Miracle-dz/UniFAR-FTUs huggingface dataset:
All-or-Nothing Phenomena: From Single-Letter to High Dimensions

We consider the problem of estimating a <tex>$p$</tex> -dimensional vector <tex>$\beta$</tex> from <tex>$n$</tex> observations <tex>$Y=X\beta+W$</tex>, where <tex>$\beta_{j}\mathop{\sim}^{\mathrm{i.i. d}. }\pi$</tex> for a real-valued distribution <tex>$\pi$</tex> with zero mean and unit variance’ <tex>$X_{ij}\mathop{\sim}^{\mathrm{i.i. d}. }\mathcal{N}(0,1)$</tex>, and <tex>$W_{i}\mathop{\sim}^{\mathrm{i.i. d}. }\mathcal{N}(0,\ \sigma^{2})$</tex>. In the asymptotic regime where <tex>$n/p\rightarrow\delta$</tex> and <tex>$p/\sigma^{2}\rightarrow$</tex> snr for two fixed constants <tex>$\delta,\ \mathsf{snr}\in(0,\ \infty)$</tex> as <tex>$p\rightarrow\infty$</tex>, the limiting (normalized) minimum mean-squared error (MMSE) has been characterized by a single-letter (additive Gaussian scalar) channel. In this paper, we show that if the MMSE function of the single-letter channel converges to a step function, then the limiting MMSE of estimating <tex>$\beta$</tex> converges to a step function which jumps from 1 to 0 at a critical threshold. Moreover, we establish that the limiting mean-squared error of the (MSE-optimal) approximate message passing algorithm also converges to a step function with a larger threshold, providing evidence for the presence of a computational-statistical gap between the two thresholds.
The Limits of Efficiency for Open- and Closed-World Query Evaluation Under Guarded TGDs

Ontology-mediated querying and querying in the presence of constraints are two key database problems where tuple-generating dependencies (TGDs) play a central role. In ontology-mediated querying, TGDs can formalize the ontology and thus derive additional facts from the given data, while in querying in the presence of constraints, they restrict the set of admissible databases. In this work, we study the limits of efficient query evaluation in the context of the above two problems, focusing on guarded and frontier-guarded TGDs and on UCQs as the actual queries. We show that a class of ontology-mediated queries (OMQs) based on guarded TGDs can be evaluated in FPT iff the OMQs in the class are equivalent to OMQs in which the actual query has bounded treewidth, up to some reasonable assumptions. For querying in the presence of constraints, we consider classes of constraint-query specifications (CQSs) that bundle a set of constraints with an actual query. We show a dichotomy result for CQSs based on guarded TGDs that parallels the one for OMQs except that, additionally, FPT coincides with PTime combined complexity. The proof is based on a novel connection between OMQ and CQS evaluation. Using a direct proof, we also show a similar dichotomy result, again up to some reasonable assumptions, for CQSs based on frontier-guarded TGDs with a bounded number of atoms in TGD heads. Our results on CQSs can be viewed as extensions of Grohe's well-known characterization of the tractable classes of CQs (without constraints). Like Grohe's characterization, all the above results assume that the arity of relation symbols is bounded by a constant. We also study the associated meta problems, i.e., whether a given OMQ or CQS is equivalent to one in which the actual query has bounded treewidth.
Animals in Virtual Environments

The core idea in an XR (VR/MR/AR) application is to digitally stimulate one or more sensory systems (e.g. visual, auditory, olfactory) of the human user in an interactive way to achieve an immersive experience. Since the early 2000s biologists have been using Virtual Environments (VE) to investigate the mechanisms of behavior in non-human animals including insects, fish, and mammals. VEs have become reliable tools for studying vision, cognition, and sensory-motor control in animals. In turn, the knowledge gained from studying such behaviors can be harnessed by researchers designing biologically inspired robots, smart sensors, and rnulti-agent artificial intelligence. VE for animals is becoming a widely used application of XR technology but such applications have not previously been reported in the technical literature related to XR. Biologists and computer scientists can benefit greatly from deepening interdisciplinary research in this emerging field and together we can develop new methods for conducting fundamental research in behavioral sciences and engineering. To support our argument we present this review which provides an overview of animal behavior experiments conducted in virtual environments.
Supra-Binary Ferroelectricity in a Nanowire

We report the prediction and observation of supra-binary ferroelectricity in a ferroelectric nanowire (FNW) covered with a semi-cylindrical gate that provides an anisotropic electric field in the FNW. There are gate-voltage-driven transitions between four polarization phases in FNW's cross section, dubbed axial-up, axial-down, radial-in and radial-out. They are determined by the interplay between the topological depolarization energy and the free energy induced by an anisotropic external electric field, in clear distinction from the conventional film-based binary ferroelectricity. When the FNW is mounted on a biased graphene nanoribbon (GNR), these transitions induce exotic current-voltage hysteresis in the FNW-GNR transistor. Our discovery suggests new operating mechanisms of ferroelectric devices. In particular, it enables intrinsic multi-bit information manipulation in parallel to the binary manipulation employed in data storage devices.
Crossover times in bipartite networks with activity constraints and time-varying switching rates

In this paper we study the performance of a bipartite network in which customers arrive at the nodes of the network, but not all nodes are able to serve their customers at all times. Each node can be either active or inactive, and two nodes connected by a bond cannot be active simultaneously. This situation arises in wireless random-access networks where, due to destructive interference, stations that are close to each other cannot use the same frequency band. We consider a model where the network is bipartite, the active nodes switch themselves off at rate 1, and the inactive nodes switch themselves on at a rate that depends on time and on which half of the bipartite network they are in. An inactive node cannot become active when one of the nodes it is connected to by a bond is active. The switching protocol allows the nodes to share activity among each other. In the limit as the activation rate becomes large, we compute the crossover time between the two states where one half of the network is active and the other half is inactive. This allows us to assess the overall activity of the network depending on the switching protocol. Our results make use of the metastability analysis for hard-core interacting particle models on bipartite graphs derived in an earlier paper. They are valid for a large class of bipartite networks, subject to certain assumptions. Proofs rely on a comparison with switching protocols that are not time-varying, through coupling techniques.
Evidence for a Multipolar Magnetic Field in SGR J1745-2900 from X-Ray Light-curve Analysis

SGR J1745-2900 was detected from its outburst activity in 2013 April and it was the first soft gamma repeater (SGR) detected near the center of the Galaxy (Sagittarius A*). We use 3.5 yr Chandra X-ray light-curve data to constrain some neutron star (NS) geometric parameters. We assume that the flux modulation comes from hot spots on the stellar surface. Our model includes the NS mass, radius, a maximum of three spots of any size, temperature and positions, and general relativistic effects. We find that the light curve of SGR J1745-2900 could be described by either two or three hot spots. The ambiguity is due to the small amount of data, but our analysis suggests that one should not disregard the possibility of multi-spots (due to a multipolar magnetic field) in highly magnetized stars. For the case of three hot spots, we find that they should be large and have angular semiapertures ranging from 16° to 67°. The large size found for the spots points to a magnetic field with a nontrivial poloidal and toroidal structure (in accordance with magnetohydrodynamics investigations and Neutron Star Interior Composition Explorer’s (NICER) recent findings for PSR J0030+0451) and is consistent with the small characteristic age of the star. Finally, we also discuss possible constraints on the mass and radius of SGR J1745-2900 and briefly envisage possible scenarios accounting for the 3.5 yr evolution of SGR J1745-290 hot spots.
The Penrose inequality for nonmaximal perturbations of the Schwarzschild initial data

We show that the Penrose inequality is satisfied for generic (in some sense) conformally flat axially symmetric nonmaximal perturbations of the Schwarzschild data. A role of horizon is played by a marginally outer trapped surface which does not have to be minimal. It has a spherical shape with respect to flat metric.
Counter examples to invariant circle packing

In this work, a unimodular random planar triangulation is constructed that has no invariant circle packing. This disputes a problem asked in [arXiv:1910.01614]. A natural weaker problem is the existence of point-stationary circle packings for a graph, which are circle packings that satisfy a certain mass transport principle. It is shown that the answer to this weaker problem is also false. Two examples are provided with two different approaches: Using indistinguishability and finite approximations.
What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge

Abstract Open-domain question answering (QA) involves many knowledge and reasoning challenges, but are successful QA models actually learning such knowledge when trained on benchmark QA tasks? We investigate this via several new diagnostic tasks probing whether multiple-choice QA models know definitions and taxonomic reasoning—two skills widespread in existing benchmarks and fundamental to more complex reasoning. We introduce a methodology for automatically building probe datasets from expert knowledge sources, allowing for systematic control and a comprehensive evaluation. We include ways to carefully control for artifacts that may arise during this process. Our evaluation confirms that transformer-based multiple-choice QA models are already predisposed to recognize certain types of structural linguistic knowledge. However, it also reveals a more nuanced picture: their performance notably degrades even with a slight increase in the number of “hops” in the underlying taxonomic hierarchy, and with more challenging distractor candidates. Further, existing models are far from perfect when assessed at the level of clusters of semantically connected probes, such as all hypernym questions about a single concept.
Peripheral stents: design investigation through computational tools

Cardiovascular disease (CVD) has a huge social impact in Western countries (49% of all deaths in 2005 [1]). CVD is often related to atherosclerosis, a degeneration of the vessel wall, leading to a stenosis, i.e. the narrowing of the vessel lumen, which can have dramatic effects as a stroke or a heart attack. Currently, minimally invasive treatment of stenoses by means of cylindrical metal structures (stents) has become common and widelyused for atherosclerotic coronaries and the trend is to apply this technique for atherosclerotic peripheral vessels such as the Superficial Femoral Artery (SFA) or the Carotid Artery (CA). Thanks to the peculiar features of Nitinol, a shape memory alloy, peripheral stents accomplish both technical and biomechanical requirements (i.e. flexibility, kink resistance, low delivery profile etc.) but, unfortunately, peripheral stenting still suffers several limitations especially due to stent fracture, in-stent restenosis, stent migration and/or malapposition due to the tortuous anatomy. Consequently, there is a significant need to relate the peripheral stenting performance to the features of device design as recently highlighted by several clinical experts [2]. The aim of this study is to investigate the impact of stent design on peripheral stent-
Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression

The paper addresses face presentation attack detection in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not present in the training step. For this purpose, a pure one-class face presentation attack detection approach based on kernel regression is developed which only utilises bona fide (genuine) samples for training. In the context of the proposed approach, a number of innovations, including multiple kernel fusion, client-specific modelling, sparse regularisation and probabilistic modelling of score distributions are introduced to improve the efficacy of the method. The results of experimental evaluations conducted on the OULU-NPU, Replay-Mobile, Replay-Attack and MSU-MFSD datasets illustrate that the proposed method compares very favourably with other methods operating in an unseen attack detection scenario while achieving very competitive performance to multi-class methods (benefiting from presentation attack data for training) despite using only bona fide samples for training.
Dimension Crossing Turbulent Cascade in an Excited Lattice Bose Gas

Turbulence is an intriguing non-equilibrium state, which originates from fluid mechanics and has far-reaching consequences in the description of climate physics, the characterization of quantum hydrodynamics, and the understanding of cosmic evolution. The concept of turbulent cascade describing the energy redistribution across different length scales offers one profound route to reconcile fundamental conservative forces with observational energy non-conservation of accelerating expansion of the universe bypassing the cosmological constant. Here, we observe a dimension crossing turbulent energy cascade in an atomic Bose-Einstein condensate confined in a two-dimensional (2d) optical lattice forming a 2d array of tubes, which exhibits universal behaviors in the dynamical energy-redistribution across different dimensions. By exciting atoms into the optical-lattice high bands, the excessive energy of this quantum many-body system is found to cascade from the transverse two-dimensional lattice directions to the continuous dimension, giving rise to a one-dimensional turbulent energy cascade, which is in general challenging to reach due to integrability. We expect this observed novel phenomenon of dimension-crossing energy cascade may inspire microscopic theories for modeling positive cosmological constant of our inflationary universe.
Learning functionals via LSTM neural networks for predicting vessel dynamics in extreme sea states

Predicting motions of vessels in extreme sea states represents one of the most challenging problems in naval hydrodynamics. It involves computing complex nonlinear wave-body interactions, hence taxing heavily computational resources. Here, we put forward a new simulation paradigm by training recurrent type neural networks (RNNs) that take as input the stochastic wave elevation at a certain sea state and output the main vessel motions, e.g. pitch, heave and roll. We first compare the performance of standard RNNs versus GRU and LSTM neural networks (NNs) and show that LSTM NNs lead to the best performance. We then examine the testing error of two representative vessels, a catamaran in sea state 1 and a battleship in sea state 8. We demonstrate that good accuracy is achieved for both cases in predicting the vessel motions for unseen wave elevations. We train the NNs with expensive CFD simulations offline, but upon training, the prediction of the vessel dynamics online can be obtained at a fraction of a second. This work is motivated by the universal approximation theorem for functionals (Chen & Chen, 1993. IEEE Trans. Neural Netw. 4, 910–918 (doi:10.1109/72.286886)), and it is the first implementation of such theory to realistic engineering problems.
Experimental evidence for orbital magnetic moments generated by moiré-scale current loops in twisted bilayer graphene

One of the most remarkable properties of magic-angle twisted bilayer graphene is that the low-energy bands can have nonzero valley Chern numbers, which enable the realization of the first example of a material with purely orbital-based magnetism. In this work, evidence for orbital magnetic moment generated by the moir\'e-scale current loops circulating in the moir\'e pattern of a twisted bilayer graphene is reported. This paves the way to explore orbital magnetism in slightly twisted graphene systems.
EdgeToll: A Blockchain-based Toll Collection System for Public Sharing of Heterogeneous Edges

Edge computing is a novel paradigm designed to improve the quality of service for latency sensitive cloud applications. However, the state-of-the-art edge services are designed for specific applications, which are isolated from each other. To better improve the utilization level of edge nodes, public resource sharing among edges from distinct service providers should be encouraged economically. In this work, we employ the payment channel techniques to design and implement EdgeToll, a blockchain-based toll collection system for heterogeneous public edge sharing. Test-bed has been developed to validate the proposal and preliminary experiments have been conducted to demonstrate the time and cost efficiency of the system.
Unsupervised Deep Transfer Learning for Intelligent Fault Diagnosis: An Open Source and Comparative Study

Recent progress on intelligent fault diagnosis has greatly depended on the deep learning and plenty of labeled data. However, the machine often operates with various working conditions or the target task has different distributions with the collected data used for training (we called the domain shift problem). This leads to the deep transfer learning based (DTL-based) intelligent fault diagnosis which attempts to remit this domain shift problem. Besides, the newly collected testing data are usually unlabeled, which results in the subclass DTL-based methods called unsupervised deep transfer learning based (UDTL-based) intelligent fault diagnosis. Although it has achieved huge development in the field of fault diagnosis, a standard and open source code framework and a comparative study for UDTL-based intelligent fault diagnosis are not yet established. In this paper, commonly used UDTL-based algorithms in intelligent fault diagnosis are integrated into a unified testing framework and the framework is tested on five datasets. Extensive experiments are performed to provide a systematically comparative analysis and the benchmark accuracy for more comparable and meaningful further studies. To emphasize the importance and reproducibility of UDTL-based intelligent fault diagnosis, the testing framework with source codes will be released to the research community to facilitate future research. Finally, comparative analysis of results also reveals some open and essential issues in DTL for intelligent fault diagnosis which are rarely studied including transferability of features, influence of backbones, negative transfer, and physical priors. In summary, the released framework and comparative study can serve as an extended interface and the benchmark results to carry out new studies on UDTL-based intelligent fault diagnosis. The code framework is available at https://github.com/ZhaoZhibin/UDTL.
How to Pick the Best Source Data? Measuring Transferability for Heterogeneous Domains

Given a set of source data with pre-trained classification models, how can we fast and accurately select the most useful source data to improve the performance of a target task? We address the problem of measuring transferability for heterogeneous domains, where the source and the target data have different feature spaces and distributions. We propose Transmeter, a novel method to efficiently and accurately measure transferability of two datasets. Transmeter utilizes a pre-trained source classifier and a reconstruction loss to increase its efficiency and performance. Furthermore, Transmeter uses feature transformation layers, label-wise discriminators, and a mean distance loss to learn common representations for source and target domains. As a result, Transmeter and its variant give the most accurate performance in measuring transferability, while giving comparable running times compared to those of competitors.
TASE: Reducing latency of symbolic execution with transactional memory

We present the design and implementation of a tool called TASE that uses transactional memory to reduce the latency of symbolic-execution applications with small amounts of symbolic state. Execution paths are executed natively while operating on concrete values, and only when execution encounters symbolic values (or modeled functions) is native execution suspended and interpretation begun. Execution then returns to its native mode when symbolic values are no longer encountered. The key innovations in the design of TASE are a technique for amortizing the cost of checking whether values are symbolic over few instructions, and the use of hardware-supported transactional memory (TSX) to implement native execution that rolls back with no effect when use of a symbolic value is detected (perhaps belatedly). We show that TASE has the potential to dramatically improve some latency-sensitive applications of symbolic execution, such as methods to verify the behavior of a client in a client-server application.
ODE-based Deep Network for MRI Reconstruction

Fast data acquisition in Magnetic Resonance Imaging (MRI) is vastly in demand and scan time directly depends on the number of acquired k-space samples. The data-driven methods based on deep neural networks have resulted in promising improvements, compared to the conventional methods, in image reconstruction algorithms. The connection between deep neural network and Ordinary Differential Equation (ODE) has been observed and studied recently. The studies show that different residual networks can be interpreted as Euler discretization of an ODE. In this paper, we propose an ODE-based deep network for MRI reconstruction to enable the rapid acquisition of MR images with improved image quality. Our results with undersampled data demonstrate that our method can deliver higher quality images in comparison to the reconstruction methods based on the standard UNet network and Residual network.
B‐value and empirical equivalence bound: A new procedure of hypothesis testing

In this study, we propose a two‐stage procedure for hypothesis testing, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced empirical equivalence bound (EEB). In 2016, the American Statistical Association released a policy statement on P‐values to clarify the proper use and interpretation in response to the criticism of reproducibility and replicability in scientific findings. A recent solution to improve reproducibility and transparency in statistical hypothesis testing is to integrate P‐values (or confidence intervals) with practical or scientific significance. Similar ideas have been proposed via the equivalence test, where the goal is to infer equality under a presumption (null) of inequality of parameters. However, the definition of scientific significance/equivalence can sometimes be ill‐justified and subjective. To circumvent this drawback, we introduce the B‐value and the EEB, which are both estimated from the data. Performing a second‐stage equivalence test, our procedure offers an opportunity to improve the reproducibility of findings across studies.