Miracle-dz/UniFAR-FTUs huggingface dataset:
A K-Band Low-Loss High-Isolation CMOS SPDT Switch Based on Multi-Tap Inductor Technique

A K-band single-pole double-throw (SPDT) transmit/receive (T/R) switch is presented in this paper. To improve bandwidth while decreasing chip area, the switch is designed using multi-tap inductor together with capacitors that replaces the λ/4 transmission line. It achieves a measured minimum insertion loss of 1.62 dB at 21 GHz, with an isolation of 24 dB. Measured output port to port isolation is better than 22 dB. The return losses at all ports are better than 10 dB from 14 to 28 GHz with a P1dB of 8 dBm. It is fabricated in 65 nm CMOS and occupies a core chip area of 0.035 mm2.
Electro-Thermal-Mechanical Multiphysics Coupling Failure Analysis Based on Improved IGBT Dynamic Model

Failure of the power device can have a very large impact on the entire power circuit. IGBT as the main power device, ensuring its reliability becomes more and more important. In different environments, there will be large differences in the failure rate of IGBTs. Therefore, studying the failure mechanism of IGBT is of great significance to ensure the reliability of IGBT. Firstly, the IGBT dynamic model is constructed and the model is improved to correct the power consumption error. Then the IGBT finite element model is used to analyze the electro-thermal-force of different material layers, at the same time, the distribution of stress and temperature between different materials are analyzed. Finally, the JavaScript script is used to generate random defects of the solder layer, and the effects of different defects of the solder layer on the weakest part of the IGBT are studied, including voids, cracks, and solder layer falling off. The results of the analysis show that after the IGBT defects reach a certain level, the solder layer voids, the solder layer fall off, and the solder layer cracks have a great influence on the IGBT junction temperature and stress. Among them, shedding and cracks have a greater influence on the stress of the solder layer.
Detection of Motor Deficits in Children with Autism Spectrum Disorder using FTT

The aim of this study is to examine the speed of finger tapping task of control group and target group. It is very important to assess the teenager's motor control in detecting possible motor deficits. The Finger Tapping Test (FTT) is a commonly used test and is a neuropsychological test which is design to measure the motor control. The sample for this study included 25 teenagers from control group and 25 teenagers from target group having their age between 13 to 16 years having mean age as 14.64. To detect the fine motor deficits in children with Autism we are going to calculate the threshold values for “Hits” and “Time” for the Control Group and then compare with the Target Group. For data collection we have used the Finger Tapping Test of Psychology Experiment Building Language (PEBL) software. The objective of this present study is to detect the motor deficits in teenagers having autism spectrum disorder which will then help to the doctors to assess and providing essential information for the development of therapies. The study also showed that older teenager's performance is significantly better than younger teenagers.
A Passive Equalizer Design for On-Interposer Differential Interconnects in 2.5D/3D ICs

This paper introduce a passive equalizer for compensating the frequency characteristics of on-interposer differential interconnects in TSV-based 3-D ICs. To facilitate the design of the equalizer, we develop a simplified odd-mode circuit model, which only consists of four elements. The simplified model can capture the electrical performance of the differential interconnects quickly and accurately up to 20 GHz. Then, the equalizer parameters are designed by using the time-domain inter-symbol interference (ISI) cancellation method, and then used for extracting the geometrical parameters. The functionality of the passive equalizer is validated in both frequency- and time-domain.
Sustainability Implementation in the Luxury Fashion Industry: Assessing Annual Sustainability Reports

The luxury fashion industry shows a healthy growth of 7% by exceeding €225 billion in 2014. Nonetheless, the luxury fashion industry has a significant environmental footprint. Sustainability reporting receives a prominence. Nevertheless, despite the proliferation of these reports, there is a lack of consensus because of unsuitable definition of performance indicators, and lack of transparency in communication. Therefore, this study makes an original contribution by assessing the comprehensiveness of sustainability reports, and categorizing ‘NOT addressed’ indicators in terms of aspects. Linkage among practices, environmental impact, and carbon intensity of the industry allows this study to propose suggestions to enhance sustainability.
Intestinal bile acid reabsorption in health and disease

56 Abstract With the improved treatment of the pulmonary complications of cystic fibrosis (CF), gastrointestinal problems have become more important in the morbidity in CF. A hallmark of the gastrointestinal phenotype of CF, apart from pancreatic insufficiency, is a disruption of bile acid homeostasis. Bile acid homeostasis is important for many gastrointestinal processes including fat absorption, inflammation, microbial composition, as well as regulation of whole body energy metabolism. This review describes the impairment of bile acid homeostasis in CF, its possible consequences for gastrointestinal and metabolic complications and its potential as a target for therapy.
Offshore Electromagnetic Spectrum Distribution Prediction Model Based on Ray Tracing Method and PM Wave Spectrum

Electromagnetic spectrum distribution prediction is the basis for the development of wireless communication technology. Due to the high randomness of the rough sea surface, it is difficult to accurately predict the offshore electromagnetic spectrum distribution. We present a prediction model of offshore electromagnetic spectrum distribution based on ray tracing method and Pierson-Moskowitz (PM) wave spectrum model. Based on the antenna attitude and the antenna directional pattern, the field strength calculation formulas of the electromagnetic wave sight line ray (SLR) propagation and the electromagnetic wave reflecting line ray (RLR) propagation off the sea surface are established. As the sea surface and the antenna attitude change, the effective rays from the transmitting antenna to the receiving antenna also change. This paper focus on the electromagnetic spectrum distribution prediction by calculating the effective angle (EA) of the transmitting antenna. We relate the EA to the antenna tilt angle and the antenna directional pattern, and combine the PM wave spectrum to establish the offshore electromagnetic spectrum distribution prediction models. Finally, we simulated the electromagnetic spectrum distribution prediction based on specific PM wave spectrum data.
CRAFTING ECONOMIC DRIVERS FOR SUSTAINABLE LOCAL AREAS IN A GLOBALISING REGIONAL ECONOMY: SYDNEY AS A CASE STUDY

Sydney is facing its future. It is the largest city and one of the fastest growing metropolitan areas of Australia. Like many newly emerging global cities, Sydney is dealing with its past economy while it shapes its future economic scenarios. Spatially uneven outcomes seem inevitable as metropolitan regions globalise. But for Sydney, this transition is especially painful when the nation bases its sociopolitical fabric on equal outcomes – ‘a fair go’ for all citizens. In response to change, Sydney has adopted a new approach to metropolitan planning that has moved from a traditional land use system to one that comes to grips with socioeconomic issues.
Emotion recognition from audio, dimensional and discrete categorization using CNNs

Emotions can not be estimated with text alone as irony, humour, cadence etc. are important components of a speech. So, text has a limited capacity in conveying emotions. There are various work in sentiment analysis with text and speech but this project deals with the identification of mood in a non-discrete way. The traditional method of prediction emotion where labeling a certain number of discrete emotions is obviously a method of classification using various learning techniques. But can there be other techniques of describing and understanding emotions in speech? This paper first discusses the different dimensions of emotions and compares them with previous similar work (Chung and Yoon(2012)) [6], they classified the user's data into classes of 2 types (high, normal, low) and (high and low) with 3 and 2 classes respectively for both Valence and Arousal. Their performance was 53.4% and 51.0% for three classes and 66.6% and 66.4% accuracy for 2 classes on Valence and Arousal upon exact emotion prediction on DEAP dataset. Using the same dataset “Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset” [5] concludes a CNN model providing 66.79% and 57.58% for exact Valence and Arousal prediction. The valence and arousal model follow accurate prediction on 2 or 3 classes, but we had a different approach of predicting emotions by placing a particular emotion in its respective quadrant in 2D valence and arousal axis. However, we had around 50-55% accuracy for the discrete model and our approach on quadrant prediction resulted in 76.2% accuracy using two different CNN architectures on RAVDESS dataset.
Detection of Chromosomal Arms 1p/19q Codeletion in Low Graded Glioma using Probability Distribution of MRI Volume Heterogeneity

Glioma is a type of brain tumor that leaves the subject with very low survival rate. However the evidence showed that co-deletion of chromosome arms 1p/19q in low-grade glioma (LGG) revealed good response to therapy in LGG and is associated with longer survival rate. Therefore, predicting 1p/19q status is essential for effective treatment planning of LGG. There are several studies related with predicting deletion of chromosomal arms 1p/19q using multimodal medical images. Our study aims to classify 1p/19q co-deleted LGG status based on 3D volumetric probability distribution of glioma. Dataset are collected from TCIA public access. Study subjects included preoperative postcontrast-T1-W (T1C) and T2-W MRIs who had biopsy-proven 1p/19q status. A total of 159 grade-II and grade III LGG (57 non-deleted and 102 co-deleted) with 3 MRI slices in each subject were utilized in our study. The proposed method utilized statistical moments to obtain moment generating function (MGF) and characteristic function (CF). The optimal range of argument values were derived from MGF and CF for which the probability distribution of two classes were easily distinguishable. KS statistical test verified that the data of two classes have different distributions. Due to imbalanced dataset RUSboost classification was performed that yields better classification performance with MGF features compared to CF features. The average classification performance on the unseen test set with T1-W and T2-W MGF features were 93.22% (precision), 84.6% (recall), and 87.1% (accuracy) for grade-II and 90% (precision), 86% (recall), and 84% (accuracy) for grade-III.
Utilizing Gene Co-expression Network for Identifying Subnetwork Biomarkers for Cancer

Cancer is one of the major causes of death worldwide. Gene biomarkers identification for diagnosis, prognosis or predictive cancer is challenging work. This work aims to study the applicability of gene co-expression network (GCN) to identify gene subnetwork biomarkers for cancer using a previously developed subnetwork-based method. Four lung cancer expression datasets, gene-set, protein-protein interaction (PPI) and gene-gene interaction (GGI) data from public databases were used. The GCN was constructed using two criteria. The GCN constructed by using whole genes in the expression data with minimum spanning of the interaction within the network termed MST-based Gene Co-expression Network (MST-GCN) and the GCN was constructed by using gene members of a certain gene-set termed a Gene-set-based Gene Co-expression Network (gGCN). The subnetworks that resulted from MST-GCN and gGCN were compared with subnetworks that resulted from PPI and GGI data. The identified subnetworks were evaluated by classification performance and the overlapped gene with cancer related genes retrieved from a public database. The gGCN resulted in subnetworks that improved classification performance when compared with other network data. The identified subnetworks results from GGI contained more lung cancer related genes while the results from GCN and PPI contained more well-known lung cancer related genes.
One-Chip CMOS Terahertz Transceiver

In the future, terahertz communication using the 300 GHz band has attracted attention from the expectation that the unallocated frequency band exceeding 275 GHz will be available. In terahertz band communication using ultrahigh frequency, compound semiconductors having excellent high frequency performance have been used in previous researches. Nevertheless, research on wireless transceivers using silicon integrated circuits, in particular CMOS integrated circuits, is increasing in this 300 GHz band. This article outlines one-chip terahertz transceiver technology using mass-produced silicon integrated circuits, its applications, and the future.
Pocket engineered electrostatically doped tunnel field effect transistor

In this paper, we propose the use of pockets to improve the performance of an electrostatically doped double-gate tunnel field effect transistor. The required n-i-p+structure for tunneling is formed on a thin intrinsic silicon film by electrostatic doping based on charge plasma concept. The source, drain and gate electrodes of different workfunctions are employed to form the p-type, n-type and intrinsic regions. Analysis using 2D TCAD simulations suggest that the proposed structure can significantly overcome the low ION and ambipolarity problem in conventional dopingless tunnel field effect transistor (DLTFET). The n-type pocket in channel and p-type pocket in source provides noteworthy improvement in the transfer characteristics of pocket engineered (PE) DLTFET. The proposed PE-DLTFET offers higher ION(2.4x10−5A), high ION/IOFF ratio ∼109and lower subthreshold slope (SS) of 42 mV/dec. We also propose an n-type pocket in the channel adjacent to the drain region to reduce the inherent ambipolarity in DLTFET. The results show that PE-DLTFET effectively suppresses ambipolarity and offers 25x higher ION as compared to DLTFET, making it a viable candidate for switching applications.
Computation Scheduling for Distributed Machine Learning With Straggling Workers

We study scheduling of computation tasks across <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> workers in a large scale distributed learning problem with the help of a master. Computation and communication delays are assumed to be random, and redundant computations are assigned to workers in order to tolerate stragglers. We consider sequential computation of tasks assigned to a worker, while the result of each computation is sent to the master right after its completion. Each computation round, which can model an iteration of the stochastic gradient descent (SGD) algorithm, is <italic>completed</italic> once the master receives <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> distinct computations, referred to as the <italic>computation target</italic>. Our goal is to characterize the <italic>average completion time</italic> as a function of the <italic>computation load</italic>, which denotes the portion of the dataset available at each worker, and the computation target. We propose two computation scheduling schemes that specify the tasks assigned to each worker, as well as their computation schedule, i.e., the order of execution. Assuming a general statistical model for computation and communication delays, we derive the average completion time of the proposed schemes. We also establish a lower bound on the minimum average completion time by assuming prior knowledge of the random delays. Experimental results carried out on Amazon EC2 cluster show a significant reduction in the average completion time over existing coded and uncoded computing schemes. It is also shown numerically that the gap between the proposed scheme and the lower bound is relatively small, confirming the efficiency of the proposed scheduling design.
A K-Band High-Gain LNA in 0.13-µm RF CMOS

This paper presents a K-band low-noise amplifier (LNA) for automotive radar applications. The LNA features high gain and low noise using a 3-stage cascode topology, which has been designed in a 0.13-µm RF CMOS. According to the measurement results, the maximum gain is 31.2 dB at 24 GHz and the -3-dB bandwidth has 1.7 GHz from 23.8 to 25.5 GHz. The NF achieves 4.9 dB at 24.5 GHz. The input and output return losses S11 and S22 at 24 GHz are -13.5 dB and -10.5 dB, respectively. The total power consumption is 22.3 mW at a supply of 1.5 V. The chip occupies a whole area of 0.93×0.57 mm2.
REMO: A Fast Region-and Morphology-based Scene Text Detector

Scene text detection remains a challenging problem as natural scene images are often displayed with complex issues. These include uneven lighting, blurring, varying text sizes and fonts, perspective distortion and oriented text. In recent years, there has been a growing focus on oriented text detection to address the prevalent issue of perspective distortion in natural scene text. However, most of these proposed methods are computationally expensive and slow. Therefore, they are not applicable to real-time detection, which is a crucial requirement for many practical applications—such as in visual navigation for autonomous vehicles. In this paper, we present a novel morphology-and machine learning-based method, the Region Extracting Morphological Operation (REMO) natural scene text detector. The proposed method is not only capable of detecting perspective-distorted and oriented text effectively, but is also robust and extremely fast. REMO's detection speed is comparable to other methods with state-of-the-art speeds. It is capable of processing up to 46.5 FPS while running on a mid-tier NVIDIA GTX 1060 GPU.
Hybrid adaptive decision-making model based on fuzzy logic and artificial immune systems

In modern decision support systems (DSS) characterized by processing large amounts of information in conditions of uncertainty, intellectualization of data processing and analysis is required, as well as the use of efficient technologies, methods, and decision-making models. The article suggests a decision-making model (DMM), presented in the form of rules for fuzzy inference, trained and adapted on highperformance systems using artificial immune systems. The process of learning and adaptation consists in setting the form and parameters of membership functions, as well as the parameters and structure of the base of fuzzy rules.
Sketching Affordances for Human-in-the-loop Robotic Manipulation Tasks

In order for robots to perform complex autonomous manipulation tasks in natural human environments, they must be able to carry out dexterous actions, beyond pick-andplace, on a wide variety of objects. The actions which are available to a given agent over a set of objects are called object affordances. The concept of affordance was first introduced by psychologist J. J. Gibson [1] and has garnered much interest in the robotics community in recent years [2], [3]. How to best acquire these affordances is an open problem in robotics. Leveraging human knowledge of object affordances can enable robots to interact with a variety of common household objects to perform complex tasks, such as making a cup of coffee. We propose to enable a human user, without expert knowledge about robotics and programming, to transfer knowledge about affordances in a given scene to a robot. To this end, we propose an easy-to-use system to acquire object geometries and their associated affordances through sketching on a graphical interface. This allows users to interact with robotic systems by utilizing sketch-based techniques to provide a straightforward user interface, as shown in Figure 2. The user sketches the geometry of the object and its affordances. During task execution, when the robot encounters the objects for which it has affordance information, it can execute the affordances by registering the object geometries to its RGB-D data and then performing actions sequentially to achieve the goal. Sketching is a low overhead means of rapid communication with the robot which can quickly provide information about the environment and the actions available to the robot. It provides the user with the ability to quickly demonstrate unseen tasks to the robot in the form of affordance templates [4]. Our system facilitates a human-in-the-loop approach which enables the robot to perform a large variety of manipulation tasks on different objects. Access to object 3D mesh models can assist in object pose estimation, but many methods for this task [5]–[7] rely on synthetically designed mesh models. In cluttered scenes, sketching over RGB-D data from the robot can provide precise geometries with very little overhead. Maghoumi et al. introduced a sketch-based system to extract 3D geometries from point clouds in cluttered scenes which recovers
Efficient Mining of High Utility Itemsets on Data Streams Efficient Mining of High Utility Itemsets on Data Streams

Since there are many applications in the form of data streams, such as sensor network, stock analysis, mining useful patterns from a data stream is an important issue nowadays. However, it is a difficult problem because of some limitations in the data stream environment. A new issue, called utility mining, for mining interesting pattern which is profitable for users is suggested in recent years. In the mining of high utility itemsets, the utility and the sales quantity of each item could be arbitrarily number, so many methods applied to frequent itemsets mining cannot be used anymore. In this thesis, we propose MHUI_TransSW and MHUI_TimeSW to mine high utility itemsets on a data stream in two types of sliding window. We use item information, i.e. TIDlist or Bitvector of 1-itemsets, and lexicographical tree to improve the efficiency of THUI-Mine. The experiment results show that our approach efficiently find the high utility itemsets not only in execution time but also in memory space.
Secant varieties to high degree Veronese reembeddings, catalecticant matrices and smoothable Gorenstein schemes

We study the secant varieties of the Veronese varieties and of Veronese reembeddings of a smooth projective variety. We give some conditions, under which these secant varieties are set-theoretically cut out by determinantal equations. More precisely, they are given by minors of a catalecticant matrix. These conditions include the case when the dimension of the projective variety is at most 3 and the degree of reembedding is sufficiently high. This gives a positive answer to a set-theoretic version of a question of Eisenbud in dimension at most 3. For dimension four and higher we produce plenty of examples when the catalecticant minors are not enough to set-theoretically define the secant varieties to high degree Veronese varieties. This is done by relating the problem to smoothability of certain zero-dimensional Gorenstein schemes. e-mail addresses: wkrych@mimuw.edu. pl and jabu@mimuw.edu. pl address: Institut Fourier, 100 rue des Maths, BP 74, 38402 St Martin d’Hères Cedex, France keywords: secant variety, catalecticant minors, Veronese variety, Veronese reembeddings, cactus variety, smoothable zero-dimensional schemes. AMS Mathematical Subject Classification 2010: Primary: 14M12; Secondary: 13H10, 14M17.