Miracle-dz/UniFAR-FTUs huggingface dataset:
Methicillin and Vancomycin Resistant Staphylococcus aureus and Associated Factors from Surgical Ward Inpatients at Debre Markos Referral Hospital, Northwest Ethiopia

Introduction Staphylococcus aureus is a well-known superbug and leading causes of wound infections. The clinical epidemiology of methicillin resistant S. aureus (MRSA) and vancomycin resistant S. aureus (VRSA) is not well documented in Ethiopia. The aim of this study was to determine the proportion of MRSA, VRSA and associated factors from surgical inpatients in Debre Markos Referral Hospital (DMRH), Northwest Ethiopia. Methods A facility-based cross-sectional study was conducted from February to April 2020 at DMRH. A structured questionnaire was used to gather demographic and clinical data. Wound swab was collected from inpatients and then inoculated on blood agar and mannitol salt agar. The presence of MRSA and VRSA was determined using the cefoxitin (30 μg) antibiotic disk diffusion and vancomycin E-test methods, respectively. The data were analyzed using SPSS 20. Data were analyzed using bivariate and multivariate logistic regression analysis. A P-value <0.05 was considered statistically significant. Results A total of 242 wound cases were enrolled and the majority of them were males 172 (71.1%). Among the total enrolled cases, S. aureus was identified from 71 (29.3%) of the admitted patients. The proportion of MRSA was 32 (13.22%) and that of VRSA was 4.1%. The proportion of vancomycin intermediate S. aureus (VISA) was gauged at 4.5%. Hospital stay over 72 hrs, wound depth, current antibiotic use, and previous history of wound infection showed statistically significant association with MRSA. On the contrary, VRSA did not showed any significant association against the analyzed variables. Conclusion High proportions of S. aureus isolates became MRSA; resistant to all β-lactam antimicrobial agents excluding newer cephalosporin. In addition, the proportion of VRSA/VISA was also high. Multiple variables demonstrated significant associations with MRSA. Hence, intervention measures for MRSA risk groups must be in place. Furthermore, hospital infection control and an antibiotic stewardship program should be strengthened.
Role of Mean Platelet Volume in the Prognosis of Gallbladder Carcinoma: A Tertiary Centre Experience

Mean platelet volume (MPV) is an inflammatory marker indicative of platelet activation. There are several studies that suggest an association between the neoplastic process and cancer metastasis. We performed a retrospective analysis to investigate the role of MPV as a prognostic informative marker in gallbladder cancer. This study included 73 patients who underwent treatment for gallbladder cancer with curative or palliative intent. MPV was obtained and statistically analysed to investigate the association between the nodal status (N), the overall stage as per the American Joint Committee on Cancer (AJCC) staging system, perineural invasion, and differentiation of the tumor. The statistical analysis was done using SPSS Statistics, version 23 (IBM Corp., Armonk, NY). We found that the MPV values were significantly high in node-positive cases (OR = 3.623, 95% CI = 7.778-1.687, p value = −0.0001), cases in the advanced stage (OR = 3.623, 95% CI = 7.778-1.687, p value = 0.0001), cases with perineural invasion (OR = 3.396, 95% CI = 8.319-1.387, p value = −0.0001), and poor differentiation (OR = 2.327, 95% CI = 4.651-1.164, p value = −0.002 ). MPV is an inexpensive and convenient inflammatory marker that correlates with nodal positivity in the staging and prognostication of gallbladder cancer. This marker can be used to ascertain the risk status of gallbladder cancer.
ME-PCN: Point Completion Conditioned on Mask Emptiness

Point completion refers to completing the missing geometries of an object from incomplete observations. Mainstream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details. In this work, we present MEPCN, a point completion network that leverages emptiness in 3D shape space. Given a single depth scan, previous methods often encode the occupied partial shapes while ignoring the empty regions (e.g. holes) in depth maps. In contrast, we argue that these ‘emptiness’ clues indicate shape boundaries that can be used to improve topology representation and detail granularity on surfaces. Specifically, our ME-PCN encodes both the occupied point cloud and the neighboring ‘empty points’. It estimates coarse-grained but complete and reasonable surface points in the first stage, followed by a refinement stage to produce fine-grained surface details. Comprehensive experiments verify that our ME-PCN presents better qualitative and quantitative performance against the state-of-the-art. Besides, we further prove that our ‘emptiness’ design is lightweight and easy to embed in existing methods, which shows consistent effectiveness in improving the CD and EMD scores.
Measurements of Photospheric and Chromospheric Magnetic Field Structures Associated with Chromospheric Heating over a Solar Plage Region

In order to investigate the relation between magnetic structures and the signatures of heating in plage regions, we observed a plage region with the He i 1083.0 nm and Si i 1082.7 nm lines on 2018 October 3 using the integral field unit mode of the GREGOR Infrared Spectrograph (GRIS) installed at the GREGOR telescope. During the GRIS observation, the Interface Region Imaging Spectrograph obtained spectra of the ultraviolet Mg ii doublet emitted from the same region. In the periphery of the plage region, within the limited field of view seen by GRIS, we find that the Mg ii radiative flux increases with the magnetic field in the chromosphere with a factor of proportionality of 2.38 × 104 erg cm−2 s−1 G−1. The positive correlation implies that magnetic flux tubes can be heated by Alfvén wave turbulence or by collisions between ions and neutral atoms relating to Alfvén waves. Within the plage region itself, the radiative flux was large between patches of strong magnetic field strength in the photosphere or at the edges of magnetic patches. On the other hand, we do not find any significant spatial correlation between the enhanced radiative flux and the chromospheric magnetic field strength or the electric current. In addition to the Alfvén wave turbulence or collisions between ions and neutral atoms relating to Alfvén waves, other heating mechanisms related to magnetic field perturbations produced by interactions of magnetic flux tubes could be at work in the plage chromosphere.
Design and Analysis of Modular Pipe Climber-III with a Multi-Output Differential Mechanism

This paper presents the design of an in-pipe climbing robot that operates using a novel ‘Three-output open differential’(3-OOD) mechanism to traverse complex networks of pipes. Conventional wheeled/tracked in-pipe climbing robots are prone to slip and drag while traversing in pipe bends. The 3-OOD mechanism helps in achieving the novel result of eliminating slip and drag in the robot tracks during motion. The proposed differential realizes the functional abilities of the traditional two-output differential, which is achieved the first time for a differential with three outputs. The 3-OOD mechanism mechanically modulates the track speeds of the robot based on the forces exerted on each track inside the pipe network, by eliminating the need for any active control. The simulation of the robot traversing in the pipe network in different orientations and in pipe-bends without slip shows the proposed design’s effectiveness.
Promoting Mental Well-Being for Audiences in a Live-Streaming Game by Highlight-Based Bullet Comments

This paper proposes a method for generating bullet comments for live-streaming games based on highlights (i.e., the exciting parts of video clips) extracted from the game content and evaluate the effect of mental health promotion. Game live streaming is becoming a popular theme for academic research. Compared to traditional online video sharing platforms, such as Youtube and Vimeo, video live streaming platform has the benefits of communicating with other viewers in real-time. In sports broadcasting, the commentator plays an essential role as mood maker by making matches more exciting. The enjoyment emerged while watching game live streaming also benefits the audience’s mental health. However, many e-sports live streaming channels do not have a commentator for entertaining viewers. Therefore, this paper presents a design of an AI commentator that can be embedded in live streaming games. To generate bullet comments for real-time game live streaming, the system employs highlight evaluation to detect the highlights, and generate the bullet comments. An experiment is conducted and the effectiveness of generated bullet comments in a live-streaming fighting game channel is evaluated.
Edge AI without Compromise: Efficient, Versatile and Accurate Neurocomputing in Resistive Random-Access Memory

Realizing today's cloud-level artificial intelligence functionalities directly on devices distributed at the edge of the internet calls for edge hardware capable of processing multiple modalities of sensory data (e.g. video, audio) at unprecedented energy-efficiency. AI hardware architectures today cannot meet the demand due to a fundamental"memory wall": data movement between separate compute and memory units consumes large energy and incurs long latency. Resistive random-access memory (RRAM) based compute-in-memory (CIM) architectures promise to bring orders of magnitude energy-efficiency improvement by performing computation directly within memory. However, conventional approaches to CIM hardware design limit its functional flexibility necessary for processing diverse AI workloads, and must overcome hardware imperfections that degrade inference accuracy. Such trade-offs between efficiency, versatility and accuracy cannot be addressed by isolated improvements on any single level of the design. By co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM - the first multimodal edge AI chip using RRAM CIM to simultaneously deliver a high degree of versatility for diverse model architectures, record energy-efficiency $5\times$ - $8\times$ better than prior art across various computational bit-precisions, and inference accuracy comparable to software models with 4-bit weights on all measured standard AI benchmarks including accuracy of 99.0% on MNIST and 85.7% on CIFAR-10 image classification, 84.7% accuracy on Google speech command recognition, and a 70% reduction in image reconstruction error on a Bayesian image recovery task. This work paves a way towards building highly efficient and reconfigurable edge AI hardware platforms for the more demanding and heterogeneous AI applications of the future.
Bounding the edge cover of a hypergraph

Let H “ pV,Eq be a hypergraph. Let C Ď E, then C is an edge cover, or a set cover, if YePCtv|v P eu “ V . A subset of vertices X is independent in H, if no two vertices in X are in any edge. Let cpHq and αpHq denote the cardinalities of a smallest edge cover and largest independent set in H , respectively. We show that cpHq ď m̂pHqcpHq, where m̂pHq is a parameter called the mighty degeneracy of H . Furthermore, we show that the inequality is tight and demonstrate the applications in domination theory.
TSI: An Ad Text Strength Indicator using Text-to-CTR and Semantic-Ad-Similarity

Coming up with effective ad text is a time consuming process, and particularly challenging for small businesses with limited advertising experience. When an inexperienced advertiser onboards with a poorly written ad text, the ad platform has the opportunity to detect low performing ad text, and provide improvement suggestions. To realize this opportunity, we propose an ad text strength indicator (TSI) which: (i) predicts the click-through-rate (CTR) for an input ad text, (ii) fetches similar existing ads to create a neighborhood around the input ad, (iii) and compares the predicted CTRs in the neighborhood to declare whether the input ad is strong or weak. In addition, as suggestions for ad text improvement, TSI shows anonymized versions of superior ads (higher predicted CTR) in the neighborhood. For (i), we propose a BERT based text-to-CTR model trained on impressions and clicks associated with an ad text. For (ii), we propose a sentence-BERT based semantic-ad-similarity model trained using weak labels from ad campaign setup data. Offline experiments demonstrate that our BERT based text-to-CTR model achieves a significant lift in CTR prediction AUC for cold start (new) advertisers compared to bag-of-words based baselines. In addition, our semantic-textual-similarity model for similar ads retrieval achieves a precision@1 of 0.93 (for retrieving ads from the same product category); this is significantly higher compared to unsupervised TF-IDF, word2vec, and sentence-BERT baselines. Finally, we share promising online results from advertisers in the Yahoo (Verizon Media) ad platform where a variant of TSI was implemented with sub-second end-to-end latency.
The supernova remnant populations of the galaxies NGC 45, NGC 55, NGC 1313, NGC 7793: luminosity and excitation functions

We present a systematic study of the supernova remnant (SNR) populations in the nearby galaxies NGC 45, NGC 55, NGC 1313, and NGC 7793 based on deep H $\rm {\alpha }$ and [S ii] imaging. We find 42 candidate and 51 possible candidate SNRs based on the [S ii]/H $\rm {\alpha }$>0.4 criterion, 81 of which are new identifications. We derive the H $\rm {\alpha }$ and the joint [S ii]–H $\rm {\alpha }$ luminosity functions after accounting for incompleteness effects. We find that the H $\rm {\alpha }$ luminosity function of the overall sample is described with a skewed Gaussian with a mean equal to $\rm \log (L_{H\alpha }/10^{36}\, erg\, s^{-1})=0.07$ and $\rm \sigma (\log (L_{H\alpha }/10^{36}\, erg\, s^{-1}))=0.58$. The joint [S ii]–H $\rm {\alpha }$ function is parametrized by a skewed Gaussian along the log([S ii]$\rm /10^{36}\, erg\, s^{-1}) = 0.88 \times \log (L_{H\alpha }/10^{36}\, erg\, s^{-1}) - 0.06$ line and a truncated Gaussian with $\rm \mu (\log (L_{[S\, II]}/10^{36})) = 0.024$ and $\rm \sigma (\log (L_{[S\, II]}/10^{36})) = 0.14$, on its vertical direction. We also define the excitation function as the number density of SNRs as a function of their [S ii]/H $\rm {\alpha }$ ratios. This function is represented by a truncated Gaussian with a mean at −0.014. We find a sub-linear [S ii]–H $\rm {\alpha }$ relation indicating lower excitation for the more luminous objects.
LOKI: Long Term and Key Intentions for Trajectory Prediction

Recent advances in trajectory prediction have shown that explicit reasoning about agents’ intent is important to accurately forecast their motion. However, the current research activities are not directly applicable to intelligent and safety critical systems. This is mainly because very few public datasets are available, and they only consider pedestrian-specific intents for a short temporal horizon from a restricted egocentric view. To this end, we propose LOKI (LOng term and Key Intentions), a novel large-scale dataset that is designed to tackle joint trajectory and intention prediction for heterogeneous traffic agents (pedestrians and vehicles) in an autonomous driving setting. The LOKI dataset is created to discover several factors that may affect intention, including i) agent’s own will, ii) social interactions, iii) environmental constraints, and iv) contextual information. We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction. We show our method outperforms state-of-the-art trajectory prediction methods by upto 27% and also provide a baseline for frame-wise intention estimation. The dataset is available at https://usa.honda-ri.com/loki
Channel-Temporal Attention for First-Person Video Domain Adaptation

Unsupervised Domain Adaptation (UDA) can transfer knowledge from labeled source data to unlabeled target data of the same categories. However, UDA for first-person action recognition is an under-explored problem, with lack of datasets and limited consideration of first-person video characteristics. This paper focuses on addressing this problem. Firstly, we propose two small-scale first-person video domain adaptation datasets: ADL$_{small}$ and GTEA-KITCHEN. Secondly, we introduce channel-temporal attention blocks to capture the channel-wise and temporal-wise relationships and model their inter-dependencies important to first-person vision. Finally, we propose a Channel-Temporal Attention Network (CTAN) to integrate these blocks into existing architectures. CTAN outperforms baselines on the two proposed datasets and one existing dataset EPIC$_{cvpr20}$.
ARDOP: A Versatile Humanoid Robotic Research Platform

This paper describes the development of a humanoid robot called ARDOP. The goal of the project is to provide amodular, open-source, and inexpensive humanoid robot that would enable researchers to answer various problems related to robotic manipulation and perception. ARDOP primarily comprises of two functional units namely the perception and manipulation system, here we discuss the conceptualization and design methodology of these systems and proceed to present their performance results on simulation and various custom-designed experiments. The project has been maintained on our page https://ardop.github. io/.
Adaptive Graph Convolution for Point Cloud Analysis

Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect. The standard convolution characterises feature correspondences indistinguishably among 3D points, presenting an intrinsic limitation of poor distinctive feature learning. In this paper, we propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. Compared with using a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike popular attentional weight schemes, the proposed AdaptConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive qualitative and quantitative evaluations show that our method outperforms state-of-the-art point cloud classification and segmentation approaches on several benchmark datasets. Our code is available at https://github.com/hrzhou2/AdaptConv-master.
Effective pointing of the ASTRI-Horn telescope using the Cherenkov camera with the Variance method

Cherenkov telescope cameras are not suitable to perform astrometrical pointing calibration since they are not designed to produce images of the sky, but rather to detect nanosecond atmospheric ﬂashes due to very high-energy cosmic radiation. Indeed, these instruments show only a moderate angular resolution (fractions of degrees) and are almost blind to the steady or slow-varying optical signal of starlight. For this reason, auxiliary optical instruments are typically adopted to calibrate the telescope pointing. However, secondary instruments are possible sources of systematic errors. Furthermore, the Cherenkov camera is the only one framing exactly the portion of the sky under study, and hence its exploitation for pointing calibration purposes would be desirable. In this contribution, we present a procedure to assess the pointing accuracy of the ASTRI-Horn telescope by means of its innovative Cherenkov camera. This instrument is endowed with a statistical method, the so-called Variance method, implemented in the logic board and able to provide images of the night sky background light as ancillary output. Taking into account the convolution between the optical point spread function and the pixel distribution, Variance images can be used to evaluate the position of stars with sub-pixel precision. In addition, the rotation of the ﬁeld of view during observations can be exploited to verify the alignment of the Cherenkov
The Missing Link? Discovery of Pulsations in the Nitrogen-rich PG 1159 Star PG 1144+005

Up to 98% of all single stars will eventually become white dwarfs—stars that link the history and future evolution of the Galaxy, and whose previous evolution is engraved in their interiors. Those interiors can be studied using asteroseismology, utilizing stellar pulsations as seismic waves. The pulsational instability strips of DA and DB white dwarf stars are pure, allowing for the important generalization that their interior structure represents that of all DA and DB white dwarfs. This is not the case for the hottest pulsating white dwarfs, the GW Vir stars: only about 50% of white dwarfs in this domain pulsate. Several explanations for the impurity of the GW Vir instability strip have been proposed, based on different elemental abundances, metallicity, and helium content. Surprisingly, there is a dichotomy that only stars rich in nitrogen, which by itself cannot cause pulsation driving, pulsate—the only previous exception being the nitrogen-rich nonpulsator PG 1144+005. Here, we report the discovery of pulsations in PG 1144+005 based on new observations. We identified four frequency regions: 40, 55, 97, and 112 day−1 with low and variable amplitudes of about 3–6 mmag and therefore confirm the nitrogen dichotomy. As nitrogen is a trace element revealing the previous occurrence of a very late thermal pulse (VLTP) in hot white dwarf stars, we speculate that it is this VLTP that provides the interior structure required to make a GW Vir pulsator.
ARCH++: Animation-Ready Clothed Human Reconstruction Revisited

We present ARCH++, an image-based method to reconstruct 3D avatars with arbitrary clothing styles. Our reconstructed avatars are animation-ready and highly realistic, in both the visible regions from input views and the unseen regions. While prior work shows great promise of reconstructing animatable clothed humans with various topologies, we observe that there exist fundamental limitations resulting in sub-optimal reconstruction quality. In this paper, we revisit the major steps of image-based avatar reconstruction and address the limitations with ARCH++. First, we introduce an end-to-end point based geometry encoder to better describe the semantics of the underlying 3D human body, in replacement of previous hand-crafted features. Second, in order to address the occupancy ambiguity caused by topological changes of clothed humans in the canonical pose, we propose a co-supervising framework with cross-space consistency to jointly estimate the occupancy in both the posed and canonical spaces. Last, we use image-to-image translation networks to further refine detailed geometry and texture on the reconstructed surface, which improves the fidelity and consistency across arbitrary viewpoints. In the experiments, we demonstrate improvements over the state of the art on both public benchmarks and user studies in reconstruction quality and realism.