Miracle-dz/UniFAR-FTUs huggingface dataset:
One-Shot Federated Learning with Neuromorphic Processors

Being very low power, the use of neuromorphic processors in mobile devices to solve machine learning problems is a promising alternative to traditional Von Neumann processors. Federated Learning enables entities such as mobile devices to collaboratively learn a shared model while keeping their training data local. Additionally, federated learning is a secure way of learning because only the model weights need to be shared between models, keeping the data private. Here we demonstrate the efficacy of federated learning in neuromorphic processors. Neuromorphic processors benefit from the collaborative learning, achieving state of the art accuracy on a one-shot learning gesture recognition task across individual processor models while preserving local data privacy.
Modelling size distributions of marine plastics under the influence of continuous cascading fragmentation

Field studies in the global ocean have shown that plastic fragments make up the majority of plastic pollution in terms of abundance. It is not well understood how quickly plastics in the marine environmental fragment, however. Here, we study the fragmentation process in the oceanic environment by considering a model which captures continuous fragmentation of particles over time in a cascading fashion. With this cascading fragmentation model we simulate particle size distributions (PSDs), specifying the abundance or mass of particles for different size classes. The fragmentation model is coupled to an environmental box model, simulating the distributions of plastic particles in the ocean, coastal waters, and on the beach. We demonstrate the capabilities of the model by calibrating it to estimated plastic transport in the Mediterranean Sea, and compare the modelled PSDs to available observations in this region. Results are used to illustrate the effect of size-selective processes such as vertical mixing in the water column and resuspension of particles from the beach into coastal waters. The model quantifies the role of fragmentation on the marine plastic mass budget: while fragmentation is a major source of secondary plastic particles in terms of abundance, it seems to have a minor effect on the total mass of particles larger than 0.1 mm. Future comparison to observed PSD data allow us to understand size-selective plastic transport in the environment, and potentially inform us on plastic longevity.
Instability of de Sitter space under thermal radiation in different vacua

We study the instability of de Sitter space-time (dS) under thermal radiation in different vacua. We argue that the mode function solution of a scalar field in four-dimensional dS can be separated into the incoming and outgoing modes. Different vacua for dS are realized by different combinations of positive frequency modes assigned to each solution. For a minimally coupled massless scalar field, we explicitly compute the behavior of the mode function and the corresponding energy-momentum tensor in the Unruh vacuum near the horizon, and find that the horizon area increases (decreases) in time when the incoming (outgoing) mode contributes to thermal flux.
"You eat with your eyes first": Optimizing Yelp Image Advertising

A business's online, photographic representation can play a crucial role in its success or failure. We use Yelp's image dataset and star-based review system as a measurement of an image's effectiveness in promoting a business. After preprocessing the Yelp dataset, we use transfer learning to train a classifier which accepts Yelp images and predicts star-ratings. Additionally, we then train a GAN to qualitatively investigate the common properties of highly effective images. We achieve 90-98% accuracy in classifying simplified star ratings for various image categories and observe that images containing blue skies, open surroundings, and many windows are correlated with higher Yelp reviews.
RIESZ TRANSFORMS ON COMPACT QUANTUM GROUPS AND STRONG SOLIDITY

Abstract One of the main aims of this paper is to give a large class of strongly solid compact quantum groups. We do this by using quantum Markov semigroups and noncommutative Riesz transforms. We introduce a property for quantum Markov semigroups of central multipliers on a compact quantum group which we shall call ‘approximate linearity with almost commuting intertwiners’. We show that this property is stable under free products, monoidal equivalence, free wreath products and dual quantum subgroups. Examples include in particular all the (higher-dimensional) free orthogonal easy quantum groups. We then show that a compact quantum group with a quantum Markov semigroup that is approximately linear with almost commuting intertwiners satisfies the immediately gradient- ${\mathcal {S}}_2$ condition from [10] and derive strong solidity results (following [10]). Using the noncommutative Riesz transform we also show that these quantum groups have the Akemann–Ostrand property; in particular, the same strong solidity results follow again (now following [27]).
Manifold Sampling for Optimizing Nonsmooth Nonconvex Compositions

We propose a manifold sampling algorithm for minimizing a nonsmooth composition $f= h\circ F$, where we assume $h$ is nonsmooth and may be inexpensively computed in closed form and $F$ is smooth but its Jacobian may not be available. We additionally assume that the composition $h\circ F$ defines a continuous selection. Manifold sampling algorithms can be classified as model-based derivative-free methods, in that models of $F$ are combined with particularly sampled information about $h$ to yield local models for use within a trust-region framework. We demonstrate that cluster points of the sequence of iterates generated by the manifold sampling algorithm are Clarke stationary. We consider the tractability of three particular subproblems generated by the manifold sampling algorithm and the extent to which inexact solutions to these subproblems may be tolerated. Numerical results demonstrate that manifold sampling as a derivative-free algorithm is competitive with state-of-the-art algorithms for nonsmooth optimization that utilize first-order information about $f$.
Frequency‐Independent Terahertz Anomalous Hall Effect in DyCo5, Co32Fe68, and Gd27Fe73 Thin Films from DC to 40 THz

The anomalous Hall effect (AHE) is a fundamental spintronic charge‐to‐charge‐current conversion phenomenon and closely related to spin‐to‐charge‐current conversion by the spin Hall effect. Future high‐speed spintronic devices will crucially rely on such conversion phenomena at terahertz (THz) frequencies. Here, it is revealed that the AHE remains operative from DC up to 40 THz with a flat frequency response in thin films of three technologically relevant magnetic materials: DyCo5, Co32Fe68, and Gd27Fe73. The frequency‐dependent conductivity‐tensor elements σxx and σyx are measured, and good agreement with DC measurements is found. The experimental findings are fully consistent with ab initio calculations of σyx for CoFe and highlight the role of the large Drude scattering rate (≈100 THz) of metal thin films, which smears out any sharp spectral features of the THz AHE. Finally, it is found that the intrinsic contribution to the THz AHE dominates over the extrinsic mechanisms for the Co32Fe68 sample. The results imply that the AHE and related effects such as the spin Hall effect are highly promising ingredients of future THz spintronic devices reliably operating from DC to 40 THz and beyond.
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?

Emotion recognition is predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory (e.g., fear, joy, anger, disgust, sadness, surprise, trust, anticipation). More recently, semantic role labeling approaches have been developed to extract structures from the text to answer questions like: “who is described to feel the emotion? ” (experiencer), “what causes this emotion? ” (stimulus), and at which entity is it directed? ” (target). Though it has been shown that jointly modeling stimulus and emotion category prediction is beneficial for both subtasks, it remains unclear which of these semantic roles enables a classifier to infer the emotion. Is it the experiencer, because the identity of a person is biased towards a particular emotion (X is always happy)? Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)? We answer these questions by training emotion classification models on five available datasets annotated with at least one semantic role by masking the fillers of these roles in the text in a controlled manner and find that across multiple corpora, stimuli and targets carry emotion information, while the experiencer might be considered a confounder. Further, we analyze if informing the model about the position of the role improves the classification decision. Particularly on literature corpora we find that the role information improves the emotion classification.
Distributed Machine Learning for Computational Engineering using MPI

We propose a framework for training neural networks that are coupled with partial differential equations (PDEs) in a parallel computing environment. Unlike most distributed computing frameworks for deep neural networks, our focus is to parallelize both numerical solvers and deep neural networks in forward and adjoint computations. Our parallel computing model views data communication as a node in the computational graph for numerical simulation. The advantage of our model is that data communication and computing are cleanly separated and thus provide better flexibility, modularity, and testability. We demonstrate using various large-scale problems that we can achieve substantial acceleration by using parallel solvers for PDEs in training deep neural networks that are coupled with PDEs.
Where am I? SLAM for Mobile Machines on A Smart Working Site

The current optimization approaches of construction machinery are mainly based on internal sensors. However, the decision of a reasonable strategy is not only determined by its intrinsic signals, but also very strongly by environmental information, especially the terrain. Due to the dynamic changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare. In this study, we proposed an affordable SLAM method to create a multi-layer grid map for the construction site so that the machine can have the environmental information and be optimized accordingly. Concretely, after the machine passes by the grid, we can obtain the local information and record it. Combining with positioning technology, we then create a map of the interesting places of the construction site. As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of one IMU and two differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3% in the harsh environment. As an outlook, our SLAM technology provides the cornerstone to activate many efficiency improvement approaches.
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning Over Large-Scale Graphs

Graph Neural Networks (GNNs) have led to state-of-the-art performance on a variety of machine learning tasks such as recommendation, node classification and link prediction. Graph neural network models generate node embeddings by merging nodes features with the aggregated neighboring nodes information. Most existing GNN models exploit a single type of aggregator (e.g., mean-pooling) to aggregate neighboring nodes information, and then add or concatenate the output of aggregator to the current representation vector of the center node. However, using only a single type of aggregator is difficult to capture the different aspects of neighboring information and the simple addition or concatenation update methods limit the expressive capability of GNNs. Not only that, existing supervised or semi-supervised GNN models are trained based on the loss function of the node label, which leads to the neglect of graph structure information. In this paper, we propose a novel graph neural network architecture, Graph Attention & Interaction Network (GAIN), for inductive learning on graphs. Unlike the previous GNN models that only utilize a single type of aggregation method, we use multiple types of aggregators to gather neighboring information in different aspects and integrate the outputs of these aggregators through the aggregator-level attention mechanism. Furthermore, we design a graph regularized loss to better capture the topological relationship of the nodes in the graph. Additionally, we first present the concept of graph feature interaction and propose a vector-wise explicit feature interaction mechanism to update the node embeddings. We conduct comprehensive experiments on two node-classification benchmarks and a real-world financial news dataset. The experiments demonstrate our GAIN model outperforms current state-of-the-art performances on all the tasks.
Applying clock comparison methods to pulsar timing observations

Frequency metrology outperforms any other branch of metrology in accuracy (parts in 10−16) and small fluctuations (<10−17). In turn, among celestial bodies, the rotation speed of millisecond pulsars is by far the most stable (<10−18). Therefore, the precise measurement of the time of arrival (TOA) of pulsar signals is expected to disclose information about cosmological phenomena, and to enlarge our astrophysical knowledge. Related to this topic, Pulsar Timing Array projects have been developed and operated for the last decades. The TOAs from a pulsar can be affected by local emission and environmental effects, in the direction of the propagation through the interstellar medium or universally by gravitational waves from super massive black hole binaries. These effects (signals) can manifest as a low-frequency fluctuation over time, phenomenologically similar to a red noise, while the remaining pulsar intrinsic and instrumental background (noise) are white. This article focuses on the frequency metrology of pulsars. From our standpoint, the pulsar is an accurate clock, to be measured simultaneously with several telescopes in order to reject the uncorrelated white noise. We apply the modern statistical methods of time-and-frequency metrology to simulated pulsar data, and we show the detection limit of the correlated red noise signal between telescopes.
Effect of Lactobacillus plantarum ACTT 8014 on 5-fluorouracil induced intestinal mucositis in Wistar rats

Some previous studies reported that probiotics might decrease the severity of chemotherapy-induced mucositis. This study assessed the potential protective effect of Lactobacillus plantarum ATCC 8014 on 5-fluorouracil (5-FU) induced intestinal mucositis in the Wistar rats. The Crl:WI rats were divided into two groups of six animals (F, L) and one group of 5 animals (M). Group L received for 9 days 3.32x109 CFU/ml of Lactobacillus plantarum orally. In the 7th day of the experiment 400 mg of 5-FU was administered intraperitoneally in groups L and F. Group M received only the vehicles. All animals were sacrificed in the 9th day. Eleven histological characteristics of mucositis were quantified from 0 (normal) to 3 (severe) for duodenum, jejunum and colon. Semiquantitative grades measured Toll-like receptor 4 (TLR4) immunopositive cells. The independent groups were analyzed using the Kruskal-Wallis test, Mann-Whitney U test, with a Bonferroni correction for alpha (P≤0.016). In the group F, treated with 5-FU, the most affected areas were the jejunum and the duodenum. The medium score of histological lesions was 27 for jejunum (minimum 25, maximum 32) and 21 for duodenum (minimum 18, maximum 29). Graded microscopic mucosal changes of the jejunum were significantly lower in group L compared with group F (U=0, P=0.009, Mann-Whitney test). The histological changes depicted on the duodenal and colonic mucosa were less severe in group L than in group F, but without reaching the statistical significance (duodenum: U=6, P=0.172, Mann-Whitney test; colon: U=12, P=0.916, Mann-Whitney test). Although the TLR4 immunoexpression was more intense in group L, no significant statistical difference was revealed at duodenum, jejunum or colonic mucosa. Significantly fewer microscopic changes were depicted in L group on the jejunum, suggesting a potential beneficial effect of Lactobacillus plantarum at this level in 5-FU induced mucositis.
Evaluations of Commercial Sleep Technologies for Objective Monitoring During Routine Sleeping Conditions

Purpose The commercial market is saturated with technologies that claim to collect proficient, free-living sleep measurements despite a severe lack of independent third-party evaluations. Therefore, the present study evaluated the accuracy of various commercial sleep technologies during in-home sleeping conditions. Materials and Methods Data collection spanned 98 separate nights of ad libitum sleep from five healthy adults. Prior to bedtime, participants utilized nine popular sleep devices while concurrently wearing a previously validated electroencephalography (EEG)-based device. Data collected from the commercial devices were extracted for later comparison against EEG to determine degrees of accuracy. Sleep and wake summary outcomes as well as sleep staging metrics were evaluated, where available, for each device. Results Total sleep time (TST), total wake time (TWT), and sleep efficiency (SE) were measured with greater accuracy (lower percent errors) and limited bias by Fitbit Ionic [mean absolute percent error, bias (95% confidence interval); TST: 9.90%, 0.25 (−0.11, 0.61); TWT: 25.64%, −0.17 (−0.28, −0.06); SE: 3.49%, 0.65 (−0.82, 2.12)] and Oura smart ring [TST: 7.39%, 0.19 (0.04, 0.35); TWT: 36.29%, −0.18 (−0.31, −0.04); SE: 5.42%, 1.66 (0.17, 3.15)], whereas all other devices demonstrated a propensity to over or underestimate at least one if not all of the aforementioned sleep metrics. No commercial sleep technology appeared to accurately quantify sleep stages. Conclusion Generally speaking, commercial sleep technologies displayed lower error and bias values when quantifying sleep/wake states as compared to sleep staging durations. Still, these findings revealed that there is a remarkably high degree of variability in the accuracy of commercial sleep technologies, which further emphasizes that continuous evaluations of newly developed sleep technologies are vital. End-users may then be able to determine more accurately which sleep device is most suited for their desired application(s).
Residents in a Remote Island Having Family Members in Distant Areas Showed Higher Preference for Place of End-of-Life Care: The Ajishima Study

Introduction: To investigate the proportion of those having preferred place for end-of-life care among residents in a remote island and its association with family composition. Methods: Cross-sectional questionnaire survey was conducted in Ajishima, an island 23 km away from the coast of Ishinomaki City, northeast of Japan. Between October 2017 and February 2018, the questionnaire was distributed to 288 eligible residents and 113 valid responses were analyzed. Primary outcome was whether the subjects had preferred place for end-of-life care. The explanatory variable was family composition defined as whether having family members inside or outside the island [none (In−/Out−), only inside the island (In+/Out−), only outside the island (In−/Out+), and both inside and outside (In+/Out+)]. Poisson regression analysis was used to calculate the prevalence ratios (PRs) and 95% confidence intervals (CIs) of showing preferred place in each group. Results: The proportion of those having preferred place for end-of-life care was 72.6% in total. This rate significantly differed across family composition groups: 67.6%, 40.0%, and 82.9% for In+/Out+, In+/Out−, and In−/Out+ groups, respectively. The PR (95%CI) of having preferred place was 0.66 (0.33, 1.36) and 1.26 (1.01, 1.56) for In+/Out− and In−/Out+ groups, respectively, compared with In+/Out+ group. Conclusions: This study showed that significantly higher preference for place of end-of-life care was seen among residents who had family members only outside the island compared with those who had families both inside and outside. Health care professionals should consider family compositions when initiating end-of-life discussion to residents in remote areas.
Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis

Objective The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.
Pseudodiaptomus marinus Sato, 1913 in the Black Sea: morphology, genetic analysis, and variability in seasonal and interannual abundance

Calanoid copepod Peudodiaptomus marinus Sato, 1913 was first recorded in Sevastopol Bay in the northern Black Sea in September 2016. We performed regular observations of this new invasive species between October 2016 and December 2018. We conducted bi-weekly plankton sampling at three stations located within or adjacent to Sevastopol Bay. This is the first paper to combine (i) a detailed morphological study, (ii) molecular genetic analysis, and (iii) an investigation of P. marinus seasonal dynamics and interannual abundance variability in the coastal Black Sea. Our morphological research showed similarities between Pseudodiaptomus specimens and existing P. marinus illustrations and descriptions. Our morphological analysis results were confirmed using molecular genetic studies. Based on the genetic variability of ITS2 and cytb, we found that all investigated specimens from Sevastopol Bay belonged to P. marinus. Investigations of P. marinus seasonal and interannual abundance variability showed the same seasonal patterns throughout the studied period, with a higher seasonal abundance from October to November and one pronounced density peak in autumn. The highest abundances (2,000 ind m–3 at the mouth of the bay and more than 5,000 ind m–3at its centre) were recorded in November 2018. In the samples, we found adults of both sexes, including ovigerous females, copepodites I–V, and nauplii, suggesting that the species reproduce in Sevastopol Bay. Our research indicated that P. marinus is a new non-indigenous species (NIS) in the Black Sea, and we will discuss a possible vector of its introduction into this basin.