Miracle-dz/UniFAR-FTUs huggingface dataset:
Routing Driverless Transport Vehicles in Car Assembly with Answer Set Programming

Abstract Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting production materials between storage locations and assembly stations. While low-level control systems take care of navigating such driverless vehicles along programmed routes and avoid collisions even under unforeseen circumstances, in the common case of multiple vehicles sharing the same operation area, the problem remains how to set up routes such that a collection of transport tasks is accomplished most effectively. We address this prevalent problem in the context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale producer of commercial vehicles, where routes for automated guided vehicles used in the production process have traditionally been hand-coded by human engineers. Such ad-hoc methods may suffice as long as a running production process remains in place, while any change in the factory layout or production targets necessitates tedious manual reconfiguration, not to mention the missing portability between different production plants. Unlike this, we propose a declarative approach based on Answer Set Programming to optimize the routes taken by automated guided vehicles for accomplishing transport tasks. The advantages include a transparent and executable problem formalization, provable optimality of routes relative to objective criteria, as well as elaboration tolerance towards particular factory layouts and production targets. Moreover, we demonstrate that our approach is efficient enough to deal with the transport tasks evolving in realistic production processes at the car factory of Mercedes-Benz Ludwigsfelde GmbH.
More Than Skin-Deep: Integration of Skin-Based and Musculoskeletal Reference Frames in Localization of Touch

The skin of the forearm is, in one sense, a flat 2-dimensional (2D) sheet, but in another sense approximately cylindrical, mirroring the 3-dimensional (3D) volumetric shape of the arm. The role of frames of reference based on the skin as a 2D sheet versus based on the musculoskeletal structure of the arm remains unclear. When we rotate the forearm from a pronated to a supinated posture, the skin on its surface is displaced. Thus, a marked location will slide with the skin across the underlying flesh, and the touch perceived at this location should follow this displacement if it is localized within a skin-based reference frame. We investigated, however, if the perceived tactile locations were also affected by the rearrangement in underlying musculoskeletal structure, that is, displaced medially and laterally on a pronated and supinated forearm, respectively. Participants pointed to perceived touches (Experiment 1), or marked them on a (3D) size-matched forearm on a computer screen (Experiment 2). The perceived locations were indeed displaced medially after forearm pronation in both response modalities. This misperception was reduced (Experiment 1), or absent altogether (Experiment 2) in the supinated posture when the actual stimulus grid moved laterally with the displaced skin. The grid was perceptually stretched at medial-lateral axis, and it was displaced distally, which suggest the influence of skin-based factors. Our study extends the tactile localization literature focused on the skin-based reference frame and on the effects of spatial positions of body parts by implicating the musculoskeletal factors in localization of touch on the body.
5G MIMO Conformal Microstrip Antenna Design

With the development of wireless communication technology, 5G will develop into a new generation of wireless mobile communication systems. MIMO (multiple-input multiple-output) technology is expected to be one of the key technologies in the field of 5G wireless communications. In this paper, 4 pairs of microstrip MIMO conformal antennas of 35 GHz have been designed. Eight-element microstrip Taylor antenna array with series-feeding not only achieves the deviation of the main lobe of the pattern but also increases the bandwidth of the antenna array and reduces sidelobe. MIMO antennas have been fabricated and measured. Measurement results match the simulation results well. The return loss of the antenna at 35 GHz is better than 20 dB, the first sidelobe level is −16 dB, and the angle between the main lobe and the plane of array is 60°.
Competitive Learning Enriches Learning Representation and Accelerates the Fine-tuning of CNNs

In this study, we propose the integration of competitive learning into convolutional neural networks (CNNs) to improve the representation learning and efficiency of fine-tuning. Conventional CNNs use back propagation learning, and it enables powerful representation learning by a discrimination task. However, it requires huge amount of labeled data, and acquisition of labeled data is much harder than that of unlabeled data. Thus, efficient use of unlabeled data is getting crucial for DNNs. To address the problem, we introduce unsupervised competitive learning into the convolutional layer, and utilize unlabeled data for effective representation learning. The results of validation experiments using a toy model demonstrated that strong representation learning effectively extracted bases of images into convolutional filters using unlabeled data, and accelerated the speed of the fine-tuning of subsequent supervised back propagation learning. The leverage was more apparent when the number of filters was sufficiently large, and, in such a case, the error rate steeply decreased in the initial phase of fine-tuning. Thus, the proposed method enlarged the number of filters in CNNs, and enabled a more detailed and generalized representation. It could provide a possibility of not only deep but broad neural networks.
Total Syntheses of Bulgecins A, B, and C and Their Bactericidal Potentiation of the β-Lactam Antibiotics

The bulgecins are iminosaccharide secondary metabolites of the Gram-negative bacterium Paraburkholderia acidophila and inhibitors of lytic transglycosylases of bacterial cell-wall biosynthesis and remodeling. The activities of the bulgecins are intimately intertwined with the mechanism of a cobiosynthesized β-lactam antibiotic. β-Lactams inhibit the penicillin-binding proteins, enzymes also critical to cell-wall biosynthesis. The simultaneous loss of the lytic transglycosylase (by bulgecin) and penicillin-binding protein (by β-lactams) activities results in deformation of the septal cell wall, observed microscopically as a bulge preceding bacterial cell lysis. We describe a practical synthesis of the three naturally occurring bulgecin iminosaccharides and their mechanistic evaluation in a series of microbiological studies. These studies identify potentiation by the bulgecin at subminimum inhibitory concentrations of the β-lactam against three pathogenic Gram-negative bacteria and establish for the first time that this potentiation results in a significant increase in the bactericidal efficacy of a clinical β-lactam.
Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles

Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A k-means baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.
Galectins at a glance

ABSTRACT Galectins are carbohydrate-binding proteins that are involved in many physiological functions, such as inflammation, immune responses, cell migration, autophagy and signalling. They are also linked to diseases such as fibrosis, cancer and heart disease. How such a small family of only 15 members can have such widespread effects remains a conundrum. In this Cell Science at a Glance article, we summarise recent literature on the many cellular activities that have been ascribed to galectins. As shown on the accompanying poster, these include carbohydrate-independent interactions with cytosolic or nuclear targets and carbohydrate-dependent interactions with extracellular glycoconjugates. We discuss how these intra- and extracellular activities might be linked and point out the importance of unravelling molecular mechanisms of galectin function to gain a true understanding of their contributions to the physiology of the cell. We close with a short outlook on the organismal functions of galectins and a perspective on the major challenges in the field. Summary: This Cell Science at a Glance article reviews the intra- and extracellular activities of carbohydrate-binding galectin proteins, and discusses their physiological roles in inflammation, immune response, cell migration, autophagy and signalling.
Exclusion of GNSS NLOS receptions caused by dynamic objects in heavy traffic urban scenarios using real-time 3D point cloud: An approach without 3D maps

Absolute positioning is an essential factor for the arrival of autonomous driving. Global Navigation Satellites System (GNSS) receiver provides absolute localization for it. GNSS solution can provide satisfactory positioning in open or sub-urban areas, however, its performance suffered in super-urbanized area due to the phenomenon which are well-known as multipath effects and NLOS receptions. The effects dominate GNSS positioning performance in the area. The recent proposed 3D map aided (3DMA) GNSS can mitigate most of the multipath effects and NLOS receptions caused by buildings based on 3D city models. However, the same phenomenon caused by moving objects in urban area is currently not modelled in the 3D geographic information system (GIS). Moving objects with tall height, such as the double-decker bus, can also cause NLOS receptions because of the blockage of GNSS signals by surface of objects. Therefore, we present a novel method to exclude the NLOS receptions caused by double-decker bus in highly urbanized area, Hong Kong. To estimate the geometry dimension and orientation relative to GPS receiver, a Euclidean cluster algorithm and a classification method are used to detect the double-decker buses and calculate their relative locations. To increase the accuracy and reliability of the proposed NLOS exclusion method, an NLOS exclusion criterion is proposed to exclude the blocked satellites considering the elevation, signal noise ratio (SNR) and horizontal dilution of precision (HDOP). Finally, GNSS positioning is estimated by weighted least square (WLS) method using the remaining satellites after the NLOS exclusion. A static experiment was performed near a double-decker bus stop in Hong Kong, which verified the effectiveness of the proposed method.
A Generalized Output-Coding Scheme with SVM for Multiclass Microarray Classification

Multiclass cancer classification based on microarray data is described. A generalized output-coding scheme combined with binary classifiers is used. Different coding strategies, decoding functions and feature selection methods are combined and validated on two cancer datasets: GCM and ALL. The effects of these different methods and their combinations are then discussed. The highest testing accuracies achieved are 78% and 100% for the two datasets respectively. The results are considered to be very good when compared with the other researchers’ work.
On the primal-dual dynamics of Support Vector Machines

The aim of this paper is to study the convergence of the primal-dual dynamics pertaining to Support Vector Machines (SVM). The optimization routine, used for determining an SVM for classification, is first formulated as a dynamical system. The dynamical system is constructed such that its equilibrium point is the solution to the SVM optimization problem. It is then shown, using passivity theory, that the dynamical system is global asymptotically stable. In other words, the dynamical system converges onto the optimal solution asymptotically, irrespective of the initial condition. Simulations and computations are provided for corroboration.
A Review of E-Textiles in Education and Society

The recent emergence of digital creativity that extends beyond the screen and into the physical world, engendering new forms of creative production, has transformed educational and professional fields. From AT&T’s bio-tracking clothing to Lady Gaga’s smart-hydraulic “Living Dress,” e-textiles infuse fashion with electronics to produce unique and aesthetic effects using new conductive materials, including thread, yarn, paint, and fabrics woven from copper, silver, or other highly conductive fibers. This chapter outlines both the educational and societal implications of these new materials in the field of e-textile creation like consumer-ready e-textile toolkits, high-profile displays of imaginative e-textile creations and an increasing body of Do-It-Yourself (DIY) literature on e-textile design that have emerged in the past decade. It also looks at ways in which e-textiles are transforming new solutions to old and persistent problems of underrepresentation of women and minorities in STEM fields and providing a vehicle in which to rethink teaching and learning in these disciplines.
Predictors of recurrence after deep vein thrombosis and pulmonary embolism: a population-based cohort study.

BACKGROUND The appropriate duration of oral anticoagulation after a first episode of venous thromboembolism (VTE) is uncertain and depends upon VTE recurrence rates. OBJECTIVE To estimate VTE recurrence rates and determine predictors of recurrence. METHODS Patients in Olmsted County, Minnesota, with a first lifetime deep vein thrombosis or pulmonary embolism diagnosed during the 25-year period from 1966 through 1990 (N = 1,719) were followed forward in time through their complete medical records in the community for first VTE recurrence. RESULTS Four hundred four patients developed recurrent VTE during 10,198 person-years of follow-up. The overall (probable/definite) cumulative percentages of VTE recurrence at 7, 30, and 180 days and 1 and 10 years were 1.6% (0.2%), 5.2% (1.4%), 10.1% (4.1%), 12.9% (5.6%), and 30.4% (17.6%), respectively. The risk of recurrence was greatest in the first 6 to 12 months after the initial event but never fell to zero. Independent predictors of first overall VTE recurrence included increasing age and body mass index, neurologic disease with paresis, malignant neoplasm, and neurosurgery during the period from 1966 through 1980. Independent predictors of first probable/definite recurrence included diagnostic certainty of the incident event and neurologic disease in patients with hospital-acquired VTE. Recurrence risk was increased by malignant neoplasm but varied with concomitant chemotherapy, patient age and sex, and study year. CONCLUSIONS Venous thromboembolism recurs frequently, especially within the first 6 to 12 months, and continues to recur for at least 10 years after the initial VTE. Patients with VTE with neurologic disease and paresis or with malignant neoplasm are at increased risk for recurrence, while VTE patients with transient or reversible risk factors are at less risk.
Downregulation of lncRNA SDPR-AS is associated with poor prognosis in renal cell carcinoma

Renal cell carcinoma (RCC) is a common type of kidney cancer. Normally, surgical treatment can prolong life, but only for patients with early stage tumors. However, it is difficult for early detection strategies to distinguish between benign and malignant kidney tumors. Therefore, potential biomarkers for early diagnosis and prognosis of RCC are needed. Intriguingly, mounting evidence has demonstrated that many long noncoding RNAs (lncRNAs) are strongly linked to cancers. Indeed, promising RCC-associated lncRNA biomarkers have also been identified. However, the functional and prognostic roles of the antisense (AS) serum deprivation response (SDPR) lncRNA (SDPR-AS) in RCC remain largely unknown. The aims of this study were to investigate the expression and prognostic relevance of SDPR-AS in RCC. We uncovered the downregulated expressions of both lncRNA SDPR-AS and its protein-coding gene, SDPR, in RCC tissues compared to the matched normal tissues. Furthermore, SDPR-AS and SDPR expressions were positively correlated. Overexpression and knockdown experiments suggested that SDPR-AS and SDPR were coregulated in RCC cell lines. In addition, overexpression of SDPR-AS suppressed cell migration and invasion, but not cell growth. Furthermore, expression of SDPR-AS was associated with tumor differentiation and lymphatic metastasis. Kaplan–Meier survival and log-rank tests demonstrated the association of elevated expression of SDPR-AS with increased overall survival. In conclusion, our results suggest that the SDPR-AS may serve as a prognostic biomarker and therapeutic target of RCC.
Does Exercise Improve Glycaemic Control in Type 1 Diabetes? A Systematic Review and Meta-Analysis

Objective Whilst regular exercise is advocated for people with type 1 diabetes, the benefits of this therapy are poorly delineated. Our objective was to review the evidence for a glycaemic benefit of exercise in type 1 diabetes. Research Design and Methods Electronic database searches were carried out in MEDLINE, Embase, Cochrane’s Controlled Trials Register and SPORTDiscus. In addition, we searched for as yet unpublished but completed trials. Glycaemic benefit was defined as an improvement in glycosylated haemoglobin (HbA1c). Both randomised and non-randomised controlled trials were included. Results Thirteen studies were identified in the systematic review. Meta-analysis of twelve of these (including 452 patients) demonstrated an HbA1c reduction but this was not statistically significant (standardised mean difference (SMD) −0.25; 95% CI, −0.59 to 0.09). Conclusions This meta-analysis does not reveal evidence for a glycaemic benefit of exercise as measured by HbA1c. Reasons for this finding could include increased calorie intake, insulin dose reductions around the time of exercise or lack of power. We also suggest that HbA1c may not be a sensitive indicator of glycaemic control, and that improvement in glycaemic variability may not be reflected in this measure. Exercise does however have other proven benefits in type 1 diabetes, and remains an important part of its management.
Simple and secure image steganography using LSB and triple XOR operation on MSB

Least Significant Bit (LSB) is a very popular method in the spatial domain of steganographic images. This method is widely used and continues to be developed to date, because of its advantages in steganographic image quality. However, the traditional LSB method is very simple and predictable. It needs a way to improve the security of hidden messages in this way. This research proposes a simple and safe way to hide messages in LSB techniques. Three times the XOR operation is done to encrypt the message before it is embedded on the LSB. To facilitate the process of encryption and decryption of messages, three MSB bits are used as keys in XOR operations. The results of this study prove that this method provides security to messages with very simple operation. The imperceptibility quality of the stego image is also excellent with a PSNR value above 50 dB.
Temporal Event Recognition: An Application to Left Ventricular Performance

The problem of recognition of time-varying events, particularly motion concepts, is discussed and a framework for the abstraction of such concepts is briefly presented. We have designed and implemented a knowledge-based expert system for motion understanding that incorporates several novel features. A frame-based representation, which includes exception handling via similarity links and the organizational primitives IS-A and PART-OF is used to construct a knowledge base of temporal concepts. This knowledge base drives the recognition process that integrates the paradigms of hypothesize-and-trust and competition and co-operation among conceptually adjacent hypotheses. The recognition stra tegy also includes a hypothesis rating scheme that is amenable to empirical performance analysis. This framework has been applied to the problem of human left ventricular performance evaluation from image sequences, and thus the knowledge base also contains much of the current understanding of left ventricular dynamics. This paper describes some aspects of this application in detail and presents a complete analytic of a typical image sequence, emphasizing comparisons between human and computer analysis. Additional justification of the adequacy of the framework has been obtained using empirical studies of the hypothesis rating scheme.
Multi Layer Sparse Coding: the Holistic Way

The recently proposed multi-layer sparse model has raised insightful connections between sparse representations and convolutional neural networks (CNN). In its original conception, this model was restricted to a cascade of convolutional synthesis representations. In this paper, we start by addressing a more general model, revealing interesting ties to fully connected networks. We then show that this multi-layer construction admits a brand new interpretation in a unique symbiosis between synthesis and analysis models: while the deepest layer indeed provides a synthesis representation, the mid-layers decompositions provide an analysis counterpart. This new perspective exposes the suboptimality of previously proposed pursuit approaches, as they do not fully leverage all the information comprised in the model constraints. Armed with this understanding, we address fundamental theoretical issues, revisiting previous analysis and expanding it. Motivated by the limitations of previous algorithms, we then propose an integrated - holistic - alternative that estimates all representations in the model simultaneously, and analyze all these different schemes under stochastic noise assumptions. Inspired by the synthesis-analysis duality, we further present a Holistic Pursuit algorithm, which alternates between synthesis and analysis sparse coding steps, eventually solving for the entire model as a whole, with provable improved performance. Finally, we present numerical results that demonstrate the practical advantages of our approach.
Wavelet based-analysis of alpha rhythm on EEG signal

One of the major frequency rhythm in EEG signal is called alpha rhythm, that indicate relax condition, calm, and awake without much concentration. In this paper we analyzing alpha rhythm using continuous wavelet transform (CWT) to explore the feature of relax condition. We do some scenario in analyzing alpha rhythm, normalizing and segmenting the data. EEG dataset was provided by DEAP. We sort the relax data (labelled with high valence and low arousal by participants) among all data to be observed. First, EEG data are normalized then filtered using band pass filter to get the specific alpha frequency (8–13Hz). Then, we use CWT to transform the signals into time-frequency domain. Entropy and energy of the coefficient wavelet transform are calculate as feature for clustering. From the result, normalized data gave different values. Besides changes the real magnitude information, it give lower accuracy 51.7% than not normalized data 67.2%. We conclude that normalizing data is not necessary especially on subject independent analysis. In additional, clustering result of all data compared with segmented data aren't gave significant differences. Finally, using CWT for feature extraction gives good enough results (67.2%).