Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Point Cloud Classification Network Based on Multilayer Feature Fusion and Projected Images

Deep Learning (DL) based point cloud classification techniques now in use suffer from issues such as disregarding local feature extraction, missing connections between points, and failure to extract two-dimensional information features from point clouds. A point cloud classification network that utilizes multi-layer feature fusion and point cloud projection images is suggested to address the aforementioned problems and produce more accurate classification outcomes. Firstly, the network extracts local characteristics of point clouds through graph convolution to strengthen the connection between points. Then, the fusing attention mechanism is introduced to aggregate the useful characteristics of the point cloud while suppressing the useless characteristics, and the point cloud characteristics are fused by multi-layer characteristic fusion. Finally, a 3D point cloud network plug-in model based on point cloud projection image (3D CLIP) is proposed, which can make up for the defects of other 3D point cloud classification networks that do not extract two-dimensional information characteristics of point clouds, and solve the problem of low accuracy of similar category recognition in datasets. The ModelNet40 dataset was used for classification studies, and the results show that the point cloud classification network, without the addition of a 3D CLIP plug-in model, achieves a classification accuracy of 92.5%. The point cloud classification network with a 3D CLIP plug-in model achieved a classification accuracy of 93.6%, demonstrating that this technique can successfully raise point cloud classification accuracy. Keywords—Point cloud; classification; graph convolution; attention mechanism; CLIP
Sequence Characterization and Phylogenetic Analyses of the Pol Region from Sire-1, an Endogenous Retrovirus from Glycine Max

SIRE-I was initially reported to be an interspersed collection of several hundred, long repeated DNA elements from G. max. The sequence of a truncated 2.4 kb SIRE-I cDNA clone contained a typical retroelement primer binding site, an open-reading frame (ORF) encoding a retroelement-like gag-protease polyprotein, and part of a long terminal repeat (LTR). Peptide sequence comparisons placed SIRE-I in the copia! TyI retrotransposon group. A full-length copy of SIRE-I has been recovered from a soybean genomic library. A subclone containing a 4.2 kb Xbal fragment, pAM4.2, from this genomic insert was sequenced previously. The translated sequence of the subclone reveiled a strong resemblance to that of vertebrate retroviral envelope proteins. Appropriately located retroelement replication and transcription signals were also present. Here we report the sequences of two subclones; one containing a 4.1 kb HindIII fragment, pEG4.1, from the genomic clone, and the other containing a 4.3 kb HindIII fragment, pEG4.3. The 4.1 kb fragment overlaps with the previously sequenced subclone pAM4.2, but contains 2400 bp of DNA upstream of that subclone. Theoretical translation of the 3' end of pEG4.3 insert produces the amino-terminus of the integrase domain. Conceptual translation of the 4.1 kb fragment, combined with the 4.3 kb fragment, produces a long open-reading frame of 942 codons. The ORF encodes some or all of the RNase H, Reverse Transcriptase (RT),
The devil in the Covid-19 detail

As deaths stack up and steeply inclined graphs of casualties reflect our new dystopian normality, the story contained within Mark Shannon tweets of how he lost his fight against coronavirus disease 2019 (Covid-19) is sobering to read. He was a young IT professional in UK, working from home and diligently looking after his 5-month-old baby. He seemed very worried about the pandemic and had researched it. He went out of his way to take precautions against it. On 16th April he tweeted proudly about his parenting skills. On April 25th, he died from Covid-19 after a short but brief period in hospital. Mark Channon could have been any of us. Mark Channon could be any of us.
Improved Stable Read Range of the RFID Tag Using Slot Apertures and Capacitive Gaps for Outdoor Localization Applications

This paper proposes a small-size UHF RFID tag antenna, which was designed to function in the frequency interval of 860–960 MHz, with a large-read range of up to 17 m. In this work, the effects of capacitive slots and gaps on the impedance matching between conventional industrial chips and a designed RFID antenna was investigated. Simulated and measured results provided a clear indication that these two techniques can efficiently improve the return loss parameter and the antenna impedance matching behavior at the UHF band. Three-dimensional electromagnetic (EM) simulations results further proved that a better impedance matching between an industrial chip and a proposed RFID antenna occurs at 870 MHz, where the estimated input antenna impedance was about Za = 16 + j184 (Ω), and the calculated read range reached a value of up to 17 m with a measured return loss value of –14 dB. The proposed RFID antenna can almost maintain the same read range value over a 180 degree angle variations on the horizontal plane owing to its omnidirectional radiation pattern. The fabrication and test stages of the antenna prototype were scheduled to validate the simulated characteristics. Experimental results confirmed the performances of our proposed RFID tag antenna and proved its potential ability for localization applications. EM simulations have been performed using the well-known commercial EM software simulator CST MWS.
Dusting Off the Turing Test

then inter- est theory and a problem about the observation of quantum systems (now as the quantum Zeno effect). With his death, train was lost, but the question computa- tion to fundamental physics has remained. Since the computing given a practical technological arena in which computation and quantum physics interact excitingly, it has not yet changed Tur- ing s picture of what is computable. There are also thought-experiment models that explore what it would mean beyond the ally require machine operate with speed or allow unlim- ited accuracy probe more the nature physical world. Perhaps the body of ideas is that of Roger Penrose ( 7). These draw strongly on the very thing motivated Tur- ing s early relationship operations to the imply that uncomputable physics abstract of how states are in the world. The problem remains: Does computation with discrete symbols a complete the world? If it does, how can make this connection manifest? If it does not, where does fail, and what would this tell us about fundamental science?
The significance of vertical land movements at convergent plate boundaries in probabilistic sea-level projections for AR6 scenarios: The New Zealand case

Anticipating and managing the impacts of sea-level rise for nations astride active tectonic margins requires rates of sea surface elevation change in relation to coastal land elevation to be understood. Vertical land motion (VLM) can either exacerbate or reduce sea-level changes with impacts varying significantly along a coastline. Determining rate, pattern, and variability of VLM near coasts leads to a direct improvement of location-specific relative sea level (RSL) estimates. Here, we utilise vertical velocity field from interferometric synthetic aperture radar (InSAR) data, calibrated with campaign and continuous Global Navigation Satellite System (GNSS), to determine the VLM for the entire coastline of New Zealand. Guided by existing knowledge of the seismic cycle, the VLM data infer long-term, interseismic rates of land surface deformation. We build probabilistic RSL projections using the Framework for Assessing Changes to Sea-level (FACTS) from IPCC Assessment Report 6 and ingest local VLM data to produce RSL projections at 7435 sites, thereby enhancing spatial coverage that was previously limited to tide gauges. We present ensembles of probability distributions of RSL for medium confidence climatic processes for each scenario to 2150 and low confidence processes to 2300. For regions where land subsidence is occurring at rates >2mm yr-1 VLM makes a significant contribution to RSL projections for all scenarios out 2150. Beyond 2150, for higher emissions scenarios, the land ice contribution to global sea level dominates. We discuss the planning implications of RSL projections, where timing of threshold exceedance for coastal inundation can be brought forward by decades.
A Dashboard for Interactive Convolutional Neural Network Training And Validation Through Saliency Maps

Quali-quantitative methods provide ways for interrogating Convolutional Neural Networks (CNN). For it, we propose a dashboard using a quali-quantitative method based on quantitative metrics and saliency maps. By those means, a user can discover patterns during the training of a CNN. With this, they can adapt the training hyperparameters of the model, obtaining a CNN that learned patterns desired by the user. Furthermore, they neglect CNNs which learned undesirable patterns. This improves users’ agency over the model training process
A Droop-Controlled Interlink Converter for a Dual DC Bus Nanogrid with Decentralized Control

This paper proposed a dual DC bus nanogrid with 380 V and 48 V buses and allows the integration of distributed energy resources on two buses. The proposed system employs an interlink converter to enable power sharing between the buses. The integration of distributed energy resources has been found to enhance the reliability of the low-voltage bus in comparison to those that lack such integration. The integration process requires the introduction of a new V-I curve for the interlink converter within a DC nanogrid controlled by DC bus signaling and droop control. Furthermore, selecting a power electronics converter for the interlink converter is essential. This paper employs a dual active bridge with galvanic isolation as an interlink converter and proposes a control strategy for the converter that relies on DC bus signaling and droop control. Moreover, this control methodology serves the purpose of preventing any detrimental impact of the interlink converter on the DC buses through the reprogramming of the V-I curve. Subsequently, the suggested control methodology underwent simulation testing via MATLAB/Simulink, which included two different test categories. Initially, the DAB was evaluated as an interlink converter, followed by a comprehensive assessment of the interlink converter in a complete dual DC bus nanogrid. The results indicate that the DAB has the potential to function as an interlink converter while the suggested control approach effectively manages the power sharing between the two buses.
Commandabilité d'un modèle de Boltzmann sur réseau en eaux peu profondes

Le Modele de Boltzmann sur Reseau (automate cellulaire) equivalent aux equations de Saint-Venant 1D est rappele. Ensuite sa representation en formalisme espace d'etat est introduit. Puis, le probleme de commandabilite du modele (discret) est investigue. Quelques methodes de calcul des grammiens de systemes discret a grande echelle sont presentees. En n, des arguments sur l'analyse de la commandabilite sont presentes.
In Vitro Assessment of Salt Stress Tolerance in Wild Potato Species

Proof of concept salt tolerance and plasticity. Wild germplasm may represent a precious source of genetic variability for salt tolerance. This study evaluated the morphological changes occurring under controlled and saline conditions in tuber-bearing S. bulbocastanum, S. commersonii, S. chomatophyllum, S. multidissectum, S. pinnactisectum, S. phureja, and cultivated S. tuberosum. An in vitro screening method was employed. Significant phenotypic variations were observed for all phenotypic traits analyzed at all NaCl levels (0, 40, 60, and 120 mM). In addition, a significant correlation between root plasticity and salt tolerance was found. Further, changes in proline and total phenolic content were assessed to envisage the metabolic adjustments of tolerant clones towards salinity. The most promising sources of tolerance were identified in S. commersonii and S. multidissectum and information obtained is discussed from a breeding perspective.
Integrated Optimization And Learning Methods Of Predictive And Prescriptive Analytics

INTEGRATED OPTIMIZATION AND LEARNING METHODS OF PREDICTIVE AND PRESCRIPTIVE ANALYTICS by MEHMET KOLCU August 2021 Advisor : Dr. Alper Murat Major : Industrial Engineering Degree : Doctor of Philosophy A typical decision problem optimizes one or more objectives subject to a set of constraints on its decision variables. Most real-world decision problems contain uncertain parameters. The exponential growth of data availability, ease of accessibility in computational power, and more efficient optimization techniques have paved the way for machine learning tools to effectively predict these uncertain parameters. Traditional machine learning models measure the quality of predictions based on the closeness between true and predicted values and ignore decision problems involving uncertain parameters for which predicted values are treated as the true values. Standard approaches passing point estimates of machine learning models into decision problems as replacement of uncertain parameters lose the connection between predictive and prescriptive tasks. Recently developed methods to strengthen the bond between predictive and prescriptive tasks still rely on either "first predict, then optimize" strategy or use approximation techniques in integrating predictive and prescriptive tasks.
An outlook of early rehabilitation of stroke patients using VR technologies

Among all the latest technologies, virtual reality (virtual reality, VR) can be a powerful and promising tool for achieving the main goals of rehabilitation. It has been experimentally proven that rehabilitation based on virtual technologies is able to recreate a realistic perception and corresponding reaction in a patient, thereby improving the quality of cognitive and motor rehabilitation with the least cost. However, the success of such rehabilitation depends mainly on the technologies and techniques used by doctors. This study is devoted to the important problem — application of virtual reality technologies in patients with acute impairment of cerebral circulation at the stage of early rehabilitation. We focus on the neurophysiological aspects of the VR technology application and the mechanisms of the brain’s neuroplasticity during application of virtual reality. The characteristic features of modern approaches to application of virtual reality are highlighted. Also, a novel concept is proposed for the modern VR-technology application in intensive care unit patients. In conclusion, we discuss the possibilities of VR-technologies application, allowing for the simulation of any situation, as well as a possibility of synchronous playback of a music track with walking simulation in ICU patients in the acute period of stroke at the stage of early rehabilitation.
D Y N E M O C: A SEMI - SUPERVISED ARCHITECTURE FOR CLASSIFYING TIME SERIES BRAIN DATA

Understanding how different regional networks of the brain get activated and how those activations change over time can help in identifying the onset of various neurodegenerative diseases, studying the efficacy of different treatment regimens for those illnesses, and developing brain-computer interfaces for patients with different types of disabilities. To explain dynamic brain networks, an RNN-VAE model named DyNeMo has recently been proposed. This model can take into account the whole recorded history of brain states while modeling their dynamics and is able to better capture the complexities in larger datasets than previous works. In this paper, we show that the latent representations learned by DyNeMo through unsupervised training are not sufficient for downstream classification tasks and propose a new semi-supervised model named DyNeMoC that overcomes this shortcoming. The downstream task we study is the classification of visual stimuli from MEG recordings. We show that both of our proposed variants of DyNeMoC — DyNeMoC-RNN and DyNeMoC-Transformer — lead to more useful latent representations for stimuli classification with the transformer variant outperforming the RNN one. Learning representations that are directly linked to a downstream task in this manner could ultimately be used to improve the monitoring and treat-ment of certain neurodegenerative diseases and building better brain-computer interfaces.
Advances in saRNA Vaccine Research against Emerging/Re-Emerging Viruses

Although conventional vaccine approaches have proven to be successful in preventing infectious diseases in past decades, for vaccine development against emerging/re-emerging viruses, one of the main challenges is rapid response in terms of design and manufacture. mRNA vaccines can be designed and produced within days, representing a powerful approach for developing vaccines. Furthermore, mRNA vaccines can be scaled up and may not have the risk of integration. mRNA vaccines are roughly divided into non-replicating mRNA vaccines and self-amplifying RNA (saRNA) vaccines. In this review, we provide an overview of saRNA vaccines, and discuss future directions and challenges in advancing this promising vaccine platform to combat emerging/re-emerging viruses.
Document Structure in Long Document Transformers

Long documents often exhibit structure with hierarchically organized elements of different functions, such as section headers and paragraphs. Despite the omnipresence of document structure, its role in natural language processing (NLP) remains opaque. Do long-document Transformer models acquire an internal representation of document structure during pre-training? How can structural information be communicated to a model after pre-training, and how does it influence downstream performance? To answer these questions, we develop a novel suite of probing tasks to assess structure-awareness of long-document Transformers, propose general-purpose structure infusion methods, and evaluate the effects of structure infusion on QASPER and Evidence Inference, two challenging long-document NLP tasks. Results on LED and LongT5 suggest that they acquire implicit understanding of document structure during pre-training, which can be further enhanced by structure infusion, leading to improved end-task performance. To foster research on the role of document structure in NLP modeling, we make our data and code publicly available.
An Empirical Deep Learning Approach for Arabic News Classification

—In this paper, we tackle the problem of Arabic news classification. A dataset of 5,000 news articles from various Saudi Arabian news sources were gathered, classified into six categories: business, entertainment, health, politics, sports, and technology. We conducted experiments using different pre-processing techniques, word embeddings, and deep learning architectures, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, as well as a hybrid CNN-LSTM model. Our proposed model achieved an accuracy of 93.15, outperforming other models in terms of accuracy. Moreover, our model is evaluated on other Arabic news datasets and obtained competitive results. Our approach demonstrates the effectiveness of deep learning methods in Arabic news classification and emphasizes the significance of careful selection of preprocessing techniques, word embeddings, and deep learning architectures.
Software Vulnerabilities’ Detection by Analysing Application Execution Traces

—Over the years, digital traces have proven to be significant for analyzing IT systems, including applications. With the persistent threats arising from the widespread proliferation of malware and the evasive techniques employed by cybercrim-inals, researchers and application vendors alike are concerned about finding effective solutions. In this article, we assess a hybrid approach to detecting software vulnerabilities based on analyzing traces of application execution. To accomplish this, we initially extract permissions and features from manifest files. Subsequently, we employ a tracer to extract events from each running application, utilizing a set of elements that indicate the behavior of the application. These events are then recorded in a trace. We convert these traces into features that can be utilized by machine learning algorithms. Finally, to identify vulnerable applications, we train these features using six machine learning algorithms (KNN, Random Forest, SVM, Naive Bayes, Decision Tree-CART, and MLP). The selection of these algorithms is based on the outcomes of several preliminary experiments. Our results indicate that the SVM algorithm produces the best performance, followed by Random Forest, achieving an accuracy of 98% for malware detection and 96% for benign applications. These findings demonstrate the relevance and utility of analyzing real application behavior through event analysis.
Compressed Opacity Maps for Ray Tracing

Recently, schemes have been proposed for accelerating ‘alpha-tested’ triangles in ray-tracing through the use of precomputed, three-level Opacity Masks/Maps that can significantly reduce the need for expensive of ‘Any-Hit shader’ invocations. We propose and compare two related schemes, VQ2 and VQ4, of compressing such maps that provide both random access and low-cost decompression. Each compressed opacity map, however, relates to a pair of adjacent triangles, taking advantage of correlation across the shared edge and matching likely underlying hardware primitive models
Itaconic Anhydride as a Bio-Based Compatibilizer for a Tung Oil-Based Thermosetting Resin Reinforced with Sand and Algae Biomass

In this work, renewable composites were prepared by the association of a thermosetting resin synthesized via free-radical polymerization, using a mixture of tung oil, n-butyl methacrylate, and divinylbenzene, with silica-rich fillers, namely an algae biomass with high silica content, and a well-sorted sand. Furthermore, to investigate if the interaction between the non-polar resin and polar reinforcements could be improved, enhancing the materials’ mechanical properties, itaconic anhydride, a bio-derived molecule obtained from itaconic acid, was introduced to the resin composition. Thermogravimetric analysis (TGA) suggested that the thermal stability of the composites was overall not changed with the addition of itaconic anhydride. The mechanical properties of the sand composites, however, did improve, as the storage modulus at room temperature, measured by dynamic mechanical analysis (DMA), almost doubled in the presence of itaconic anhydride. The glass transition temperatures of the materials increased by approximately 30 °C when sand was used as a reinforcement. Water absorption experiments validated an increase in the polarity of the unreinforced resin by the addition of itaconic anhydride to its formulation. The composites, however, did not exhibit a significant difference in polarity in the presence of itaconic anhydride. Finally, scanning electron microscopy (SEM), equipped with energy dispersive spectroscopy (EDS), demonstrated better matrix–filler adhesion in the presence of itaconic anhydride for high-silica algae composites.