Miracle-dz/UniFAR-FTUs huggingface dataset:
OL4EL: Online Learning for Edge-Cloud Collaborative Learning on Heterogeneous Edges with Resource Constraints

Distributed ML at the network edge is a promising paradigm that can preserve both network bandwidth and privacy of data providers. However, heterogeneity and limited computation and communication resources on edge servers (or edges) pose great challenges on distributed ML and formulate a new paradigm of edge learning (i.e., edge-cloud collaborative machine learning). In this article, we propose a novel framework of "learning to learn" for effective EL on heterogeneous edges with resource constraints. We first model the dynamic determination of collaboration strategy (i.e., the allocation of local iterations at edge servers and global aggregations on the cloud during the collaborative learning process) as an online optimization problem to achieve the trade-off between the performance of EL and the resource consumption of edge servers. Then we propose an OL4EL framework based on the budget-limited multi-armed bandit model. OL4EL supports both synchronous and asynchronous learning patterns, and can be used for both supervised and unsupervised learning tasks. To evaluate the performance of OL4EL, we conducted both real-world testbed experiments and extensive simulations based on Docker containers, where both support vector machine and K-means were considered as use cases. Experimental results demonstrate that OL4EL significantly outperforms state-of-the-art EL and other collaborative ML approaches in terms of the trade-off between learning performance and resource consumption.
Electrical Contact between an Ultrathin Topological Dirac Semimetal and a Two-Dimensional Material

Ultrathin films of topological Dirac semimetal, Na$_3$Bi, has recently been revealed as an unusual electronic materials with field-tunable topological phases. Here we investigate the electronic and transport properties of ultrathin Na$_3$Bi as an electrical contact to two-dimensional (2D) metal, i.e. graphene, and 2D semiconductor, i.e. MoS$_2$ and WS$_2$ monolayers. Using combined first-principle density functional theory and nonequilibrium Green's function simulation, we show that the electrical coupling between Na$_3$Bi bilayer thin film and graphene results in a notable interlayer charge transfer, thus inducing sizable $n$-type doping in the Na$_3$Bi/graphene heterostructures. In the case of MoS$_2$ and WS$_2$ monolayers, the lateral Schottky transport barrier is significantly lower than many commonly studied bulk metals, thus unraveling Na$_3$Bi bilayer as a high-efficiency electrical contact material for 2D semiconductors. These findings opens up an avenue of utilizing topological semimetal thin film as electrical contact to 2D materials, and further expands the family of 2D heterostructure devices into the realm of topological materials.
Practical calibration of the temperature parameter in Gibbs posteriors

PAC-Bayesian algorithms and Gibbs posteriors are gaining popularity due to their robustness against model misspecification even when Bayesian inference is inconsistent. The PAC-Bayesian alpha-posterior is a generalization of the standard Bayes posterior which can be tempered with a parameter alpha to handle inconsistency. Data driven methods for tuning alpha have been proposed but are still few, and are often computationally heavy. Additionally, the adequacy of these methods in cases where we use variational approximations instead of exact alpha-posteriors is not clear. This narrows their usage to simple models and prevents their application to large-scale problems. We hence need fast methods to tune alpha that work with both exact and variational alpha-posteriors. First, we propose two data driven methods for tuning alpha, based on sample-splitting and bootstrapping respectively. Second, we formulate the (exact or variational) posteriors of three popular statistical models, and modify them into alpha-posteriors. For each model, we test our strategies and compare them with standard Bayes and Grunwald's SafeBayes. While bootstrapping achieves mixed results, sample-splitting and SafeBayes perform well on the exact and variational alpha-posteriors we describe, and achieve better results than standard Bayes in misspecified or complex models. Additionally, sample-splitting outperforms SafeBayes in terms of speed. Sample-splitting offers a fast and easy solution to inconsistency and typically performs similarly or better than Bayesian inference. Our results provide hints on the calibration of alpha in PAC-Bayesian and Gibbs posteriors, and may facilitate using these methods in large and complex models.
Breaking Down Memory Walls: Adaptive Memory Management in LSM-based Storage Systems (Extended Version)

Log-Structured Merge-trees (LSM-trees) have been widely used in modern NoSQL systems. Due to their out-of-place update design, LSM-trees have introduced memory walls among the memory components of multiple LSM-trees and between the write memory and the buffer cache. Optimal memory allocation among these regions is non-trivial because it is highly workload-dependent. Existing LSM-tree implementations instead adopt static memory allocation schemes due to their simplicity and robustness, sacrificing performance. In this paper, we attempt to break down these memory walls in LSM-based storage systems. We first present a memory management architecture that enables adaptive memory management. We then present a partitioned memory component structure with new flush policies to better exploit the write memory to minimize the write cost. To break down the memory wall between the write memory and the buffer cache, we further introduce a memory tuner that tunes the memory allocation between these two regions. We have conducted extensive experiments in the context of Apache AsterixDB using the YCSB and TPC-C benchmarks and we present the results here.
Trading Off Diversity and Quality in Natural Language Generation

For open-ended language generation tasks such as storytelling or dialogue, choosing the right decoding algorithm is vital for controlling the tradeoff between generation quality and diversity. However, there presently exists no consensus on which decoding procedure is best or even the criteria by which to compare them. In this paper, we cast decoding as a tradeoff between response quality and diversity, and we perform the first large-scale evaluation of decoding methods along the entire quality-diversity spectrum. Our experiments confirm the existence of the likelihood trap: the counter-intuitive observation that high likelihood sequences are often surprisingly low quality. We also find that when diversity is a priority, all methods perform similarly, but when quality is viewed as more important, nucleus sampling (Holtzman et al., 2019) outperforms all other evaluated decoding algorithms.
An information‐theoretic approach to the analysis of location and co‐location patterns

We propose a statistical framework to quantify location and co-location associations of economic activities using information-theoretic measures. We relate the resulting measures to existing measures of revealed comparative advantage, localization and specialization and show that they can all be seen as part of the same framework. Using a Bayesian approach, we provide measures of uncertainty of the estimated quantities. Furthermore, the information-theoretic approach can be readily extended to move beyond pairwise co-locations and instead capture multivariate associations. To illustrate the framework, we apply our measures to the co-location of occupations in US cities, showing the associations between different groups of occupations.
Quantum Interference and Nonequilibrium Josephson Currents in Molecular Andreev Interferometers

We study the quantum interference (QI) effects in three-terminal Andreev interferometers based on polyaromatic hydrocarbons (PAHs) under non-equilibrium conditions. The Andreev interferometer consists of a PAH coupled to two superconducting and one normal conducting terminals. We calculate the current measured in the normal lead as well as the current between the superconducting terminals under non-equilibrium conditions. We show that both the QI arising in the PAH cores and the bias voltage applied to a normal contact have a fundamental effect on the charge distribution associated with the Andreev Bound States (ABSs). QI can lead to a peculiar dependence of the normal current on the superconducting phase difference that was not observed in earlier studies of mesoscopic Andreev interferometers. We explain our results by an induced asymmetry in the spatial distribution of the electron- and hole-like quasiparticles. The non-equilibrium charge occupation induced in the central PAH core can result in a π transition in the current-phase relation of the supercurrent for large enough applied bias voltage on the normal lead. The asymmetry in the spatial distribution of the electron- and hole-like quasiparticles might be used to split Cooper pairs and hence to produce entangled electrons in four terminal setups.
Neural forecasting: Introduction and literature overview

Neural network based forecasting methods have become ubiquitous in large-scale industrial forecasting applications over the last years. As the prevalence of neural network based solutions among the best entries in the recent M4 competition shows, the recent popularity of neural forecasting methods is not limited to industry and has also reached academia. This article aims at providing an introduction and an overview of some of the advances that have permitted the resurgence of neural networks in machine learning. Building on these foundations, the article then gives an overview of the recent literature on neural networks for forecasting and applications.
Multiscale schemes for stochastic dynamical systems driven by $\alpha$-stable processes

This work is about strong and weak convergence of projective integration schemes for multiscale stochastic dynamical systems driven by $\alpha$-stable processes. Firstly, we analyze a class of "projective integration" methods, which are used to estimate the effect that the fast components have on slow ones. Secondly, we obtain the $p$th moment error bounds between the results of the projective integration method and the slow components of the original system with $p \in (1, \min(\alpha_1, \alpha_2))$. Finally, a numerical experiment is constructed to illustrate this scheme.
Unveiling the Physical Conditions in NGC 6910

Deep and wide-field optical photometric observations along with multiwavelength archival data sets have been employed to study the physical properties of the cluster NGC 6910. The study also examines the impact of massive stars on their environment. The age, distance, and reddening of the cluster are estimated to be ∼4.5 Myr, 1.72 ± 0.08 kpc, and mag, respectively. The mass function slope (Γ = −0.74 ± 0.15) in the cluster region is found to be flatter than the Salpeter value (−1.35), indicating the presence of an excess number of massive stars. The cluster also shows mass segregation toward the central region due to their formation processes. The distribution of warm dust emission is investigated toward the central region of the cluster, showing the signature of the impact of massive stars within the cluster region. Radio continuum clumps powered by massive B-type stars (age range ∼0.07–0.12 Myr) are traced that are located away from the center of the stellar cluster NGC 6910 (age ∼4.5 Myr). Based on the values of different pressure components exerted by massive stars, the photoionized gas associated with the cluster is found to be the dominant feedback mechanism in the cluster. Overall, the massive stars in the cluster might have triggered the birth of young, massive B-type stars in the cluster. This argument is supported with evidence of the observed age gradient between the cluster and the powering sources of the radio clumps.
A Framework for Evaluating Gradient Leakage Attacks in Federated Learning

Federated learning (FL) is an emerging distributed machine learning framework for collaborative model training with a network of clients (edge devices). FL offers default client privacy by allowing clients to keep their sensitive data on local devices and to only share local training parameter updates with the federated server. However, recent studies have shown that even sharing local parameter updates from a client to the federated server may be susceptible to gradient leakage attacks and intrude the client privacy regarding its training data. In this paper, we present a principled framework for evaluating and comparing different forms of client privacy leakage attacks. We first provide formal and experimental analysis to show how adversaries can reconstruct the private local training data by simply analyzing the shared parameter update from local training (e.g., local gradient or weight update vector). We then analyze how different hyperparameter configurations in federated learning and different settings of the attack algorithm may impact on both attack effectiveness and attack cost. Our framework also measures, evaluates, and analyzes the effectiveness of client privacy leakage attacks under different gradient compression ratios when using communication efficient FL protocols. Our experiments also include some preliminary mitigation strategies to highlight the importance of providing a systematic attack evaluation framework towards an in-depth understanding of the various forms of client privacy leakage threats in federated learning and developing theoretical foundations for attack mitigation.
Higher connectivity of the Morse complex

The Morse complex $\mathcal{M}(\Delta)$ of a finite simplicial complex $\Delta$ is the complex of all gradient vector fields on $\Delta$. In particular $\mathcal{M}(\Delta)$ encodes all possible discrete Morse functions (in the sense of Forman) on $\Delta$. In this paper we find sufficient conditions for $\mathcal{M}(\Delta)$ to be connected or simply connected, in terms of certain measurements on $\Delta$. When $\Delta=\Gamma$ is a graph we get similar sufficient conditions for $\mathcal{M}(\Gamma)$ to be $(m-1)$-connected. The main technique we use is Bestvina-Brady discrete Morse theory, applied to a "generalized Morse complex" that is easier to analyze.
Recursive Social Behavior Graph for Trajectory Prediction

Social interaction is an important topic in human trajectory prediction to generate plausible paths. In this paper, we present a novel insight of group-based social interaction model to explore relationships among pedestrians. We recursively extract social representations supervised by group-based annotations and formulate them into a social behavior graph, called Recursive Social Behavior Graph. Our recursive mechanism explores the representation power largely. Graph Convolutional Neural Network then is used to propagate social interaction information in such a graph. With the guidance of Recursive Social Behavior Graph, we surpass state-of-the-art methods on ETH and UCY dataset for 11.1% in ADE and 10.8% in FDE in average, and successfully predict complex social behaviors.
Proactive Aging Mitigation in CGRAs through Utilization-Aware Allocation

Resource balancing has been effectively used to mitigate the long-term aging effects of Negative Bias Temperature Instability (NBTI) in multi-core and Graphics Processing Unit (GPU) architectures. In this work, we investigate this strategy in Coarse-Grained Reconfigurable Arrays (CGRAs) with a novel application-to-CGRA allocation approach. By introducing important extensions to the reconfiguration logic and the datapath, we enable the dynamic movement of configurations throughout the fabric and allow overutilized Functional Units (FUs) to recover from stress-induced NBTI aging. Implementing the approach in a resource-constrained state-of-the-art CGRA reveals 2.2× lifetime improvement with negligible performance overheads and less than 10% increase in area.
Medical Imaging Synthesis using Deep Learning and its Clinical Applications: A Review

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were summarized in the discussion part.
Provably robust deep generative models

Recent work in adversarial attacks has developed provably robust methods for training deep neural network classifiers. However, although they are often mentioned in the context of robustness, deep generative models themselves have received relatively little attention in terms of formally analyzing their robustness properties. In this paper, we propose a method for training provably robust generative models, specifically a provably robust version of the variational auto-encoder (VAE). To do so, we first formally define a (certifiably) robust lower bound on the variational lower bound of the likelihood, and then show how this bound can be optimized during training to produce a robust VAE. We evaluate the method on simple examples, and show that it is able to produce generative models that are substantially more robust to adversarial attacks (i.e., an adversary trying to perturb inputs so as to drastically lower their likelihood under the model).
TetraTSDF: 3D Human Reconstruction From a Single Image With a Tetrahedral Outer Shell

Recovering the 3D shape of a person from its 2D appearance is ill-posed due to ambiguities. Nevertheless, with the help of convolutional neural networks (CNN) and prior knowledge on the 3D human body, it is possible to overcome such ambiguities to recover detailed 3D shapes of human bodies from single images. Current solutions, however, fail to reconstruct all the details of a person wearing loose clothes. This is because of either (a) huge memory requirement that cannot be maintained even on modern GPUs or (b) the compact 3D representation that cannot encode all the details. In this paper, we propose the tetrahedral outer shell volumetric truncated signed distance function (TetraTSDF) model for the human body, and its corresponding part connection network (PCN) for 3D human body shape regression. Our proposed model is compact, dense, accurate, and yet well suited for CNN-based regression task. Our proposed PCN allows us to learn the distribution of the TSDF in the tetrahedral volume from a single image in an end-to-end manner. Results show that our proposed method allows to reconstruct detailed shapes of humans wearing loose clothes from single RGB images.
Textual Visual Semantic Dataset for Text Spotting

Text Spotting in the wild consists of detecting and recognizing text appearing in images (e.g. signboards, traffic signals or brands in clothing or objects). This is a challenging problem due to the complexity of the context where texts appear (uneven backgrounds, shading, occlusions, perspective distortions, etc.). Only a few approaches try to exploit the relation between text and its surrounding environment to better recognize text in the scene. In this paper, we propose a visual context dataset 1 for Text Spotting in the wild, where the publicly available dataset COCO-text [40] has been extended with information about the scene (such as objects and places appearing in the image) to enable researchers to include semantic relations between texts and scene in their Text Spotting systems, and to offer a common framework for such approaches. For each text in an image, we extract three kinds of context information: objects in the scene, image location label and a textual image description (caption). We use state-of-the-art out-of-the-box available tools to extract this additional information. Since this information has textual form, it can be used to leverage text similarity or semantic relation methods into Text Spotting systems, either as a post-processing or in an end-to-end training strategy.
Thermal convection in the crust of the dwarf planet – I. Ceres

Ceres is the largest body in the Main Belt, and it is characterized by a large abundance of water ice in its interior. This feature is suggested by its relatively low bulk density (2162 kg m$^{-3}$), while its partial differentiation into a rocky core and icy crust is suggested by several geological and geochemical features: minerals and salts produced by aqueous alteration, icy patches on the surface, lobate morphology interpreted as surface flows. In this work we explore how the composition can influence the characteristics of thermal convection in the crust of Ceres. Our results suggest that the onset of thermal convection is difficult and when it occurs it is short lived and this could imply that Ceres preserved deep liquid until present, as recent suggested by the work of Castillo-Rogez et al. Moreover, cryovolcanism could be driven by diapirism (chemical convection) rather than thermal convection.
NuSTAR observation of the supergiant fast X-ray transient IGR J11215−5952 during its 2017 outburst

We report on the results of a NuSTAR observation of the supergiant fast X-ray transient pulsar IGR J11215−5952 during the peak of its outburst in June 2017. IGR J11215−5952 is the only SFXT undergoing strictly periodic outbursts (every 165 days). NuSTAR caught several X-ray flares, spanning a dynamic range of 100, and detected X-ray pulsations at 187.0 s, which is consistent with previous measurements. The spectrum from the whole observation is well described by an absorbed power law (with a photon index of 1.4), which is modified, above ∼7 keV, by a cutoff with an e-folding energy of ∼24 keV. A weak emission line is present at 6.4 keV, consistent with Kα emission from cold iron in the supergiant wind. The time-averaged flux is ∼1.5 × 10−10 erg cm−2 s−1 (3−78 keV, corrected for the absorption), translating into an average luminosity of about 9 × 1035 erg s−1 (1–100 keV, assuming a distance of 6.5 kpc). The NuSTAR observation allowed us to perform the most sensitive search for cyclotron resonant scattering features in the hard X-ray spectrum, resulting in no significant detection in any of the different spectral extractions adopted (time-averaged, temporally selected, spin-phase-resolved and intensity-selected spectra). The pulse profile showed an evolution with both the energy (3−12 keV energy range compared with 12−78 keV band) and the X-ray flux: a double-peaked profile was evident at higher fluxes (and in both energy bands), while a single-peaked, sinusoidal profile was present at the lowest intensity state achieved within the NuSTAR observations (in both energy bands). The intensity-selected analysis allowed us to observe an anti-correlation of the pulsed fraction with the X-ray luminosity. The pulse profile evolution can be explained by X-ray photon scattering in the accreting matter above magnetic poles of a neutron star at the quasi-spherical settling accretion stage.