Miracle-dz/UniFAR-FTUs huggingface dataset:
Variations of the gas temperature in He/N2 barrier discharges

In order to obtain the gas temperature in the microdischarge areas and in the ambient gas of dielectric barrier discharges in helium/nitrogen mixtures we have studied the rotational temperature of the emission of molecular nitrogen ions. In these mixtures the excited state N+2(B 2Σ+u) is formed mainly by Penning ionization. We have calculated the rotational spectra of the transition N+2(B 2Σ+u→X 2Σg+) using a known reaction scheme for discharges in helium/nitrogen mixtures (Pouvesle et al 1982 J. Chem. Phys. 77 817) by averaging over the temporal variation of the gas temperature after the ignition of a microdischarge. We obtained this variation by solving the Navier-Stokes equations for this case. It is shown, that under our experimental conditions (pHe = 38.5-156 kPa, pN2 = 40-6000 Pa) the rotational population of the nitrogen ions corresponds to the equilibrium distribution at the temperature of the surrounding gas. Experimentally we obtained the rotational temperatures of molecular nitrogen ions by high-resolution optical emission spectroscopy. For the investigated range of nitrogen and helium partial pressures we fitted to these temperature values the calculated ones and obtained from these fits estimates of the following parameters of the microdischarges: 310±10 K as the ambient gas temperature and 28±1 µm as the microdischarge radius r.
A Benchmark Dataset for Devnagari Document Recognition Research

: - A benchmark dataset is required for the development of an efficient and a reliable recognition system. Unfortunately, no comprehensive benchmark dataset exists for handwritten Devnagari optical document recognition research, at least in the public domain. This paper is an effort in this direction. In here, we introduce a comprehensive dataset that we referred to as CPAR-2012 dataset, for such benchmark studies, also present some preliminary recognition results. The dataset includes 35,000 isolated handwritten numerals, 83,300 characters, 2,000 constrained and 2,000 unconstrained handwritten pangrams. It is organized in a relational data model that contains text images along with their writer's information and related handwriting attributes. We collected the handwriting samples from 2,000 subjects who were chosen from different age, ethnicity, and educational background, regional and linguistic groups. The samples reflect expected variations in Devnagari handwriting. The digit recognition results using recognition schemes that uses simple most features & four neural network classifiers & KNN, and classifier ensemble have also been reported for benchmarking.
Improving Performance Through Novel Enhanced Hierarchial Attention Neural Network

— Big data and its classification have been the recent challenge in the evolving world. Data evolving needs to be classified in an effective way. For the classifying process, deep learning and machine learning models are evolved. Hierarchical Attention Network (HAN) is one of the most dominant neural network structures in classification. The major demerits which the HAN is facing are, high computation time and numerous layers. The drawback of HAN is vanquished by a new idea arrived from the mining methods that yield mixed attention network for android data classification. By this flow it could handle more complex request apart from the concept identified. The EHAN (Enhanced Hierarchical Attention Network) has two prototypes. The first one is the attention model to distinguish the features and the second one is the self-attention model to identify worldwide facts. By the demonstrated outcome we infer that the partitioning of task is constructive and therefore the EHAN features shows a significant growth on the news database. In addition to this paper we could add other subnetworks subsequently to assist this ability further.
Compatibility Study of Dihydroxylammonium 5,5′-Bistetrazole-1,1′-diolate (TKX-50) with Some Energetic Materials and Inert Materials

Dihydroxylammonium 5,5′-bistetrazole-1,1′-diolate (TKX-50) emerged as a novel energetic material with low sensitivity and excellent calculated detonation performance. The compatibility of TKX-50 with nitrocellulose (NC), an NC/NG (nitroglycerine) mixture (mass rate: 1.25:1), 2,4-dinitroanisole (DNAN), 2,4,6-trinitrotoluene (TNT), cyclotrimethylenetrinitramine (RDX), ammonium perchlorate (AP), hexanitroethane (HNE), cyclotetramethylenetetranitramine (HMX), hexanitrohexazaisowurtzitane (CL-20), glycidyl azide polymer (GAP), hydroxyl-terminated polybutadiene (HTPB), aluminum powder (Al), boron powder, and centralite was studied by differential scanning calorimetry (DSC). The results show that TKX-50/HNE possesses good compatibility, TKX-50 and HMX have moderate compatibility, and the compatibility of TKX-50 with TNT, CL-20, centralite, NC, AP, Al, and GAP is poor; in addition, TKX-50/RDX, TKX-50/NC + NG, TKX-50/B, and TKX-50/HTPB have poor compatibility.
IMPACT OF DUST EMISSION ON PLANT VEGETATION IN THE VICINITY OF CEMENT PLANT

Environment is a major issue which confronts industry and business in today’s world on daily basis. Different industrial activities are degrading various environmental components like water, air, soil and plant vegetation. Cement industry is one of the 17 most polluting industries listed by the Central Pollution Control Board (CPCB). The Jaypee Rewa Cement Cement industry, Rewa, Madhaya Pradesh is located between 24 o 33 ’ North longitude and 81 o 10 ’ east latitude and is situated at Jay Prakash Nagar 20 km from Rewa Town of Madhya Pradesh, India. The Jaypee Rewa Cement industry is the major source of particulate matters, SOx, NOx and CO2, emissions. Cement dust contains heavy metals like nickel, cobalt, lead, chromium, pollutants hazardous to the biotic environment, with impact for vegetation, human and animal health and ecosystems. Present paper attempts to focus on impact of cement emission on plant vegetation.
Meta Adversarial Training

Recently demonstrated physical-world adversarial attacks have exposed vulnerabilities in perception systems that pose severe risks for safety-critical applications such as autonomous driving. These attacks place adversarial artifacts in the physical world that indirectly cause the addition of universal perturbations to inputs of a model that can fool it in a variety of contexts. Adversarial training is the most effective defense against image-dependent adversarial attacks. However, tailoring adversarial training to universal perturbations is computationally expensive since the optimal universal perturbations depend on the model weights which change during training. We propose meta adversarial training (MAT), a novel combination of adversarial training with meta-learning, which overcomes this challenge by meta-learning universal perturbations along with model training. MAT requires little extra computation while continuously adapting a large set of perturbations to the current model. We present results for universal patch and universal perturbation attacks on image classiﬁcation and trafﬁc-light detection. MAT considerably increases robustness against universal patch attacks compared to prior work.
Optimization of Vacuum Microwave Predrying and Vacuum Frying Conditions to Produce Fried Potato Chips

The effects of vacuum microwave predrying and vacuum frying conditions on the quality of vacuum-fried potato chips were studied. Both the moisture content and oil content of potato chips decreased with increasing vacuum microwave predrying time. During vacuum frying, the moisture content of potato chips decreased with increasing frying temperature and time, while the oil content increased. Statistical analysis with response surface regression showed that the moisture content, oil content, and breaking force of potato chips were significantly (P<0.05) correlated with vacuum microwave predrying time, frying temperature, and frying time. Based on surface responses and contour plots, optimum conditions were vacuum microwave predrying time of 8–9 min, vacuum frying temperature of 108–110°C, and vacuum frying time of 20–21 min.
A Survey on 5G Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning

Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives, by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low latency communications. In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorise and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green networks. Then, as a main contribution, we survey in detail —from a theoretical and a practical viewpoint— the main energy efficiency enabling features that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key technology features, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of —and the status on— green networking. Finally, the areas of research where more effort is needed to make future networks greener are also discussed.
Compactness within the space of complete, constant Q-curvature metrics on the sphere with isolated singularities

In this paper we consider the moduli space of complete, conformally flat metrics on a sphere with k punctures having constant positive Q-curvature and positive scalar curvature. Previous work has shown that such metrics admit an asymptotic expansion near each puncture, allowing one to define an asymptotic necksize of each singular point. We prove that any set in the moduli space such that the distances between distinct punctures and the asymptotic necksizes all remain bounded away from zero is sequentially compact, mirroring a theorem of D. Pollack about singular Yamabe metrics. Along the way we define a radial Pohozaev invariant at each puncture and refine some a priori bounds of the conformal factor, which may be of independent interest.
Arbitrary-Oriented Ship Detection Through Center-Head Point Extraction

Ship detection in remote sensing images plays a crucial role in various applications and has drawn increasing attention in recent years. However, existing arbitrary-oriented ship detection methods are generally developed on a set of predefined rotated anchor boxes. These predefined boxes not only lead to inaccurate angle predictions but also introduce extra hyperparameters and high computational cost. Moreover, the prior knowledge of ship size has not been fully exploited by existing methods, which hinders the improvement of their detection accuracy. Aiming at solving the above issues, in this article, we propose a center-head point extraction-based detector (CHPDet) to achieve arbitrary-oriented ship detection in remote sensing images. Our CHPDet formulates arbitrary-oriented ships as rotated boxes with head points that are used to determine the direction. Also, a rotated Gaussian kernel is used to map the annotations into target heatmaps. Keypoint estimation is performed to find the center of ships. Then, the size and head point of the ships are regressed. The orientation-invariant model (OIM) is also used to produce orientation-invariant feature maps. Finally, we use the target size as prior to fine-tune the results. Moreover, we introduce a new dataset for multiclass arbitrary-oriented ship detection in remote sensing images at a fixed ground sample distance (GSD) that is named FGSD2021. Experimental results on FGSD2021 and two other widely used datasets, i.e., HRSC2016 and UCAS-AOD, demonstrate that our CHPDet achieves the state-of-the-art performance and can well distinguish between bow and stern. Code and FGSD2021 dataset are available at https://github.com/zf020114/CHPDet.
REFORM: Fast and Adaptive Solution for Subteam Replacement

Subteam Replacement: given a team of people embedded in a social network to complete a certain task, and a subset of members (i.e., subteam) in this team which have become unavailable, find another set of people who can perform the subteam’s role in the larger team. We conjecture that a good candidate subteam should have high skill and structural similarity with the replaced subteam while sharing a similar connection with the larger team as a whole. Based on this conjecture, we propose a novel graph kernel which evaluates the goodness of candidate subteams in this holistic way freely adjustable to the need of the situation. To tackle the significant computational difficulties, we equip our kernel with a fast approximation algorithm which (a) employs effective pruning strategies, (b) exploits the similarity between candidate team structures to reduce kernel computations, and (c) features a solid theoretical bound on the quality of the obtained solution. We extensively test our solution on both synthetic and real datasets to demonstrate its effectiveness and efficiency. Our proposed graph kernel outputs more human-agreeable recommendations compared to metrics used in previous work, and our algorithm consistently outperforms alternative choices by finding nearoptimal solutions while scaling linearly with the size of the replaced subteam.
In-IDE Code Generation from Natural Language: Promise and Challenges

A great part of software development involves conceptualizing or communicating the underlying procedures and logic that needs to be expressed in programs. One major difficulty of programming is turning concept into code, especially when dealing with the APIs of unfamiliar libraries. Recently, there has been a proliferation of machine learning methods for code generation and retrieval from natural language queries, but these have primarily been evaluated purely based on retrieval accuracy or overlap of generated code with developer-written code, and the actual effect of these methods on the developer workflow is surprisingly unattested. In this article, we perform the first comprehensive investigation of the promise and challenges of using such technology inside the PyCharm IDE, asking, “At the current state of technology does it improve developer productivity or accuracy, how does it affect the developer experience, and what are the remaining gaps and challenges? ” To facilitate the study, we first develop a plugin for the PyCharm IDE that implements a hybrid of code generation and code retrieval functionality, and we orchestrate virtual environments to enable collection of many user events (e.g., web browsing, keystrokes, fine-grained code edits). We ask developers with various backgrounds to complete 7 varieties of 14 Python programming tasks ranging from basic file manipulation to machine learning or data visualization, with or without the help of the plugin. While qualitative surveys of developer experience are largely positive, quantitative results with regards to increased productivity, code quality, or program correctness are inconclusive. Further analysis identifies several pain points that could improve the effectiveness of future machine learning-based code generation/retrieval developer assistants and demonstrates when developers prefer code generation over code retrieval and vice versa. We release all data and software to pave the road for future empirical studies on this topic, as well as development of better code generation models.
Shape or Texture: Understanding Discriminative Features in CNNs

Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a `texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture. However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level. In this paper, we design a series of experiments that overcome these issues. We do this with the goal of better understanding what type of shape information contained in the network is discriminative, where shape information is encoded, as well as when the network learns about object shape during training. We show that a network learns the majority of overall shape information at the first few epochs of training and that this information is largely encoded in the last few layers of a CNN. Finally, we show that the encoding of shape does not imply the encoding of localized per-pixel semantic information. The experimental results and findings provide a more accurate understanding of the behaviour of current CNNs, thus helping to inform future design choices.
First Align, then Predict: Understanding the Cross-Lingual Ability of Multilingual BERT

Multilingual pretrained language models have demonstrated remarkable zero-shot cross-lingual transfer capabilities. Such transfer emerges by fine-tuning on a task of interest in one language and evaluating on a distinct language, not seen during the fine-tuning. Despite promising results, we still lack a proper understanding of the source of this transfer. Using a novel layer ablation technique and analyses of the model’s internal representations, we show that multilingual BERT, a popular multilingual language model, can be viewed as the stacking of two sub-networks: a multilingual encoder followed by a task-specific language-agnostic predictor. While the encoder is crucial for cross-lingual transfer and remains mostly unchanged during fine-tuning, the task predictor has little importance on the transfer and can be reinitialized during fine-tuning. We present extensive experiments with three distinct tasks, seventeen typologically diverse languages and multiple domains to support our hypothesis.
Text2Gestures: A Transformer-Based Network for Generating Emotive Body Gestures for Virtual Agents**This work has been supported in part by ARO Grants W911NF1910069 and W911NF1910315, and Intel. Code and additional materials available at: https://gamma.umd.edu/t2g

We present Text2Gestures, a transformer-based learning method to interactively generate emotive full-body gestures for virtual agents aligned with natural language text inputs. Our method generates emotionally expressive gestures by utilizing the relevant biomechanical features for body expressions, also known as affective features. We also consider the intended task corresponding to the text and the target virtual agents' intended gender and handedness in our generation pipeline. We train and evaluate our network on the MPI Emotional Body Expressions Database and observe that our network produces state-of-the-art performance in generating gestures for virtual agents aligned with the text for narration or conversation. Our network can generate these gestures at interactive rates on a commodity GPU. We conduct a web-based user study and observe that around 91% of participants indicated our generated gestures to be at least plausible on a five-point Likert Scale. The emotions perceived by the participants from the gestures are also strongly positively correlated with the corresponding intended emotions, with a minimum Pearson coefficient of 0.77 in the valence dimension.
Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss.

PURPOSE Vessel-wall-volume (VWV) and localized vessel-wall-thickness (VWT) measured from 3D ultrasound (US) carotid images are sensitive to anti-atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen-intima (LIB) and media-adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. METHODS Segmentation for CCA and ICA was performed independently using the proposed two-channel U-Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test-time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input axial images and its flipped versions; the final segmentation was generated by pixel-wise majority voting. RESULTS Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.1%±4.1% and 91.6%±6.6% for the MAB and LIB, in the CCA, respectively, and 94.2%±3.3% and 89.0%±8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA. CONCLUSIONS The proposed two-channel U-Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.
Shortest Paths in Graphs of Convex Sets

Given a graph, the shortest-path problem requires finding a sequence of edges with minimum cumulative length that connects a source vertex to a target vertex. We consider a variant of this classical problem in which the position of each vertex in the graph is a continuous decision variable constrained in a convex set, and the length of an edge is a convex function of the position of its endpoints. Problems of this form arise naturally in many areas, from motion planning of autonomous vehicles to optimal control of hybrid systems. The price for such a wide applicability is the complexity of this problem, which is easily seen to be NP-hard. Our main contribution is a strong and lightweight mixed-integer convex formulation based on perspective operators, that makes it possible to efficiently find globally optimal paths in large graphs and in high-dimensional spaces.
A Convolutional Neural Network based Cascade Reconstruction for the IceCube Neutrino Observatory

Continued improvements on existing reconstruction methods are vital to the success of high-energy physics experiments, such as the IceCube Neutrino Observatory. In IceCube, further challenges arise as the detector is situated at the geographic South Pole where computational resources are limited. However, to perform real-time analyses and to issue alerts to telescopes around the world, powerful and fast reconstruction methods are desired. Deep neural networks can be extremely powerful, and their usage is computationally inexpensive once the networks are trained. These characteristics make a deep learning-based approach an excellent candidate for the application in IceCube. A reconstruction method based on convolutional architectures and hexagonally shaped kernels is presented. The presented method is robust towards systematic uncertainties in the simulation and has been tested on experimental data. In comparison to standard reconstruction methods in IceCube, it can improve upon the reconstruction accuracy, while reducing the time necessary to run the reconstruction by two to three orders of magnitude.