Miracle-dz/UniFAR-FTUs huggingface dataset:
Inertial Bregman Proximal Gradient Algorithm For Nonconvex Problem with Smooth Adaptable Property

In this paper we study the problems of minimizing the sum of two nonconvex functions: one is differentiable and satisfies smooth adaptable property. The smooth adaptable property, also named relatively smooth condition, is weaker than the globally gradient Lipschitz continuity. We analyze an inertial version of the Bregman Proximal Gradient (BPG) algorithm and prove its stationary convergence. Besides, we prove a sublinear convergence of the inertial algorithm. Moreover, if the objective function satisfies Kurdyka--{\L}ojasiewicz (KL) property, its global convergence to a critical point of the objective function can be also guaranteed.
Using gamma ray monitoring to avoid missing the next Milky Way Type Ia supernova

A Milky Way Type Ia supernova (SNIa) could be unidentified or even initially unnoticed, being dim in radio, X-rays, and neutrinos, and suffering large optical/IR extinction in the Galactic plane. But SNIa emit nuclear gamma-ray lines from 56Ni → 56Co → 56Fe radioactive decays. These lines fall within the Fermi/GBM energy range, and the 56Ni 158 keV line is detectable by Swift/BAT. Both instruments frequently monitor the Galactic plane, which is transparent to gamma rays. Thus GBM and BAT are ideal Galactic SNIa early warning systems. We simulate SNIa MeV light curves and spectra to show that GBM and BAT could confirm a Galactic SNIa explosion, followed by Swift localization and observation in X-rays and UVOIR band. The time of detection depends sensitively on the 56Ni distribution, and can be as early as a few days if ${\gtrsim } 10{{\ \rm per\ cent}}$ of the 56Ni is present in the surface as suggested by SN2014J gamma data.
Metasurface Engineering through Bound States in the Continuum

Metasurfaces have attracted a lot of attention in recent years due to novel ways they provide for the efficient wavefront control and engineering of the resonant transmission. We discuss an approach allowing effectively control appearance of the sharp Fano resonances in metasurfaces associated with the {\it bound states in the continuum}. We demonstrate that by breaking the symmetry transversely, in the direction perpendicular to a metasurface with a complex unit cell, we can control the number, frequency, and type of high-$Q$ resonances originating from bound states in the continuum. As example, we demonstrate experimentally the metasurfaces with magnetic dipole and toroidal dipole responses governed by the physics of multipolar bound states.
Arithmetic occult period maps

Several natural complex configuration spaces admit surprising uniformizations as arithmetic ball quotients, by identifying each parametrized object with the periods of some auxiliary object. In each case, the theory of canonical models of Shimura varieties gives the ball quotient the structure of a variety over the ring of integers of a cyclotomic field. We show that the (transcendentally-defined) period map actually respects these algebraic structures, and thus that occult period maps are arithmetic. As an intermediate tool, we develop an arithmetic theory of lattice-polarized K3 surfaces.
Progressive Speech Enhancement with Residual Connections

This paper studies the Speech Enhancement based on Deep Neural Networks. The proposed architecture gradually follows the signal transformation during enhancement by means of a visualization probe at each network block. Alongside the process, the enhancement performance is visually inspected and evaluated in terms of regression cost. This progressive scheme is based on Residual Networks. During the process, we investigate a residual connection with a constant number of channels, including internal state between blocks, and adding progressive supervision. The insights provided by the interpretation of the network enhancement process leads us to design an improved architecture for the enhancement purpose. Following this strategy, we are able to obtain speech enhancement results beyond the state-of-the-art, achieving a favorable trade-off between dereverberation and the amount of spectral distortion.
MVF-Net: Multi-View 3D Face Morphable Model Regression

We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morphable Model (3DMM) based facial reconstruction, the settings are mostly restricted to a single view. There is an inherent drawback in the single-view setting: the lack of reliable 3D constraints can cause unresolvable ambiguities. We in this paper explore 3DMM-based shape recovery in a different setting, where a set of multi-view facial images are given as input. A novel approach is proposed to regress 3DMM parameters from multi-view inputs with an end-to-end trainable Convolutional Neural Network (CNN). Multi-view geometric constraints are incorporated into the network by establishing dense correspondences between different views leveraging a novel self-supervised view alignment loss. The main ingredient of the view alignment loss is a differentiable dense optical flow estimator that can backpropagate the alignment errors between an input view and a synthetic rendering from another input view, which is projected to the target view through the 3D shape to be inferred. Through minimizing the view alignment loss, better 3D shapes can be recovered such that the synthetic projections from one view to another can better align with the observed image. Extensive experiments demonstrate the superiority of the proposed method over other 3DMM methods.
Label Super Resolution with Inter-Instance Loss

For the task of semantic segmentation, high-resolution (pixel-level) ground truth is very expensive to collect, especially for high resolution images such as gigapixel pathology images. On the other hand, collecting low resolution labels (labels for a block of pixels) for these high resolution images is much more cost efficient. Conventional methods trained on these low-resolution labels are only capable of giving low-resolution predictions. The existing state-of-the-art label super resolution (LSR) method is capable of predicting high resolution labels, using only low-resolution supervision, given the joint distribution between low resolution and high resolution labels. However, it does not consider the inter-instance variance which is crucial in the ideal mathematical formulation. In this work, we propose a novel loss function modeling the inter-instance variance. We test our method on two real world applications: cell detection in multiplex immunohistochemistry (IHC) images, and infiltrating breast cancer region segmentation in histopathology slides. Experimental results show the effectiveness of our method.
Ptr/CTL0175 Is Required for the Efficient Recovery of Chlamydia trachomatis From Stress Induced by Gamma-Interferon

Chlamydia trachomatis is the most common sexually transmitted bacterial pathogen in humans and a frequent cause of asymptomatic, persistent infections leading to serious complications, particularly in young women. Chlamydia displays a unique obligate intracellular lifestyle involving the infectious elementary body and the replicative reticulate body. In the presence of stressors such as gamma-interferon (IFNγ) or beta-lactam antibiotics, C. trachomatis undergoes an interruption in its replication cycle and enters a viable but non-cultivable state. Upon removal of the stressors, surviving C. trachomatis resume cell division and developmental transitions. In this report, we describe a genetic screen to identify C. trachomatis mutants with defects in recovery from IFNγ- and/or penicillin-induced stress and characterized a chemically derived C. trachomatis mutant strain that exhibited a significant decrease in recovery from IFNγ- but not penicillin-induced stress. Through lateral gene transfer and targeted insertional gene inactivation we identified ptr, encoding a predicted protease, as a gene required for recovery from IFNγ-induced stress. A C. trachomatis LGV-L2 ptr-null strain displayed reduced generation of infectious progeny and impaired genome replication upon removal of IFNγ. This defect was restored by introducing a wild type copy of ptr on a plasmid, indicating that Ptr is required for a rapid growth upon removal of IFNγ. Ptr was expressed throughout the developmental cycle and localized to the inclusion lumen. Overall, our findings indicate that the putative secreted protease Ptr is required for C. trachomatis to specifically recover from IFNγ- but not penicillin-induced stress.
Meta-analysis of Bayesian analyses.

Meta-analysis aims to combine results from multiple related statistical analyses. While the natural outcome of a Bayesian analysis is a posterior distribution, Bayesian meta-analyses traditionally combine analyses summarized as point estimates, often limiting distributional assumptions. In this paper, we develop a framework for combining posterior distributions, which builds on standard Bayesian inference, but using distributions instead of data points as observations. We show that the resulting framework preserves basic theoretical properties, such as order-invariance in successive updates and posterior concentration. In addition to providing a consensus analysis for multiple Bayesian analyses, we highlight the benefit of being able to reuse posteriors from computationally costly analyses and update them post-hoc without having to rerun the analyses themselves. The wide applicability of the framework is illustrated with examples of combining results from likelihood-free Bayesian analyses, which would be difficult to carry out using standard methodology.
On the origin and the manipulation of ferromagnetism in Fe$_3$GeTe$_2$: defects and dopings

To understand the magnetic properties of Fe$_3$GeTe$_2$, we performed the detailed first-principles study. Contrary to the conventional wisdom, it is unambiguously shown that Fe$_3$GeTe$_2$ is not ferromagnetic but antiferromagnetic carrying zero net moment in its stoichiometric phase. Fe defect and hole doping are the keys to make this material ferromagnetic, which are shown by the magnetic force response as well as the total energy calculation with the explicit Fe defects and the varied system charges. Further, we found that the electron doping also induces the antiferro- to ferromagnetic transition. It is a crucial factor to understand the notable recent experiment of gate-controlled ferromagnetism. Our results not only unveil the origin of ferromagnetism of this material but also show how it can be manipulated with defect and doping.
On the Adaptivity of Stochastic Gradient-Based Optimization

Stochastic-gradient-based optimization has been a core enabling methodology in applications to large-scale problems in machine learning and related areas. Despite the progress, the gap between theory and practice remains significant, with theoreticians pursuing mathematical optimality at a cost of obtaining specialized procedures in different regimes (e.g., modulus of strong convexity, magnitude of target accuracy, signal-to-noise ratio), and with practitioners not readily able to know which regime is appropriate to their problem, and seeking broadly applicable algorithms that are reasonably close to optimality. To bridge these perspectives it is necessary to study algorithms that are adaptive to different regimes. We present the stochastically controlled stochastic gradient (SCSG) method for composite convex finite-sum optimization problems and show that SCSG is adaptive to both strong convexity and target accuracy. The adaptivity is achieved by batch variance reduction with adaptive batch sizes and a novel technique, which we referred to as \emph{geometrization}, which sets the length of each epoch as a geometric random variable. The algorithm achieves strictly better theoretical complexity than other existing adaptive algorithms, while the tuning parameters of the algorithm only depend on the smoothness parameter of the objective.
Time crystallinity in dissipative Floquet systems

We investigate the conditions under which periodically driven quantum systems subject to dissipation exhibit a stable subharmonic response. Noting that coupling to a bath introduces not only cooling but also noise, we point out that a system subject to the latter for the entire cycle tends to lose coherence of the subharmonic oscillations, and thereby the long-time temporal symmetry breaking. We provide an example of a short-ranged two-dimensional system which does not suffer from this and therefore displays persistent subharmonic oscillations stabilised by the dissipation. We also show that this is fundamentally different from the disordered DTC previously found in closed systems, both conceptually and in its phenomenology. The framework we develop here clarifies how fully connected models constitute a special case where subharmonic oscillations are stable in the thermodynamic limit.
Effective resistance of random percolating networks of stick nanowires: Functional dependence on elementary physical parameters

We study by means of Monte-Carlo numerical simulations the resistance of two-dimensional random percolating networks of stick, widthless nanowires. We use the multi-nodal representation (MNR) to model a nanowire network as a graph. We derive numerically from this model the expression of the total resistance as a function of all meaningful parameters, geometrical and physical, over a wide range of variation for each. We justify our choice of non-dimensional variables applying Buckingham $\pi-$theorem. The effective resistance of 2D random percolating networks of nanowires is found to write as $R^{eq}(\rho,R_c,R_{m,w})=A\left(N,\frac{L}{l^{*}}\right) \rho l^* + B\left(N,\frac{L}{l^{*}}\right) R_c+C\left(N,\frac{L}{l^*} \right) R_{m,w}$ where $N$, $\frac{L}{l^{*}}$ are the geometrical parameters (number of wires, aspect ratio of electrode separation over wire length) and $\rho$, $R_c$, $R_{m,w}$ are the physical parameters (nanowire linear resistance per unit length, nanowire/nanowire contact resistance, metallic electrode/nanowire contact resistance). The dependence of the resistance on the geometry of the network, one the one hand, and on the physical parameters (values of the resistances), on the other hand, is thus clearly separated thanks to this expression, much simpler than the previously reported analytical expressions.
Comparison of SEDs of very massive radio-loud and radio-quiet AGN

The main objective of this work is to establish and interpret the dominant spectral components and their differences in radio-loud (RL) and radio-quiet (RQ) AGN with very massive black holes, and accreting at moderate rates. Such a sample is selected from the Swift/BAT catalogue of AGN having determined optical spectra types and hosting black holes with masses $\rm \gt 10^{8.5}\, M_{\odot }$. We confirm our previous results, that radio loudness distribution of Swift/BAT AGN is bimodal and that radio galaxies are about two times X-ray louder than their RQ counterparts. We show that the average X-ray loudness (defined as a ratio of luminosity in the 14–195 keV band to that at 12 μm) of Type 1 and Type 2 AGN is very similar. This similarity holds for both RL and RQ subsamples and indicates negligible dependence of the observed X-ray luminosities on the inclination angle in both populations. In both the radiative output is dominated by mid-IR and hard X-ray components, and relatively weak UV luminosities indicate large amounts of dust in polar regions.
Analog nature of quantum adiabatic unstructured search

The quantum adiabatic unstructured search algorithm is one of only a handful of quantum adiabatic optimization algorithms to exhibit provable speedups over their classical counterparts. With no fault tolerance theorems to guarantee the resilience of such algorithms against errors, understanding the impact of imperfections on their performance is of both scientific and practical significance. We study the robustness of the algorithm against various types of imperfections: limited control over the interpolating schedule, Hamiltonian misspecification, and interactions with a thermal environment. We find that the unstructured search algorithm’s quadratic speedup is generally not robust to the presence of any one of the above non-idealities, and in some cases we find that it imposes unrealistic conditions on how the strength of these noise sources must scale to maintain the quadratic speedup.
Enabling Privacy-Preserving, Compute- and Data-Intensive Computing using Heterogeneous Trusted Execution Environment

There is an urgent demand for privacy-preserving techniques capable of supporting compute and data intensive (CDI) computing in the era of big data. However, none of existing TEEs can truly support CDI computing tasks, as CDI requires high throughput accelerators like GPU and TPU but TEEs do not offer security protection of such accelerators. This paper present HETEE (Heterogeneous TEE), the first design of TEE capable of strongly protecting heterogeneous computing with unsecure accelerators. HETEE is uniquely constructed to work with today's servers, and does not require any changes for existing commercial CPUs or accelerators. The key idea of our design runs security controller as a stand-alone computing system to dynamically adjust the boundary of between secure and insecure worlds through the PCIe switches, rendering the control of an accelerator to the host OS when it is not needed for secure computing, and shifting it back when it is. The controller is the only trust unit in the system and it runs the custom OS and accelerator runtimes, together with the encryption, authentication and remote attestation components. The host server and other computing systems communicate with controller through an in memory task queue that accommodates the computing tasks offloaded to HETEE, in the form of encrypted and signed code and data. Also, HETEE offers a generic and efficient programming model to the host CPU. We have implemented the HETEE design on a hardware prototype system, and evaluated it with large-scale Neural Networks inference and training tasks. Our evaluations show that HETEE can easily support such secure computing tasks and only incurs a 12.34% throughput overhead for inference and 9.87% overhead for training on average.
Surface Defect Classification in Real-Time Using Convolutional Neural Networks

Surface inspection systems are an important application domain for computer vision, as they are used for defect detection and classification in the manufacturing industry. Existing systems use hand-crafted features which require extensive domain knowledge to create. Even though Convolutional neural networks (CNNs) have proven successful in many large-scale challenges, industrial inspection systems have yet barely realized their potential due to two significant challenges: real-time processing speed requirements and specialized narrow domain-specific datasets which are sometimes limited in size. In this paper, we propose CNN models that are specifically designed to handle capacity and real-time speed requirements of surface inspection systems. To train and evaluate our network models, we created a surface image dataset containing more than 22000 labeled images with many types of surface materials and achieved 98.0% accuracy in binary defect classification. To solve the class imbalance problem in our datasets, we introduce neural data augmentation methods which are also applicable to similar domains that suffer from the same problem. Our results show that deep learning based methods are feasible to be used in surface inspection systems and outperform traditional methods in accuracy and inference time by considerable margins.
An Emergent Higgs Alignment

We explain how an $SU(2)_R$ R-symmetry allows alignment of the vacuum expectation value and the mass eigenstates in the Higgs bosons squared-mass matrix. More important, we discuss how our parametrization of the breaking of this global symmetry allows to quantitatively apprehend the diverse sources of misalignment, which are kept, by supersymmetry, small.
A Hierarchical Decoding Model for Spoken Language Understanding from Unaligned Data

Spoken language understanding (SLU) systems can be trained on two types of labelled data: aligned or unaligned. Unaligned data do not require word by word annotation and is easier to be obtained. In the paper, we focus on spoken language understanding from unaligned data whose annotation is a set of act-slot-value triples. Previous works usually focus on improve slot-value pair prediction and estimate dialogue act types separately, which ignores the hierarchical structure of the act-slot-value triples. Here, we propose a novel hierarchical decoding model which dynamically parses act, slot and value in a structured way and employs pointer network to handle out-of-vocabulary (OOV) values. Experiments on DSTC2 dataset, a benchmark unaligned dataset, show that the proposed model not only outperforms previous state-of-the-art model, but also can be generalized effectively and efficiently to unseen act-slot type pairs and OOV values.
TDM/WDM over AWGR Based Passive Optical Network Data Centre Architecture

Passive Optical Data Centre Networks have been developed due to the performance limitations in current data centres to provide high performance within data centre networks. An AWGR based passive optical network data centre architecture is evaluated using a TDM/WDM multiple access technique to provision the flow of traffic among the network efficiently. A Mixed Integer Linear Programming model is developed to optimise resource allocation in the architecture. Using WDM-TDM as a multiple access technique helps in solving issues such as oversubscription and congestion by allowing servers to make simultaneous transmissions of packets in different time slots. The results show that the provisioning / allocation of resource within the architecture is improved with improvements of up to 75% in resource utilisation.
Ergodicity Breaking Arising from Hilbert Space Fragmentation in Dipole-Conserving Hamiltonians

We show that the combination of charge and dipole conservation---characteristic of fracton systems---leads to an extensive fragmentation of the Hilbert space, which in turn can lead to a breakdown of thermalization. As a concrete example, we investigate the out-of-equilibrium dynamics of one-dimensional spin-1 models that conserve charge (total $S^z$) and its associated dipole moment. First, we consider a minimal model including only three-site terms and find that the infinite temperature auto-correlation saturates to a finite value---showcasing non-thermal behavior. The absence of thermalization is identified as a consequence of the strong fragmentation of the Hilbert space into exponentially many invariant subspaces in the local $S^z$ basis, arising from the interplay of dipole conservation and local interactions. Second, we extend the model by including four-site terms and find that this perturbation leads to a weak fragmentation: the system still has exponentially many invariant subspaces, but they are no longer sufficient to avoid thermalization for typical initial states. More generally, for any finite range of interactions, the system still exhibits non-thermal eigenstates appearing throughout the entire spectrum. We compare our results to charge and dipole moment conserving random unitary circuit models for which we reach identical conclusions.
Gumbel and Fréchet convergence of the maxima of independent random walks

Abstract We consider point process convergence for sequences of independent and identically distributed random walks. The objective is to derive asymptotic theory for the largest extremes of these random walks. We show convergence of the maximum random walk to the Gumbel or the Fréchet distributions. The proofs depend heavily on precise large deviation results for sums of independent random variables with a finite moment generating function or with a subexponential distribution.