Miracle-dz/UniFAR-FTUs huggingface dataset:
The smallest fluid on Earth

High energy heavy ion collisions create quark gluon plasmas that behave like almost perfect fluids. Very similar features to those that led to this insight have also been observed in experimental data from collisions of small systems, involving protons or other light nuclei. We describe recent developments aimed at understanding whether, and if so how, systems that produce relatively few particles (orders of magnitude less than in typical heavy ion collisions) and are only one to a few times the size of a proton, can behave like fluids. This involves a deeper understanding of fluid dynamics and its applicability, improvements of our understanding of the initial geometry of the collisions by considering fluctuations of the proton shape, as well as advancements in the calculation of initial state effects within an effective theory of quantum chromodynamics, which can affect the observables that are used to study fluid behavior. We further address open questions and discuss future directions.
A POTENTIAL AND NOVEL HIGH k DIELECTRICS SUITABLE FOR DRAM

ABSTRACT The dielectric and ferroelectric properties of a novel high-k ‘Y5V’ (Ba1 − xLax) (Ti1 − x/4 −yCey)O3 ceramics (x = 0.03, y = 0.05−BL3TC5) with cubic symmetry and fine-grained (∼ 0.9 μ m) microstructure were investigated in detail. BL3TC5 shows strong diffuse phase transition and high k behavior (ϵ ′RT > 10000) from 1 to 100 k Hz in the vicinity of room temperature, and meets EIA specifications ‘Y5V’ and ‘Z5U’ at 1 k Hz when the dielectric relaxation effect is considered. The paraelectricity and high-k behavior around room temperature suggest that BL3TC5 is a promising candidate for future DRAM application.
Supermartingales, Ranking Functions and Probabilistic Lambda Calculus

We introduce a method for proving almost sure termination in the context of lambda calculus with continuous random sampling and explicit recursion, based on ranking supermartingales. This result is extended in three ways. Antitone ranking functions have weaker restrictions on how fast they must decrease, and are applicable to a wider range of programs. Sparse ranking functions take values only at a subset of the program’s reachable states, so they are simpler to define and more flexible. Ranking functions with respect to alternative reduction strategies give yet more flexibility, and significantly increase the applicability of the ranking supermartingale approach to proving almost sure termination, thanks to a novel (restricted) confluence result which is of independent interest. The notion of antitone ranking function was inspired by similar work by McIver, Morgan, Kaminski and Katoen in the setting of a first-order imperative language, but adapted to a higher-order functional language. The sparse ranking function and confluent semantics extensions are unique to the higher-order setting. Our methods can be used to prove almost sure termination of programs that are beyond the reach of methods in the literature, including higher-order and non-affine recursion.
Channel Estimation and Data Decoding Analysis of Massive MIMO with 1-Bit ADCs

We present an analytical framework for the channel estimation and the data decoding in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs). First, we provide a closed-form expression of the mean squared error of the channel estimation for a general class of linear estimators. In addition, we propose a novel linear estimator with signiﬁcantly enhanced performance compared with existing estimators with the same structure. For the data decoding, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efﬁciently implement maximum likelihood decoding and, potentially, to design the set of transmit symbols. Comprehensive numerical results are presented to study the performance of the channel estimation and the data decoding with 1-bit ADCs with respect to the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right noise level signiﬁcantly enhances the system performance.
First-principles study of the role of surface in the heavy-fermion compound 
CeRh2Si2

In the heavy fermion materials, the characteristic energy scales of many exotic strongly correlated phenomena (Kondo eﬀect, magnetic order, superconductivity, etc.) are at milli-electron-volt order, implying that the heavy fermion materials are surface sensitive. Here, we investigate the electronic structures for Si- and Ce-terminated surfaces of CeRh 2 Si 2 by ﬁrst-principles methods. Our research reveals three notable impacts of surface eﬀects on electronic structures, which are consistent with recent angle-resolved photoemission spectroscopy (ARPES) experiments. Firstly, the relaxation of surface crystal structures changes the relative position of Fermi level, adjusts the dispersion of bands and enhances the Kondo resonance. Secondly, the decrease of the hybridization between the Ce-4 f and conduction electrons in the surface layer leads to a weaker Kondo resonance peak and the shift of spin-orbit bands. Thirdly, the variation of crystal electric ﬁeld around surface Ce atoms aﬀects the splitting of Kondo resonance peaks, and also pushes down the lower-Hubbard bands of surface 4 f electrons. Moreover, we ﬁnd the characteristic of bulk’s lower-Hubbard bands, which was overlooked in previous works. Our investigation suggests that these surface eﬀects are potentially important and highlighted in the future researches on properties of strongly correlated materials. Our supplemental information is organized as follows. Sec.I shows the convergence tests results. Sec.II gives the detailed results of surface crystal structure relaxation and the discussion on the performance of diﬀerence methods. Sec.III shows our prediction results of band structure on other k-path. Sec.IV displays the band structure with crystal eﬀect. Sec.V shows the variation of Kondo resonance peaks with surface relaxation.
Equilibrium Price Formation with a Major Player and its Mean Field Limit

In this article, we consider the problem of equilibrium price formation in an incomplete securities market consisting of one major financial firm and a large number of minor firms. They carry out continuous trading via the securities exchange to minimize their cost while facing idiosyncratic and common noises as well as stochastic order flows from their individual clients. The equilibrium price process that balances demand and supply of the securities, including the functional form of the price impact for the major firm, is derived endogenouslyboth in the market of finite population size and in the corresponding mean field limit.
Linear Transformers Are Secretly Fast Weight Memory Systems

We show the formal equivalence of linearised self-attention mechanisms and fast weight memories from the early ’90s. From this observation we infer a memory capacity limitation of recent lin-earised softmax attention variants. With ﬁnite memory, a desirable behaviour of fast weight memory models is to manipulate the contents of memory and dynamically interact with it. Inspired by previous work on fast weights, we propose to replace the update rule with an alternative rule yielding such behaviour. We also propose a new kernel function to linearise attention, balancing simplicity and effectiveness. We conduct experiments on synthetic retrieval problems as well as standard machine translation and language modelling tasks which demonstrate the beneﬁts of our methods.
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning

Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a meta-initialization} of model parameters (that we call meta-model) to rapidly adapt to new tasks using a small amount of labeled training data. Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning. In addition to generalization, robustness is also desired for a meta-model to defend adversarial examples (attacks). Toward promoting adversarial robustness in MAML, we first study WHEN a robustness-promoting regularization should be incorporated, given the fact that MAML adopts a bi-level (fine-tuning vs. meta-update) learning procedure. We show that robustifying the meta-update stage is sufficient to make robustness adapted to the task-specific fine-tuning stage even if the latter uses a standard training protocol. We also make additional justification on the acquired robustness adaptation by peering into the interpretability of neurons' activation maps. Furthermore, we investigate HOW robust regularization can efficiently be designed in MAML. We propose a general but easily-optimized robustness-regularized meta-learning framework, which allows the use of unlabeled data augmentation, fast adversarial attack generation, and computationally-light fine-tuning. In particular, we for the first time show that the auxiliary contrastive learning task can enhance the adversarial robustness of MAML. Finally, extensive experiments are conducted to demonstrate the effectiveness of our proposed methods in robust few-shot learning.
Decompositions of matrices into potent and square-zero matrices

In order to find a suitable expression of an arbitrary square matrix over an arbitrary finite commutative ring, we prove that every such matrix is always representable as a sum of a potent matrix and a nilpotent matrix of order at most two when the Jacobson radical of the ring has zero-square. This somewhat extends results of ours in Linear Multilinear Algebra (2022) established for matrices considered on arbitrary fields. Our main theorem also improves on recent results due to Abyzov et al. in Mat. Zametki (2017), Šter in Linear Algebra Appl. (2018) and Shitov in Indag. Math. (2019).
Bayesian Cross-matching of High Proper-motion Stars in Gaia DR2 and Photometric Metallicities for ∼1.7 million K and M Dwarfs

We present a Bayesian method to cross-match 5,827,988 high proper-motion Gaia sources (μ > 40 mas yr−1) to various photometric surveys: Two Micron All Sky Survey, AllWISE data release from the Wide-field Infrared Explorer (WISE) mission, Galaxy Evolution Explorer, Radial Velocity Experiment, Sloan Digital Sky Survey, and Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). To efficiently associate these objects across catalogs, we develop a technique that compares the multidimensional distribution of all sources in the vicinity of each Gaia star to a reference distribution of random field stars obtained by extracting all sources in a region on the sky displaced 2′. This offset preserves the local field stellar density and magnitude distribution, allowing us to characterize the frequency of chance alignments. The resulting catalog with Bayesian probabilities >95% has a marginally higher match rate than current internal Gaia data release 2 (DR2) matches for most catalogs. However, a significant improvement is found with Pan-STARRS, where ∼99.8% of the sample within the Pan-STARRS footprint is recovered, as compared to a low ∼20.8% in Gaia DR2. Using these results, we train a Gaussian process regressor to calibrate two photometric metallicity relationships. For dwarfs of 3500 < T eff < 5280 K, we use metallicity values of 4378 stars from the Apache Point Observatory Galactic Evolution Experiment and Hejazi et al. to calibrate the relationship, producing results with a 1σ precision of 0.12 dex and few systematic errors. We then indirectly infer the metallicity of 4018 stars with 2850 < T eff < 3500 K, which are wide companions of primaries whose metallicities are estimated with our first regressor, to produce a relationship with a 1σ precision of 0.21 dex and significant systematic errors. Additional work is needed to better remove unresolved binaries from this second sample to reduce these systematic errors.
Corrosion of stellite-6 in lithiated and borated high-temperature water

Etude de la corrosion de l'alliage de cobalt stellite-6 dans l'eau boratee et lithiatee a 300°C avec une surpression d'hydrogene. Ces conditions simulent le regime chimique bien defini dans les circuits primaires d'un reacteur a eau pressurisee. Les produits obtenues sont caracterisees a l'aide de differentes techniques d'analyse de surface et de microscopie electronique. La composition chimique des films d'oxyde est obtenue par spectrometrie XPS
LVCNet: Efficient Condition-Dependent Modeling Network for Waveform Generation

In this paper, we propose a novel conditional convolution network, named location-variable convolution, to model the dependencies of the waveform sequence. Different from the use of unified convolution kernels in WaveNet to capture the dependencies of arbitrary waveform, the location-variable convolution uses convolution kernels with different coefficients to perform convolution operations on different waveform intervals, where the coefficients of kernels is predicted according to conditioning acoustic features, such as Mel-spectrograms. Based on location-variable convolutions, we design LVCNet for waveform generation, and apply it in Parallel WaveGAN to design more efficient vocoder. Experiments on the LJSpeech dataset show that our proposed model achieves a four-fold increase in synthesis speed compared to the original Parallel WaveGAN without any degradation in sound quality, which verifies the effectiveness of location-variable convolutions.
Regression Filtration With Resetting to Provide Exponential Convergence of MRAC for Plants With Jump Change of Unknown Parameters

This article proposes a new method to provide the exponential convergence of both the parameter and tracking errors of the composite adaptive control system without the persistent excitation (PE) requirement. Instead, the derived composite adaptive law ensures the abovementioned properties under the strictly weaker finite excitation (FE) condition. Unlike known solutions, in addition to the PE requirement relaxation, it provides better transient response under jump change of the plant uncertainty parameters. To derive such an adaptive law, a novel scheme of uncertainty filtration with resetting is proposed, which provides the required properties of the control system. A rigorous proof of all mentioned properties of the developed adaptive law is presented. Such a law is compared with the known composite ones, which also relax the PE requirement, using the wing-rock problem to conduct numerical experiments. The obtained results fully support the theoretical analysis.
T
-operator limits on optical communication: Metaoptics, computation, and input-output transformations

We present an optimization framework based on Lagrange duality and the scattering $\mathbb{T}$ operator of electromagnetism to construct limits on the possible features that may be imparted to a collection of output fields from a collection of input fields, i.e., constraints on achievable optical transformations and the characteristics of structured materials as communication channels. Implications of these bounds on the performance of representative optical devices having multi-wavelength or multiport functionalities are examined in the context of electromagnetic shielding, focusing, near-field resolution, and linear computing.
Targeted Attack against Deep Neural Networks via Flipping Limited Weight Bits

To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have been well studied, such as the poisoning-based backdoor attack in the training stage and the adversarial attack in the inference stage. In this paper, we study a novel attack paradigm, which modifies model parameters in the deployment stage for malicious purposes. Specifically, our goal is to misclassify a specific sample into a target class without any sample modification, while not significantly reduce the prediction accuracy of other samples to ensure the stealthiness. To this end, we formulate this problem as a binary integer programming (BIP), since the parameters are stored as binary bits ($i.e.$, 0 and 1) in the memory. By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method. Consequently, the flipped critical bits can be easily determined through optimization, rather than using a heuristic strategy. Extensive experiments demonstrate the superiority of our method in attacking DNNs.
Trumpets: Injective Flows for Inference and Inverse Problems

We propose injective generative models called Trumpets that generalize invertible normalizing flows. The proposed generators progressively increase dimension from a low-dimensional latent space. We demonstrate that Trumpets can be trained orders of magnitudes faster than standard flows while yielding samples of comparable or better quality. They retain many of the advantages of the standard flows such as training based on maximum likelihood and a fast, exact inverse of the generator. Since Trumpets are injective and have fast inverses, they can be effectively used for downstream Bayesian inference. To wit, we use Trumpet priors for maximum a posteriori estimation in the context of image reconstruction from compressive measurements, outperforming competitive baselines in terms of reconstruction quality and speed. We then propose an efficient method for posterior characterization and uncertainty quantification with Trumpets by taking advantage of the low-dimensional latent space.
Novel pH-sensitive polymers containing sulfonamide groups

Novel pH-sensitive polymers were synthesized by copolymerizing a monomer derivatized from 4-amino-N-[4,6-dimethyl-2-pyrimidinyl]benzene sulfonamide with N,N-dimethylacrylamide. The linear copolymers showed pH-sensitive solubility, while chemically crosslinked hydrogels exhibit a relatively sharp transition in swelling around physiological pH. These changes were found to be reversible. By varying the type of sulfonamide and the copolymer composition, a new class of pH-sensitive polymers with a broad range of transition pH can be synthesized.
Sintering and Electrical Properties of Lead-Free Na0.5K0.5NbO3 Piezoelectric Ceramics

Lead-free Na0.5K0. 5NbO3 (NKN) piezoelectric ceramics were fairly well densified at a relatively low temperature under atmospheric conditions. A relative density of 96%–99% can be achieved by either using high-energy attrition milling or adding 1 mol% oxide additives. It is suggested that ultra-fine starting powders by active milling or oxygen vacancies and even liquid phases from B-site oxide additives mainly lead to improved sintering. Not only were dielectric properties influenced by oxide additives, such as the Curie temperature (Tc) and dielectric loss (D), but also the ferroelectricity was modified. A relatively large remanent polarization was produced, ranging from 16 μC/cm2 for pure NKN to 23 μC/cm2 for ZnO-added NKN samples. The following dielectric and piezoelectric properties were obtained: relative permittivity ɛT33/ɛ0=570–650, planar mode electromechanical coupling factor, kp=32%–44%, and piezoelectric strain constant, d33=92–117 pC/N.
Software Engineering for Internet of Things: The Practitioners' Perspective

Internet of Things based systems (IoT systems for short) are becoming increasingly popular across different industrial domains and their development is rapidly increasing to provide value-added services to end-users and citizens. Little research to date uncovers the core development process lifecycle needed for IoT systems, and thus software engineers find themselves unprepared and unfamiliar with this new genre of system development. To ameliorate this gap, we conducted a mixed quantitative and qualitative research study where we derived a conceptual process framework from the extant literature on IoT, through which 27 key tasks for incorporation into the development processes of IoT systems were identified. The framework was then validated by the means of a survey of 127 IoT practitioners from 35 countries across 6 continents with 15 different industry backgrounds. Our research provides an understanding of the most important development process tasks and informs both software engineering practitioners and researchers of the challenges and recommendations related to the development of next-generation of IoT systems.
Anchor-Assisted Channel Estimation for Intelligent Reflecting Surface Aided Multiuser Communication

Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle this challenge, we propose in this paper a new anchor-assisted channel estimation approach, where two anchor nodes, namely A1 and A2, are deployed near the IRS for facilitating its aided base station (BS) in acquiring the cascaded BS-IRS-user channels required for data transmission. Specifically, in the first scheme, the partial channel state information (CSI) on the element-wise channel gain square of the common BS-IRS link for all users is first obtained at the BS via the anchor-assisted training and feedback. Then, by leveraging such partial CSI, the cascaded BS-IRS-user channels are efficiently resolved at the BS with additional training by the users. While in the second scheme, the BS-IRS-A1 and A1-IRS-A2 channels are first estimated via the training by A1. Then, with additional training by A2, all users estimate their individual cascaded A2-IRS-user channels simultaneously. Based on the CSI fed back from A2 and all users, the BS resolves the cascaded BS-IRS-user channels efficiently. In both schemes, the channels among the fixed BS, IRS, and two anchors are estimated in a large timescale, which greatly reduces the real-time training overhead. Simulation results demonstrate that our proposed anchor-assisted channel estimation schemes achieve superior performance as compared to existing IRS channel estimation schemes, under various practical setups. In addition, the first proposed scheme outperforms the second one when the number of antennas at the BS is sufficiently large, and vice versa.
Model Order Reduction for Water Quality Dynamics

A state‐space representation of water quality (WQ) dynamics describing disinfectant (e.g., chlorine) transport dynamics in drinking water distribution networks has been recently proposed. Such representation is a byproduct of space‐ and time‐discretization of the partial differential equations modeling transport dynamics. This results in a large state‐space dimension even for small networks with tens of nodes. Although such a state‐space model provides a model‐driven approach to predict WQ dynamics, incorporating it into model‐based control algorithms or state estimators for large networks is challenging and at times intractable. To that end, this paper investigates model order reduction (MOR) methods for WQ dynamics with the objective of performing post‐reduction feedback control. The presented investigation focuses on reducing state‐dimension by orders of magnitude, the stability of the MOR methods, and the application of these methods to model predictive control.