, neighbor-to-neighbor communication suffices and all subsystems solve their local optimization problem in parallel. The approach relies on consistency constraints which define a neighborhood around each subsystem’s reference trajectory where the state of the respective subsystem is guaranteed to stay in. Reference trajectories and consistency constraints are known to neighboring subsystems. Contrary to other relevant approaches, the reference trajectories are improved iteratively. Besides, the presented approach allows the formulation of convex optimization problems even in the presence of non-convex state constraints. The algorithm’s effectiveness is demonstrated with a simulation.
Non-distributive positive logic as a fragment of first-order logic over semilattices

We characterise non-distributive positive logic as the fragment of a single-sorted first-order language that is preserved by a new notion of simulation called a meet-simulation. Meet-simulations distinguish themselves from simulations because they relate pairs of states from one model to single states from another. En route to this result we use a more traditional notion of simulations and prove a Hennessy-Milner style theorem for it, using an analogue of modal saturation called meet-compactness.
Microwave spectroscopy of interacting Andreev spins

Andreev bound states are fermionic states localized in weak links between superconductors which can be occupied with spinful quasiparticles. Microwave experiments using superconducting circuits with InAs/Al nanowire Josephson junctions have recently enabled probing and coherent manipulation of Andreev states but have remained limited to zero or small fields. Here we use a flux-tunable superconducting circuit in external magnetic fields up to 1T to perform spectroscopy of spin-polarized Andreev states up to ~250 mT, beyond which the spectrum becomes gapless. We identify singlet and triplet states of two quasiparticles occupying different Andreev states through their dispersion in magnetic field. These states are split by exchange interaction and couple via spin-orbit coupling, analogously to two-electron states in quantum dots. We also show that the magnetic field allows to drive a direct spin-flip transition of a single quasiparticle trapped in the junction. Finally, we measure a gate- and field-dependent anomalous phase shift of the Andreev spectrum, of magnitude up to approximately $0.7\pi$. Our observations demonstrate new ways to manipulate Andreev states in a magnetic field and reveal spin-polarized triplet states that carry supercurrent.
ICANet: A Method of Short Video Emotion Recognition Driven by Multimodal Data

With the fast development of artificial intelligence and short videos, emotion recognition has become one of the most important research topics in human-computer interaction. At present, most emotion recognition methods still stay in a single modality. However, in daily life, human beings will usually disguise their real emotions, which leads to the problem that the low accuracy of single modal emotion recognition. Moreover, it is not easy to distinguish similar emotions. Therefore, we propose a new approach denoted as ICANet to achieve multimodal short video emotion recognition by employing three different modalities of audio, video, and optical flow, making up for the lack of a single modality and then improving the accuracy of emotion recognition in short videos. ICANet has a better accuracy of 80.77% on the IEMOCAP benchmark. The cross-modal fusion method of short video emotion recognition established in this paper can effectively improve the accuracy of emotion recognition in human-computer interaction scenarios.
Multidifferential study of identified charged hadron distributions in 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>Z</mml:mi></mml:math>
-tagged jets in proton-proton collisions at 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:m

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $<p_{\textrm{T}}<100$ GeV and in the pseudorapidity range $2.5<\eta<4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.
Sliding Window Recurrent Network for Efficient Video Super-Resolution

Video super-resolution (VSR) is the task of restoring high-resolution frames from a sequence of low-resolution inputs. Different from single image super-resolution, VSR can utilize frames' temporal information to reconstruct results with more details. Recently, with the rapid development of convolution neural networks (CNN), the VSR task has drawn increasing attention and many CNN-based methods have achieved remarkable results. However, only a few VSR approaches can be applied to real-world mobile devices due to the computational resources and runtime limitations. In this paper, we propose a \textit{Sliding Window based Recurrent Network} (SWRN) which can be real-time inference while still achieving superior performance. Specifically, we notice that video frames should have both spatial and temporal relations that can help to recover details, and the key point is how to extract and aggregate information. Address it, we input three neighboring frames and utilize a hidden state to recurrently store and update the important temporal information. Our experiment on REDS dataset shows that the proposed method can be well adapted to mobile devices and produce visually pleasant results.
Maintaining Optimality in Assignment Problem against Weight Updates around Vertices

We consider a dynamic situation in the weighted bipartite matching problem in which edge weights are repeatedly updated and each update occurs around a single vertex. Our objective is to maintain an optimal matching at any moment. A trivial approach is to compute an optimal matching from scratch each time an update occurs, but this seems ineﬃcient. In this paper, we show that, if we simultaneously maintain a dual solution, then it suﬃces to perform Dijkstra’s algorithm only once per update. As an application of our result, we provide a faster implementation of the envy-cycle procedure for ﬁnding an envy-free allocation of indivisible goods. Our algorithm runs in O( mn 2 ) time, while the known bound of the original one is O( mn 3 ), where n and m denote the numbers of agents and items, respectively.
Self-Supervised Exploration via Temporal Inconsistency in Reinforcement Learning

Under sparse extrinsic reward settings, reinforcement learning has remained challenging, despite surging interests in this field. Previous attempts suggest that intrinsic reward can alleviate the issue caused by sparsity. In this article, we present a novel intrinsic reward that is inspired by human learning, as humans evaluate curiosity by comparing current observations with historical knowledge. Our method involves training a self-supervised prediction model, saving snapshots of the model parameters, and using nuclear norm to evaluate the temporal inconsistency between the predictions of different snapshots as intrinsic rewards. We also propose a variational weighting mechanism to assign weight to different snapshots in an adaptive manner. Our experimental results on various benchmark environments demonstrate the efficacy of our method, which outperforms other intrinsic reward-based methods without additional training costs and with higher noise tolerance. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.
Boundary time crystals engineered by long-range dissipation

Dissipative time crystals can appear in spin systems, when the 𝑍 2 symmetry of the Hamiltonian is broken by the environment, and the square of total spin operator 𝑆 2 is conserved. In this manuscript, we relax the latter condition and show that time-translation-symmetry breaking collective oscillations persist, in the thermodynamic limit, even in the absence of spin symmetry. We engineer an ad hoc Lindbladian using power-law decaying spin operators and show that time-translation symmetry breaking appears when the decay exponent obeys 0 < 𝜂 ≤ 1. This model shows a surprisingly rich phase diagram, including the time-crystal phase as well as ﬁrst-order, second-order, and continuous transitions of the ﬁxed points. We study the phase diagram and the magnetization dynamics in the mean-ﬁeld approximation, which we prove to be exact, in the thermodynamic limit, as the system does not develop sizable quantum ﬂuctuations, up to the third order cumulant expansion.
A Scalable and Energy E ﬃ cient GPU Thread Map for Standard m -Simplex Domains

This work proposes a new GPU thread map for standard m -simplex domains, that scales its speedup with dimension and is energy e ﬃ cient compared to other state of the art approaches. The main contributions of this work are the formulation of the new block-space map H : Z m (cid:55)→ Z m which is analyzed in terms of resource usage, and its experimental evaluation in terms of speedup over a bounding box approach and energy e ﬃ ciency as elements per second per Watt. Results from the analysis show that H has a potential speedup of up to 2 × and 6 × for 2 and 3-simplices, respectively. Experimental evaluation shows that H is competitive for 2-simplices, reaching 1 . 2 × ∼ 2 . 0 × of speedup for di ﬀ erent tests, which is on par with the fastest state of the art approaches. For 3-simplices H reaches up to 1 . 3 × ∼ 6 . 0 × of speedup making it the fastest of all. The extension of H to higher dimensional m -simplices is feasible and has a potential speedup that scales as m ! given a proper selection of parameters r , β which are the scaling and replication factors, respectively. In terms of energy consumption, although H is among the highest in power consumption, it compensates by its short duration, making it one of the most energy e ﬃ cient approaches. Lastly, further improvements with Tensor and Ray Tracing Cores are analyzed, giving insights to leverage each one of them. The results obtained in this work show that H is a scalable and energy e ﬃ cient map that can contribute to the e ﬃ ciency of GPU applications when they need to process standard m -simplex domains, such as Cellular Automata or PDE simulations.
Multi-AI Complex Systems in Humanitarian Response

AI is being increasingly used to aid response efforts to humanitarian emergencies at multiple levels of decision-making. Such AI systems are generally understood to be stand-alone tools for decision support, with ethical assessments, guidelines and frameworks applied to them through this lens. However, as the prevalence of AI increases in this domain, such systems will begin to encounter each other through information flow networks created by interacting decision-making entities, leading to multi-AI complex systems which are often ill understood. In this paper we describe how these multi-AI systems can arise, even in relatively simple real-world humanitarian response scenarios, and lead to potentially emergent and erratic erroneous behavior. We discuss how we can better work towards more trustworthy multi-AI systems by exploring some of the associated challenges and opportunities, and how we can design better mechanisms to understand and assess such systems. This paper is designed to be a first exposition on this topic in the field of humanitarian response, raising awareness, exploring the possible landscape of this domain, and providing a starting point for future work within the wider community.
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis

Recently, some span-based methods have achieved encouraging performances for joint aspect-sentiment analysis, which first extract aspects (aspect extraction) by detecting aspect boundaries and then classify the span-level sentiments (sentiment classification). However, most existing approaches either sequentially extract task-specific features, leading to insufficient feature interactions, or they encode aspect features and sentiment features in a parallel manner, implying that feature representation in each task is largely independent of each other except for input sharing. Both of them ignore the internal correlations between the aspect extraction and sentiment classification. To solve this problem, we novelly propose a hierarchical interactive network (HI-ASA) to model two-way interactions between two tasks appropriately, where the hierarchical interactions involve two steps: shallow-level interaction and deep-level interaction. First, we utilize cross-stitch mechanism to combine the different task-specific features selectively as the input to ensure proper two-way interactions. Second, the mutual information technique is applied to mutually constrain learning between two tasks in the output layer, thus the aspect input and the sentiment input are capable of encoding features of the other task via backpropagation. Extensive experiments on three real-world datasets demonstrate HI-ASA’s superiority over baselines.
Coherence in the Two Kondo Impurity Problem

We show through a perturbative and an exact calculation using Wilson's renormalization methods that in the problem of two interacting Kondo impurities, on-site potential scattering generates a quantum-tunneling between the two impurities through a marginally relevant operator. The magnitude of this tunneling $V_{12}$ depends on the spin-correlation between the two impurities. For exchange interactions between the moments comparable to the Kondo energy $T_K$, $V_{12}$ is typically much larger than $T_K$. This implies that the heavy-fermion mass in this interesting range is determined by $V_{12}$ rather than $T_K$. The importance of these results for experiments in coupled Kondo dots is also pointed out.
Abundant ammonium hydrosulphide embedded in cometary dust grains

Ammonium hydrosulphide has long since been postulated to exist at least in certain layers of the giant planets. Its radiation products may be the reason for the red colour seen on Jupiter. Several ammonium salts, the products of NH3 and an acid, have previously been detected at comet 67P/Churyumov-Gerasimenko. The acid H2S is the fifth most abundant molecule in the coma of 67P followed by NH3. In order to look for the salt NH4+SH−, we analysed in situ measurements from the Rosetta/ROSINA Double Focusing Mass Spectrometer during the Rosetta mission. NH3 and H2S appear to be independent of each other when sublimating directly from the nucleus. However, we observe a strong correlation between the two species during dust impacts, clearly pointing to the salt. We find that NH4+SH− is by far the most abundant salt, more abundant in the dust impacts than even water. We also find all previously detected ammonium salts and for the first time ammonium fluoride. The amount of ammonia and acids balance each other, confirming that ammonia is mostly in the form of salt embedded into dust grains. Allotropes S2 and S3 are strongly enhanced in the impacts, while H2S2 and its fragment HS2 are not detected, which is most probably the result of radiolysis of NH4+SH−. This makes a prestellar origin of the salt likely. Our findings may explain the apparent depletion of nitrogen in comets and maybe help to solve the riddle of the missing sulphur in star forming regions.
Your Blockchain Needn't Care How the Message is Spread

In a blockchain system, nodes regularly distribute data to other nodes. The ideal perspective taken in the scientiﬁc literature is that data is broadcast to all nodes directly, while in practice data is distributed by repeated multicast. Since correctness and security typically have been established for the ideal setting only, it is vital to show that these properties carry over to real-world implementations. This can be done by proving that the ideal and the real behavior are equivalent. In the work described in this paper, we take an important step towards such a proof by proving a simpler variant of the above equivalence statement. The simpliﬁcation is that we consider only a concrete pair of network topologies, which nevertheless illustrates important phenomena encountered with arbitrary topologies. For describing systems that distribute data, we use a domain-speciﬁc language of processes that is embedded in a general-purpose process calculus. This allows us to leverage the rich theory of process calculi in our proof, which is machine-checked using the Isabelle proof assistant.
Jump teste to evaluate the volleyball player

The objective of the review was to explain the most important jump tests for the volleyball player. The literature informed that the strength has two groups: active and reactive. The active strength is composed by explosive strength. The reactive strength is composed by elastic explosive strength and reflex elastic explosive strength. The jump test for evaluate the explosive strength is with the squat jump, but for evaluate the elastic explosive strength is with the countermovement jump, the test for evaluate the reflex elastic explosive strength is with the Bosco test. However, some authors consider best the intermittent tests with several jumps. But recently, an author recommended the uses the Kinovea ® software to determine the jump and the reach of the athlete during the match for measures the elastic explosive strength and/or the reflex elastic explosive strength. In conclusion, the article determined the best jump test for the volleyball player. RESUMO Teste de salto para avaliar o jogador de voleibol O objetivo da revisao foi de explicar os testes de salto mais importantes para o jogador de voleibol. A literatura informou que a forca tem dois grupos: ativa e reativa. A forca ativa e composta pela forca explosiva. A forca reativa e composta pela forca explosiva elastica e pela forca explosiva elastica reflexa. O teste de salto para avaliar a forca explosiva e com o squat jump, mas para avaliar a forca explosiva elastica e com o countermovement jump, o teste para avaliar a forca explosiva elastica reflexa e com o teste de Bosco. Entretanto, alguns autores consideram melhor o teste intermitente com varios saltos. Mas recentemente, um autor recomendou usar o software Kinovea ® para determinar o salto e o alcance do atleta durante o jogo para mensurar a forca explosiva elastica e/ou a forca explosiva elastica reflexa. Em conclusao, o artigo determinou o melhor teste de salto para o jogador de voleibol.
Properties of shocked dust grains in supernova remnants

Shockwaves driven by supernovae both destroy dust and reprocess the surviving grains, greatly affecting the resulting dust properties of the interstellar medium (ISM). While these processes have been extensively studied theoretically, observational constraints are limited. We use physically-motivated models of dust emission to fit the infrared (IR) spectral energy distributions of seven Galactic supernova remnants, allowing us to determine the distribution of dust mass between diffuse and dense gas phases, and between large and small grain sizes. We find that the dense ($\sim 10^3 \,{\rm cm}^{-3}$), relatively cool ($\sim 10^3 \, {\rm K}$) gas phase contains $>90\%$ of the dust mass, making the warm dust located in the X-ray emitting plasma ($\sim 1 \,{\rm cm}^{-3}$/$10^6 \, {\rm K}$) a negligible fraction of the total, despite dominating the mid-IR emission. The ratio of small ($\lesssim 10 \, {\rm nm}$) to large ($\gtrsim 0.1 \, {\rm \mu m}$) grains in the cold component is consistent with that in the ISM, and possibly even higher, whereas the hot phase is almost entirely devoid of small grains. This suggests that grain shattering, which processes large grains into smaller ones, is ineffective in the low-density gas, contrary to model predictions. Single-phase models of dust destruction in the ISM, which do not account for the existence of the cold swept-up material containing most of the dust mass, are likely to greatly overestimate the rate of dust destruction by supernovae.