Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental Realization of Near-Field Photonic Routing with All-Electric Metasources

Yang Long, Jie Ren, Zhiwei Guo2,∗ Haitao Jiang2,† Yuqian Wang, Yong Sun, and Hong Chen Center for Phononics and Thermal Energy Science, China-EU Joint Center for Nanophononics, Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology, School of Physics Sciences and Engineering, Tongji University, Shanghai 200092, China Key Laboratory of Advanced Micro-structure Materials, MOE, School of Physics Science and Engineering, Tongji University, Shanghai, 200092, China (Dated: April 14, 2020)
Linear-time Algorithms for Eliminating Claws in Graphs

Since many NP-complete graph problems have been shown polynomial-time solvable when restricted to claw-free graphs, we study the problem of determining the distance of a given graph to a claw-free graph, considering vertex elimination as measure. CLAW-FREE VERTEX DELETION (CFVD) consists of determining the minimum number of vertices to be removed from a graph such that the resulting graph is claw-free. Although CFVD is NP-complete in general and recognizing claw-free graphs is still a challenge, where the current best algorithm for a graph $G$ has the same running time of the best algorithm for matrix multiplication, we present linear-time algorithms for CFVD on weighted block graphs and weighted graphs with bounded treewidth. Furthermore, we show that this problem can be solved in linear time by a simpler algorithm on forests, and we determine the exact values for full $k$-ary trees. On the other hand, we show that CLAW-FREE VERTEX DELETION is NP-complete even when the input graph is a split graph. We also show that the problem is hard to approximate within any constant factor better than $2$, assuming the Unique Games Conjecture.
The nonperturbative phase diagram of the bosonic BMN matrix model

We study the thermal phase transition of the bosonic BMN model which is a mass deformed version of the bosonic part of the BFSS model. Our results connect the massless region of the phase diagram described by the bosonic BFSS model with the large-mass region, where the model is analytically solvable. We observe that at finite value of the matrix size $N$, the critical region is smeared over a small temperature range. The model has a single critical temperature, which arises as the large $N$ limit of two apparent transitions at finite $N$. We emphasise the vital role played by finite $N$ corrections in the confined phase and illustrate this with a novel treatment of the noninteracting Gaussian model.
A Non-Parametric Test to Detect Data-Copying in Generative Models

Detecting overfitting in generative models is an important challenge in machine learning. In this work, we formalize a form of overfitting that we call {\em{data-copying}} -- where the generative model memorizes and outputs training samples or small variations thereof. We provide a three sample non-parametric test for detecting data-copying that uses the training set, a separate sample from the target distribution, and a generated sample from the model, and study the performance of our test on several canonical models and datasets. For code \& examples, visit this https URL
Vintage Factor Analysis with Varimax Performs Statistical Inference

Psychologists developed Multiple Factor Analysis to decompose multivariate data into a small number of interpretable factors without any a priori knowledge about those factors [Thurstone, 1935]. In this form of factor analysis, the Varimax factor rotation redraws the axes through the multidimensional factors to make them sparse and thus make them more interpretable [Kaiser, 1958]. Charles Spearman and many others objected to factor rotations because the factors seem to be rotationally invariant [Thurstone, 1947, Anderson and Rubin, 1956]. These objections are still reported in all contemporary multivariate statistics textbooks. However, this vintage form of factor analysis has survived and is widely popular because, empirically, the factor rotation often makes the factors easier to interpret. We argue that the rotation makes the factors easier to interpret because, in fact, the Varimax factor rotation performs statistical inference. We show that Principal Components Analysis (PCA) with the Varimax axes provides a unified spectral estimation strategy for a broad class of semi-parametric factor models, including the Stochastic Blockmodel and a natural variation of Latent Dirichlet Allocation (i.e., \topic modeling"). In addition, we show that Thurstone's widely employed sparsity diagnostics implicitly assess a key leptokurtic condition that makes the axes statistically identifiable in these models. Taken together, this shows that the know-how of Vintage Factor Analysis performs statistical inference, reversing nearly a century of statistical thinking on the topic. We illustrate these techniques use on two large bibliometric examples (a citation network and a text corpus). With a sparse eigensolver, PCA with Varimax is both fast and stable. Combined with Thurstone's straightforward diagnostics, this vintage approach is suitable for a wide array of modern applications.
DarkneTZ: towards model privacy at the edge using trusted execution environments

We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs). Increasingly, edge devices (smartphones and consumer IoT devices) are equipped with pre-trained DNNs for a variety of applications. This trend comes with privacy risks as models can leak information about their training data through effective membership inference attacks (MIAs). We evaluate the performance of DarkneTZ, including CPU execution time, memory usage, and accurate power consumption, using two small and six large image classification models. Due to the limited memory of the edge device's TEE, we partition model layers into more sensitive layers (to be executed inside the device TEE), and a set of layers to be executed in the untrusted part of the operating system. Our results show that even if a single layer is hidden, we can provide reliable model privacy and defend against state of the art MIAs, with only 3% performance overhead. When fully utilizing the TEE, DarkneTZ provides model protections with up to 10% overhead.
The evolution of inverted magnetic fields through the inner heliosphere

Local inversions are often observed in the heliospheric magnetic field (HMF), but their origins and evolution are not yet fully understood. Parker Solar Probe has recently observed rapid, Alfvénic, HMF inversions in the inner heliosphere, known as ‘switchbacks’, which have been interpreted as the possible remnants of coronal jets. It has also been suggested that inverted HMF may be produced by near-Sun interchange reconnection; a key process in mechanisms proposed for slow solar wind release. These cases suggest that the source of inverted HMF is near the Sun, and it follows that these inversions would gradually decay and straighten as they propagate out through the heliosphere. Alternatively, HMF inversions could form during solar wind transit, through phenomena such velocity shears, draping over ejecta, or waves and turbulence. Such processes are expected to lead to a qualitatively radial evolution of inverted HMF structures. Using Helios measurements spanning 0.3–1 au, we examine the occurrence rate of inverted HMF, as well as other magnetic field morphologies, as a function of radial distance r, and find that it continually increases. This trend may be explained by inverted HMF observed between 0.3 and 1 au being primarily driven by one or more of the above in-transit processes, rather than created at the Sun. We make suggestions as to the relative importance of these different processes based on the evolution of the magnetic field properties associated with inverted HMF. We also explore alternative explanations outside of our suggested driving processes which may lead to the observed trend.
RaSSteR: Random Sparse Step-Frequency Radar

We propose a method for synthesizing high range resolution profiles (HRRP) using stepped frequency waveform (SFW) processing. Conventional SFW radars sweep over the available spectrum linearly to achieve high resolution from their instantaneous bandwidth. However, they suffer from strong range-Doppler coupling and coexisting spectral interference. Prior works are able to mitigate only one of these drawbacks. We present a new \textit{ra}ndom \textit{s}parse \textit{ste}p-frequency \textit{r}adar (RaSSteR) waveform that consumes less spectral resources without loss of range resolution and estimates both high-resolution range and Doppler by exploiting sparse recovery techniques. In the presence of interference, the operation with the new waveform is made cognitive by focusing available transmit power only in the few transmit bands. Our theoretical analyses show that, even while using fewer carriers in the available bandwidth, RaSSteR has identical recovery guarantees as the standard random stepped frequency (RSF) waveform. Numerical experiments demonstrate performance enhancements with RaSSteR over state-of-the-art such as SFW, RSF, conventional pulse-compression-based pulse Doppler radar, and sub-Nyquist radar. In addition, the target hit rate of RaSSteR in the presence of strong interference is 30% more than conventional RSF.
Bayesian modelling for spatially misaligned health areal data: A multiple membership approach

Diabetes prevalence is on the rise in the United Kingdom, and for public health strategy, estimation of relative disease risk and subsequent mapping is important. We consider an application to London data on diabetes prevalence and mortality. In order to improve the estimation of relative risks, we analyse jointly prevalence and mortality data to ensure borrowing strength over the two outcomes. The available data involve two spatial frameworks, areas (Middle Layer Super Output Areas, MSOAs) and general practices (GPs) recruiting patients from several areas. This raises a spatial misalignment issue that we deal with by employing the multiple membership principle. Specifically, we translate areal spatial effects to explain GP practice prevalence according to proportions of GP populations resident in different areas. A sparse implementation in RStan of both the multivariate conditional autoregressive (MCAR) and generalised MCAR (GMCAR) with multiple membership allows the comparison of these bivariate priors as well as exploring the different implications for the mapping patterns for both outcomes. The necessary causal precedence of diabetes prevalence over mortality allows a specific conditionality assumption in the GMCAR, not always present in the context of disease mapping. Additionally, an area‐locality comparison is considered to locate high versus low relative risk clusters.
Autoencoding Generative Adversarial Networks

In the years since Goodfellow et al. introduced Generative Adversarial Networks (GANs), there has been an explosion in the breadth and quality of generative model applications. Despite this work, GANs still have a long way to go before they see mainstream adoption, owing largely to their infamous training instability. Here I propose the Autoencoding Generative Adversarial Network (AEGAN), a four-network model which learns a bijective mapping between a specified latent space and a given sample space by applying an adversarial loss and a reconstruction loss to both the generated images and the generated latent vectors. The AEGAN technique offers several improvements to typical GAN training, including training stabilization, mode-collapse prevention, and permitting the direct interpolation between real samples. The effectiveness of the technique is illustrated using an anime face dataset.
Lower bound for succinct range minimum query

Given an integer array A[1.n], the Range Minimum Query problem (RMQ) asks to preprocess A into a data structure, supporting RMQ queries: given a,b∈ [1,n], return the index i∈[a,b] that minimizes A[i], i.e., argmin i∈[a,b] A[i]. This problem has a classic solution using O(n) space and O(1) query time by Gabow, Bentley, Tarjan (STOC, 1984) and Harel, Tarjan (SICOMP, 1984). The best known data structure by Fischer, Heun (SICOMP, 2011) and Navarro, Sadakane (TALG, 2014) uses 2n+n/(logn/t) t +Õ(n 3/4) bits and answers queries in O(t) time, assuming the word-size is w=Θ(logn). In particular, it uses 2n+n/polylogn bits of space as long as the query time is a constant. In this paper, we prove the first lower bound for this problem, showing that 2n+n/polylogn space is necessary for constant query time. In general, we show that if the data structure has query time O(t), then it must use at least 2n+n/(logn)Õ(t 2) space, in the cell-probe model with word-size w=Θ(logn).
A Novel Pose Proposal Network and Refinement Pipeline for Better Object Pose Estimation

In this paper, we present a novel deep learning pipeline for 6D object pose estimation and refinement from RGB inputs. The first component of the pipeline leverages a region proposal framework to estimate multi-class single-shot 6D object poses directly from an RGB image and through a CNN-based encoder multi-decoders network. The second component, a multi-attentional pose refinement network (MARN), iteratively refines the estimated pose. MARN takes advantage of both visual and flow features to learn a relative transformation between an initially predicted pose and a target pose. MARN is further augmented by a spatial multi-attention block that emphasizes objects' discriminative feature parts. Experiments on three benchmarks for 6D pose estimation show that the proposed pipeline outperforms state-of-the-art RGB-based methods with competitive runtime performance.
Deep Siamese Domain Adaptation Convolutional Neural Network for Cross-domain Change Detection in Multispectral Images

Recently, deep learning has achieved promising performance in the change detection task. However, the deep models are task-specific and data set bias often exists, thus it is difficult to transfer a network trained on one multi-temporal data set (source domain) to another multi-temporal data set with very limited (even no) labeled data (target domain). In this paper, we propose a novel deep siamese domain adaptation convolutional neural network (DSDANet) architecture for cross-domain change detection. In DSDANet, a siamese convolutional neural network first extracts spatial-spectral features from multi-temporal images. Then, through multiple kernel maximum mean discrepancy (MK-MMD), the learned feature representation is embedded into a reproducing kernel Hilbert space (RKHS), in which the distribution of two domains can be explicitly matched. By optimizing the network parameters and kernel coefficients with the source labeled data and target unlabeled data, the DSDANet can learn transferrable feature representation that can bridge the discrepancy between two domains. To the best of our knowledge, it is the first time that such a domain adaptation-based deep network is proposed for change detection. The theoretical analysis and experimental results demonstrate the effectiveness and potential of the proposed method.
Investigation of Magnetic Anisotropy and Heat Dissipation in Thin Films of Compensated Antiferromagnet CuMnAs by Pump-probe Experiment.

We recently reported on a method to determine the easy axis position in a 10 nm thick film of the fully compensated antiferromagnet CuMnAs. The film had a uniaxial magnetic anisotropy and the technique utilized a magneto-optical pump and probe experiment [Nature Photonics 11, 91 (2017)]. In this contribution we discuss the applicability of this method for the investigation of a broader set of epitaxial CuMnAs films having different thicknesses. This work reveals that the equilibrium magnetic anisotropy can be studied only in samples where this anisotropy is rather strong. However, in the majority of CuMnAs films, the impact of a strong pump pulse induces nano-fragmentation of the magnetic domains and, therefore, the magnetic anisotropy measured by the pump-probe technique differs substantially from that in the equilibrium conditions. We also demonstrate that optical pump-probe experiment can be used very efficiently to study the local heating and heat dissipation in CuMnAs epitaxial layers. In particular, we determined the electron-phonon relaxation time in CuMnAs. We also observed that for a local film heating by a focused laser the thinner films are heated more, but the heat is dissipated considerably faster than in the case of thicker films. This illustrates that the optical pump-probe experiment is a valuable characterization tool for the heat management optimization in the CuMnAs memory devices and can be applied in a similar way to those used during heat-assisted magnetic recording (HAMR) technology development for the latest generation of hard drive disks.
Explaining the absence of symbiotic stars in globular clusters and observational prospects for their identification

Even though plenty of symbiotic stars (SySts) have been found in the Galactic field and nearby galaxies, not a single one has ever been confirmed in a Galactic globular cluster (GC). We investigate the lack of such systems in GCs for the first time by analysing 144 GC models evolved with the MOCCA code, which have different initial properties and are roughly representative of the Galactic GC population. We focus here on SySts formed through the wind-accretion channel, which can be consistently modelled in binary population synthesis codes. We found that the orbital periods of the majority of such SySts are sufficiently long (& 10 days) so that, for very dense GC models, dynamical interactions play an important role in destroying their progenitors before the present day (∼ 11− 12 Gyr). In less dense GC models, some SySts are still predicted to exist. However, these systems tend to have relatively low white dwarf luminosities (. 100 L ), are located far from the central parts (& 70 per cent are far beyond the half-light radius) and are sufficiently rare (. 1 per GC per Myr), which makes their identification rather difficult in current observational campaigns. We propose that future searches for SySts in GCs should be performed with either high-quality spectroscopy or photometry (using narrow-band filters centred on either He II and Hα or Raman scattered O VI emission lines), in the outskirts of nearby low-density GCs with sufficiently long half-mass relaxation times and relatively large Galactocentric distances. Finally, we obtained spectra of the candidate proposed in ω Cen (SOPS IV e-94) and showed that this object is not a SySt.
Fractal projections with an application in number theory

Abstract In this paper, we discuss a connection between geometric measure theory and number theory. This method brings a new point of view for some number-theoretic problems concerning digit expansions. Among other results, we show that for each integer k, there is a number $M>0$ such that if $b_{1},\ldots ,b_{k}$ are multiplicatively independent integers greater than M, there are infinitely many integers whose base $b_{1},b_{2},\ldots ,b_{k}$ expansions all do not have any zero digits.
Path dependent Feynman–Kac formula for forward backward stochastic Volterra integral equations

This paper is concerned with the relationship between forward-backward stochastic Volterra integral equations (FBSVIEs, for short) and a system of (non-local in time) path dependent partial differential equations (PPDEs, for short). Due to the nature of Volterra type equations, the usual flow property (or semigroup property) does not hold. Inspired by Viens-Zhang \cite{Viens-Zhang-2019} and Wang-Yong \cite{Wang-Yong-2019}, auxiliary processes are introduced so that the flow property of adapted solutions to the FBSVIEs is recovered in a suitable sense, and thus the functional Ito's formula is applicable. Having achieved this stage, a natural PPDE is found so that the adapted solution of the backward SVIEs admits a representation in terms of the solution to the forward SVIE via the solution to a PPDE. On the other hand, the solution of the PPDE admits a representation in terms of adapted solution to the (path dependent) FBSVIE, which is referred to as a Feynman-Kac formula. This leads to the existence and uniqueness of a classical solution to the PPDE, under smoothness conditions on the coefficients of the FBSVIEs. Further, when the smoothness conditions are relaxed with the backward component of FBSVIE being one-dimensional, a new (and suitable) notion of viscosity solution is introduced for the PPDE, for which a comparison principle of the viscosity solutions is established, leading to the uniqueness of the viscosity solution. Finally, some results have been extended to coupled FBSVIEs and type-II BSVIEs, and a representation formula for the path derivatives of PPDE solution is obtained by a closer investigation of linear FBSVIEs.
Compositional Visual Generation and Inference with Energy Based Models

A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge. In this paper we show that energy-based models can exhibit this ability by directly combining probability distributions. Samples from the combined distribution correspond to compositions of concepts. For example, given a distribution for smiling faces, and another for male faces, we can combine them to generate smiling male faces. This allows us to generate natural images that simultaneously satisfy conjunctions, disjunctions, and negations of concepts. We evaluate compositional generation abilities of our model on the CelebA dataset of natural faces and synthetic 3D scene images. We also demonstrate other unique advantages of our model, such as the ability to continually learn and incorporate new concepts, or infer compositions of concept properties underlying an image.
Adaptive HTAP through Elastic Resource Scheduling

Modern Hybrid Transactional/Analytical Processing (HTAP) systems use an integrated data processing engine that performs analytics on fresh data, which are ingested from a transactional engine. HTAP systems typically consider data freshness at design time, and are optimized for a fixed range of freshness requirements, addressed at a performance cost for either OLTP or OLAP. The data freshness and the performance requirements of both engines, however, may vary with the workload. We approach HTAP as a scheduling problem, addressed at runtime through elastic resource management. We model an HTAP system as a set of three individual engines: an OLTP, an OLAP and a Resource and Data Exchange (RDE) engine. We devise a scheduling algorithm which traverses the HTAP design spectrum through elastic resource management, to meet the workload data freshness requirements. We propose an in-memory system design which is non-intrusive to the current state-of-art OLTP and OLAP engines, and we use it to evaluate the performance of our approach. Our evaluation shows that the performance benefit of our system for OLAP queries increases over time, reaching up to 50% compared to static schedules for 100 query sequences, while maintaining a small, and controlled, drop in the OLTP throughput.