Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining Low-luminosity Gamma-Ray Bursts as Ultra-high-energy Cosmic Ray Sources Using GRB 060218 as a Proxy

We study the connection between low-luminosity gamma-ray bursts (llGRBs) and ultra-high-energy cosmic rays (UHECRs) using the canonical low-luminosity GRB 060218 as a proxy. We focus on the consequential synchrotron emission from electrons that are coaccelerated in the UHECR acceleration region, comparing this emission to observations. Both the prompt and afterglow phases are considered. For the prompt phase, we assume the coaccelerated electrons are injected with a power-law distribution instantaneously (without additional heating or reacceleration), which results in bright optical-UV emission in tension with observations. For the afterglow phase, we constrain the total kinetic energy of the blast wave by comparing electron thermal synchrotron radiation to available radio data at ∼ 3 days. Considering mildly relativistic outflows with bulk Lorentz factor Γ ≳ 2 (slower transrelativistic outflows are not treated), we find that the limited available energy does not allow for GRB 060218-like afterglows to be the main origin of UHECRs. This analysis independently constrains the prompt phase as a major UHECR source as well, given that the prompt energy budget is comparable to that of the afterglow kinetic energy. More generally, our study demonstrates that synchrotron emission from thermal electrons is a powerful diagnostic of the physics of mildly relativistic shocks.
DeepClaw: A Robotic Hardware Benchmarking Platform for Learning Object Manipulation*

We present DeepClaw as a reconfigurable benchmark of robotic hardware and task hierarchy for robot learning. The DeepClaw benchmark aims at a mechatronics perspective of the robot learning problem, which features a minimum design of robot cell that can be easily reconfigured to host robot hardware from various vendors, including manipulators, grippers, cameras, desks, and objects, aiming at a streamlined collection of physical manipulation data and evaluation of the learned skills for hardware benchmarking. We provide a detailed design of the robot cell with readily available parts to build the experiment environment that can host a wide range of robotic hardware commonly adopted for robot learning. We propose a hierarchical pipeline of software integration, including localization, recognition, grasp planning, and motion planning, to streamline learning-based robot control, data collection, and experiment validation towards shareability and reproducibility. We present benchmarking results of the DeepClaw system for a baseline Tic-Tac-Toe task, a bin-clearing task, and a jigsaw puzzle task using three sets of standard robotic hardware. Our results show that tasks defined in DeepClaw can be easily reproduced on three robot cells. Under the same task setup, the differences in robotic hardware used will present a non-negligible impact on the performance metrics of robot learning. All design layouts and codes are hosted on Github for open access (https://github.com/bionicdl-sustech/DeepClaw).
Biogeography of Trimerotropis pallidipennis (Acrididae: Oedipodinae): deep divergence across the Americas

Trimerotropis pallidipennis represents a species complex of band‐winged grasshopper distributed over North and South America. Previous studies indicated a North American origin of the species and suggested that colonization of South America occurred during the Pleistocene after the closure of the Isthmus of Panama. Here we use a phylogeographical approach in order to test different biogeographical scenarios and determine how many distinct units exist within the species complex.
Reliable and Efficient Long-Term Twitter Monitoring

Social media data is now widely used by many academic researchers. However, long-term social media data collection projects, which involve collecting Twitter data from Twitter's public-use APIs, often encounter various issues when they try to collect streaming social media monitoring data from local-area network servers (LANs). In this technical report, we discuss some of the issues that we have encountered in our Twitter data collection project. We present a cloud-based data collection, pre-processing, and archiving infrastructure which we argue mitigates or resolves the problems we have encountered, at minimal cloud-computing costs. We show how this approach works in different cloud computing architectures.
CS-TSSOS: Correlative and Term Sparsity for Large-Scale Polynomial Optimization

This work proposes a new moment-SOS hierarchy, called CS-TSSOS, for solving large-scale sparse polynomial optimization problems. Its novelty is to exploit simultaneously correlative sparsity and term sparsity by combining advantages of two existing frameworks for sparse polynomial optimization. The former is due to Waki et al. [40] while the latter was initially proposed by Wang et al. [42] and later exploited in the TSSOS hierarchy [46, 47]. In doing so we obtain CS-TSSOS—a two-level hierarchy of semidefinite programming relaxations with (i) the crucial property to involve blocks of SDP matrices and (ii) the guarantee of convergence to the global optimum under certain conditions. We demonstrate its efficiency and scalability on several large-scale instances of the celebrated Max-Cut problem and the important industrial optimal power flow problem, involving up to six thousand variables and tens of thousands of constraints.
Integrating Prior Knowledge in Mixed-Initiative Social Network Clustering

We propose a new approach-called PK-clustering-to help social scientists create meaningful clusters in social networks. Many clustering algorithms exist but most social scientists find them difficult to understand, and tools do not provide any guidance to choose algorithms, or to evaluate results taking into account the prior knowledge of the scientists. Our work introduces a new clustering approach and a visual analytics user interface that address this issue. It is based on a process that 1) captures the prior knowledge of the scientists as a set of incomplete clusters, 2) runs multiple clustering algorithms (similarly to clustering ensemble methods), 3) visualizes the results of all the algorithms ranked and summarized by how well each algorithm matches the prior knowledge, 4) evaluates the consensus between user-selected algorithms and 5) allows users to review details and iteratively update the acquired knowledge. We describe our approach using an initial functional prototype, then provide two examples of use and early feedback from social scientists. We believe our clustering approach offers a novel constructive method to iteratively build knowledge while avoiding being overly influenced by the results of often randomly selected black-box clustering algorithms.
Atmospheric sulphur and cloud condensation nuclei in marine air in the Southern Hemisphere

Measurements of atmospheric sulphur species made in Southern Ocean air, at the Cape Grim Baseline Air Pollution Station, are reviewed in an attempt to discern the role played by oceanic emissions of dimethyl sulphide (DMS) as a source of cloud condensation nuclei (CCN). Consistent with conclusions reached by others, our data indicate that the connection between DMS concentration and CCN concentration is neither simple nor direct, being mediated through a range of chemical pathways and intermediate species that are subject to considerable variability over timescales ranging from minutes to months. Physical and meteorological processes are no less important than chemical processes as sources of complexity in the DMS to CCN transformation process. Moreover, the considerable uncertainty that currently exists about both the number of chemical pathways involved in DMS oxidation, and the kinetic coefficients associated with the proposed pathways, make quantitative modelling studies problematic. Nevertheless, synthesis of a large body of data available from Cape Grim and other Southern Ocean sites does permit some refinement of our understanding of the DMS–CCN connection. Here, these data are employed to illustrate the current state of knowledge about the connections between DMS, CCN and cloud properties at Cape Grim, and to highlight the many complexities that underlie these connections.
Deep xVA Solver – A Neural Network Based Counterparty Credit Risk Management Framework

In this paper, we present a novel computational framework for portfolio-wide risk management problems, where the presence of a potentially large number of risk factors makes traditional numerical techniques ineffective. The new method utilises a coupled system of BSDEs for the valuation adjustments (xVA) and solves these by a recursive application of a neural network based BSDE solver. This not only makes the computation of xVA for high-dimensional problems feasible, but also produces hedge ratios and dynamic risk measures for xVA, and allows simulations of the collateral account.
Phylogenetics of Lythraceae sensu lato: A Preliminary Analysis Based on Chloroplast rbcL Gene, psaA‐ycf3 Spacer, and Nuclear rDNA Internal Transcribed Spacer (ITS) Sequences

Phylogenetic relationships of Lythraceae sensu lato (s.l.) were investigated by parsimony and likelihood analyses of 85 accessions representing 23 species, 16 genera that have been assigned to the family at various times. The three data sets, including the chloroplast rbcL gene, the psaA‐ycf3 spacer, and the nuclear internal transcribed spacer (ITS; including the 5.8S ribosomal gene), are highly congruent on the basis of the partition homogeneity test. No significantly long branches are detected by relative apparent synapomorphy analysis. Phylogenetic analyses based on the combined data strongly support the monophyly of the Lythraceae s.l. , in which the satellite genera Dubanga, Punica, Sonneratia, and Trapa were included, with Onagraceae and Combretaceae as outgroups. Paraphyly of subfamily Lythroideae (= Lythraceae sensu stricto) is proposed with the other four monotypic subfamilies nested within. The analysis further supports the sister relationship between Sonneratia and Trapa instead of Duabanga and Sonneratia, the latter of which have traditionally been treated as constituting the ditypic family Sonneratiaceae sensu Engl. & Gilg. The main clades within Lythraceae s.l. exhibit relatively weak support. Terminal relationships among genera, however, have strong bootstrap support.
Abondance de l’épervier brun en migration automnale : relations avec l’oscillation nord-atlantique et l’abondance de ses proies

La foret boreale se caracterise par une diversite de cycles de productivite qui affectent les chaines trophiques, lesquelles sont en partie affectees par le climat. Nous avons analyse les donnees de l’Observatoire d’oiseaux de Tadoussac pour l’epervier brun (Accipiter striatus) entre 1996 et 2010 en lien avec l’indice climatique de l’Oscillation nord-atlantique (ONA), les precipitations estivales et l’abondance de proies potentielles. A l’automne, les eperviers bruns juveniles ont migre plus tot que les adultes et les deplacements des 2 groupes ne se sont pas chevauches. L’abondance automnale de l’epervier brun, principalement celle des juveniles, etait positivement correlee avec l’indice climatique de l’ONA de l’hiver precedent, et negativement avec les precipitions des mois de juin et juillet, lesquelles sont reliees a l’indice de l’ONA. De plus, nos resultats ont revele des correlations positives entre l’abondance de l’epervier brun lors de la migration d’automne et l’abondance de proies potentielles en periode estivale, principalement celle de la grive a dos olive (Catharus ustulatus) et du junco ardoise (Junco hyemalis). La correlation positive entre l’abondance d’eperviers bruns et l’ONA de l’hiver precedent suggere que le climat influence probablement la dynamique de population de l’espece. Les hivers doux et enneiges sont souvent suivis d’un ete pluvieux, qui se caracterise par une production moindre de jeunes eperviers. Cela semble lie a la disponibilite de proies pour les eperviers adultes puisque les proies principales sont egalement moins nombreuses lors d’etes avec une pluviosite superieure a la normale.
Efficient Bird Eye View Proposals for 3D Siamese Tracking

Tracking vehicles in LIDAR point clouds is a challenging task due to the sparsity of the data and the dense search space. The lack of structure in point clouds impedes the use of convolution filters usually employed in 2D object tracking. In addition, structuring point clouds is cumbersome and implies losing fine-grained information. As a result, generating proposals in 3D space is expensive and inefficient. In this paper, we leverage the dense and structured Bird Eye View (BEV) representation of LIDAR point clouds to efficiently search for objects of interest. We use an efficient Region Proposal Network and generate a small number of object proposals in 3D. Successively, we refine our selection of 3D object candidates by exploiting the similarity capability of a 3D Siamese network. We regularize the latter 3D Siamese network for shape completion to enhance its discrimination capability. Our method attempts to solve both for an efficient search space in the BEV space and a meaningful selection using 3D LIDAR point cloud. We show that the Region Proposal in the BEV outperforms Bayesian methods such as Kalman and Particle Filters in providing proposal by a significant margin and that such candidates are suitable for the 3D Siamese network. By training our method end-to-end, we outperform the previous baseline in vehicle tracking by 12% / 18% in Success and Precision when using only 16 candidates.
Data Augmentation via Mixed Class Interpolation using Cycle-Consistent Generative Adversarial Networks Applied to Cross-Domain Imagery

Machine learning driven object detection and classification within non-visible imagery has an important role in many fields such as night vision, all-weather surveillance and aviation security. However, such applications often suffer due to the limited quantity and variety of non-visible spectral domain imagery, in contrast to the high data availability of visible-band imagery that readily enables contemporary deep learning driven detection and classification approaches. To address this problem, this paper proposes and evaluates a novel data augmentation approach that leverages the more readily available visible-band imagery via a generative domain transfer model. The model can synthesise large volumes of non-visible domain imagery by image-to-image (I2I) translation from the visible image domain. Furthermore, we show that the generation of interpolated mixed class (non-visible domain) image examples via our novel Conditional CycleGAN Mixup Augmentation (C2GMA) methodology can lead to a significant improvement in the quality of non-visible domain classification tasks that otherwise suffer due to limited data availability. Focusing on classification within the Synthetic Aperture Radar (SAR) domain, our approach is evaluated on a variation of the Statoil/C-CORE Iceberg Classifier Challenge dataset and achieves 75.4 % accuracy, demonstrating a significant improvement when compared against traditional data augmentation strategies (Rotation, Mixup, and MixCycleGAN).
Nitrogenous components of cecal contents in fasted chickens.

絶食時にも盲腸糞の排泄がかなりあることから,盲腸内容物の成分,とくに窒素成分が給食時とどのように異なるかを,60日齢の単冠白色レグホーン種雄を用いて検討し,盲腸内容物の由来を明らかにする手掛りを得ようとした. 1) 24時間絶食し,盲腸内容物中の全窒素を調査した結果,その含量は給食時よりも高い傾向を示した. 2) 給食時および絶食時における盲腸内容物と小腸遠位端内容物中の各窒素成分(アンモア態窒素,尿酸態窒素,尿素態窒素)を経時的に調査した結果,盲腸内容物は各窒素成分ともに給食時の小腸遠位端内容物よりも数倍高い値を示した. なお絶食時の小腸遠位端には測定できる量の内容物が存在しなかった. 3) 盲腸内容物中の窒素成分は給食時および絶食時ともに日内変動がみられたが,絶食あるいは盲腸糞排泄による特異的な差異はなく,また人工肛門設着鶏と通常鶏の盲腸内容物中の窒素成分量にも差がみられなかった. 以上の結果から,盲腸内容物中の窒素成分は内因的な分泌物に由来する部分が大きいものと推論される.
CCAAT/Enhancer Binding Proteins Beta and Delta Regulate α1-Acid Glycoprotein Gene Expression in Rat Intestinal Epithelial Cells

Isoforms of CCAAT/enhancer binding protein (C/EBP) are expressed in rodent intestine as well as in the rat intestinal epithelial cell line IEC-6. However, no specific roles have been attributed to these isoforms in intestinal epithelial cells. To determine whether C/EBP family members could be implicated in the regulation of acute-phase response gene expression in intestinal epithelial cells, we have studied the effect of glucocorticoids on expression of the α1-acid glycoprotein gene and C/EBP isoforms in IEC-6 cells. Glucocorticoids induced α1-acid glycoprotein gene expression in these cells. This induction coincided with an increase of DNA-binding capacity of both C/EBPβ and C/EBPδ to the B1 and B2 C/EBP-interacting sites localized in the rat AGP promoter. Transforming growth factor beta, (TGFβ), a cytokine involved in the transcriptional regulation of several acute-phase plasma proteins, antagonized the glucocorticoid-dependent induction of α1-acid glycoprotein gene expression. In parallel, TGFβ downre.
An Overview of Self-Similar Traffic: Its Implications in the Network Design

The knowledge about the true nature of the traffic in computer networking is a key requirement in the design of such networks. The phenomenon of self-similarity is a characteristic of the traffic of current client/server packet networks in LAN/WAN environments dominated by network technologies such as Ethernet and the TCP/IP protocol stack. The development of networks traffic simulators, which take into account this attribute, is necessary for a more realistic description the traffic on these networks and their use in the design of resources (contention elements) and protocols of flow control and network congestion. In this scenario it is recommended do not adopt standard traffic models of the Poisson type.
Robot Errors in Proximate HRI

Advancements within human–robot interaction generate increasing opportunities for proximate, goal-directed joint action (GDJA). However, robot errors are common and researchers must determine how to mitigate them. In this article, we examine how expectations for robot functionality affect people’s perceptions of robot reliability and trust for a robot that makes errors. Here 35 participants (n = 35) performed a collaborative banner-hanging task with an autonomous mobile manipulator (Toyota HSR). Each participant received either a low- or high-functionality framing for the robot. We then measured how participants perceived the robot’s reliability and trust prior to, during, and after interaction. Functionality framing changed how robot errors affected participant experiences of robot behavior. People with low expectations experienced positive changes in reliability and trust after interacting with the robot, while those with high expectations experienced a negative change in reliability and no change in trust. The low-expectation group also showed greater trust recovery following the robot’s first error compared to the high group. Our findings inform human–robot teaming through: (1) identifying robot presentation factors that can be employed to facilitate trust calibration and (2) establishing the effects of framing, functionality, and the interactions between them to improve dynamic models of human–robot teaming.
DEEPEYE

Video object detection and action recognition typically require deep neural networks (DNNs) with huge number of parameters. It is thereby challenging to develop a DNN video comprehension unit in resource-constrained terminal devices. In this article, we introduce a deeply tensor-compressed video comprehension neural network, called DEEPEYE, for inference on terminal devices. Instead of building a Long Short-Term Memory (LSTM) network directly from high-dimensional raw video data input, we construct an LSTM-based spatio-temporal model from structured, tensorized time-series features for object detection and action recognition. A deep compression is achieved by tensor decomposition and trained quantization of the time-series feature-based LSTM network. We have implemented DEEPEYE on an ARM-core-based IOT board with 31 FPS consuming only 2.4W power. Using the video datasets MOMENTS, UCF11 and HMDB51 as benchmarks, DEEPEYE achieves a 228.1× model compression with only 0.47% mAP reduction; as well as 15k× parameter reduction with up to 8.01% accuracy improvement over other competing approaches.
Generating Representative Test Sequences from Real Workload for Minimizing DRAM Verification Overhead

Dynamic Random Access Memory (DRAM) standards have evolved for higher bandwidth, larger capacity, and lower power consumption, so their specifications have become complicated to satisfy the design goals. These complex implementations have significantly increased the test time overhead for design verification; thus, a tremendous amount of command sequences are used. However, since the sequences generated by real machines or memory simulators are the results of scheduling for high performance, they result in low test coverage with repetitive patterns. Eventually, various workloads should be applied to increase the coverage, but this approach incurs significant test time overhead. A few preliminary studies have been proposed to generate predefined or random sequences to cover various test cases or increase test coverage. However, they have limitations in representing various memory behaviors of real workloads. In this article, we define a performance metric for estimating the test coverage when using command sequences. Then, our experiment shows that the coverage of a real machine and a simulator is low and similar. Also, the coverage patterns are almost the same in all tested benchmarks. To alleviate the problem, we propose a test-oriented command scheduling algorithm that increases the test coverage while preserving the memory behaviors of workloads and reducing the test time overhead by extracting representative sequences based on the similarity between command sequences. For the sequence extraction and the coverage estimation, our test sequences are embedded into vectors using bag-of-Ngrams. Compared to the simulator, our algorithm achieves 2.94x higher coverage while reducing the test overhead to 7.57%.
Evaluation of genetic diversity and relationshipsin orchardgrass (Dactylis glomerata L.) germplasm based on SRAP markers

The present study is the first report of characterizing the levels and patterns ofgenetic diversity in 60 orchardgrass accessions from four continents by sequence-related amplified polymorphism (SRAP) markers. Twenty-one primer pairs were used to produce 480 bands, of which 405 (84.38%) were polymorphic. The genetic similarity coeffic ients (GS) varied from 0.5863 to 0.9686 among the 60 collections, with an average of 0.7891. The genetic diversity of orchardgrass from China and the United States of America were found to be higher than that found in other countries. The dendrogram and principal component analysis realized from these markers clustered the materials into four main groups. The cluster analysis showed that orchardgrass from Australia was different from other collections in genetic diversity. Accessions from the same continent were classified into the same group, indicating that the genetic diversity of orchardgrass and the entire genetic basis of cultivars used in a continent is narrow. Furthe.