Miracle-dz/UniFAR-FTUs huggingface dataset:
An optical-fiber interface to a trapped-ion quantum computer

The trapped-ion quantum computer is an atom-based implementation of a quantum computer that has successfully demonstrated numerous quantum algorithms and the potential for scalability. Fundamental to its operation is the short-range Coulombic interaction among its atomic ion registers, which has led to the development of local, single-chip devices. In this work, we demonstrate the integration of an optical fiber with a planar ion trap, and show the physical interaction between fiber light and the trapped-ion qubit. As the single-mode fiber is well-suited to the transport of single photons, the fiber interface (when augmented by an optical cavity) represents a means to link distantly located quantum computers through a common optical network. Hence, this work represents a step towards the paradigm of distributed quantum computing: self-contained, technically-simple processors may be optically linked together to perform large-scale quantum computation. This thesis is divided into two parts. In the first, we provide a thorough review of ion trap design and a detailed numerical analysis of trapped-ion motion. This theoretical discussion culminates with the development of an electronic technique that permits the arbitrary, in situ positioning of a trapped atom in the ion trap. The positioning ability is an enabling technology for optics integration as it allows for complete freedom in the alignment of the trapped atom with respect to the integrated element. In the second part, the construction of the experimental setup and the integrated “fibertrap” is described. In our experiment, a single 88Sr+ is trapped 670 μm above the end of an optical fiber in a cryogenic (8 K) surface-electrode ion trap. The fiber serves as an integrated source of laser light, which drives the quadrupole qubit transition of 88Sr+. Using in situ translation of the ion, the Gaussian beam profile of the fiber output is imaged, and the fiberion displacement, in units of the mode waist at the ion, is optimized to within 0.13± 0.10 of the mode center despite an initial offset of 3.30± 0.10 arising from fabrication. We also quantify the perturbative effects of the fiber dielectric on ion trap operation. Light-induced charging by 125 μWof 674 nm fiber light is measured as an induced electric field of∼ 10 V/m at the ion, with charging and discharging time constants of 1.6±0.3 s and 4.7±0.6 s. These measurements are of general importance to trapped-ion quantum computing, where the scalability of the platform depends crucially on the feasibility of on-chip optics integration. Thesis Supervisor: Prof. Isaac L. Chuang Title: Professor of Electrical Engineering and Computer Science Professor of Physics
Training bioinspired sensors to classify flows

We consider the inverse problem of classifying flow patterns from local sensory measurements. This problem is inspired by the ability of various aquatic organisms to respond to ambient flow signals, and is relevant for translating these abilities to underwater robotic vehicles. In Colvert, Alsalman and Kanso, B&B (2018), we trained neural networks to classify vortical flows by relying on a single flow sensor that measures a ‘time history’ of the local vorticity. Here, we systematically investigate the effects of distinct types of sensors on the accuracy of flow classification. We consider four types of sensors—vorticity, flow velocities parallel and transverse to the direction of flow propagation, and flow speed—and show that the networks trained using transverse velocity outperform other networks, even when subjected to aggressive data corruption. We then train the network to classify flow patterns instantaneously, using a spatially-distributed array of sensors and a single ‘one time’ sensory measurement. The network, based on a handful of spatially-distributed sensors, exhibits remarkable accuracy in flow classification. These results lay the groundwork for developing learning algorithms for the dynamic deployment of sensory arrays in unsteady flows.
Clinicopathological study and management of benign biliary stricture

Background: A stricture of the biliary tract can be one of the most difficult challenges that a surgeon can face. If unrecognized or managed improperly life threatening complications may develop. The goal of this study is to describe the most appropriate management for benign biliary stricture.Methods: This was a prospective study done on 29 patients diagnosed with benign biliary stricture. Complete biochemical profile, USG-whole abdomen, and other radiological investigations were done to evaluate the detailed etiological condition of the patient. Appropriate surgical intervention was implemented in the patients. Related complications and clinical outcome was observed during the follow up period of 2 years.Results: Mean age of the study population was 45 years (ranging from 22-62 years). Female preponderance was seen in the study. USG of abdomen was done in all the patients (100%). Overall complications were observed in 15 patients.Conclusions: Benign biliary strictures occur more often in women. Complications and clinical outcome depends on the time of diagnosis, matured clinical judgment and appropriate surgical intervention. Hepaticojejunostomy (HJJ) was the common surgical procedure adopted in in 23 patients.
MEMOIR: Multi-class Extreme Classification with Inexact Margin

Multi-class classification with a very large number of classes, or extreme classification, is a challenging problem from both statistical and computational perspectives. Most of the classical approaches to multi-class classification, including one-vs-rest or multi-class support vector machines, require the exact estimation of the classifier's margin, at both the training and the prediction steps making them intractable in extreme classification scenarios. In this paper, we study the impact of computing an approximate margin using nearest neighbor (ANN) search structures combined with locality-sensitive hashing (LSH). This approximation allows to dramatically reduce both the training and the prediction time without a significant loss in performance. We theoretically prove that this approximation does not lead to a significant loss of the risk of the model and provide empirical evidence over five publicly available large scale datasets, showing that the proposed approach is highly competitive with respect to state-of-the-art approaches on time, memory and performance measures.
Miniaturization Design of Millimeter Wave Conformal Array Antenna

With the rapid development of wireless communication technology, more requirements for miniaturized microstrip antennas have been put forward. In this paper, the input impedance of the last element of the array is designed to match the characteristic impedance of the series-fed antenna array to achieve the effect of traveling wave antenna array. The distance between elements can be adjusted to realize the beam control. A 35GHz series-fed conformal antenna array composed of three microstrip radiation units is designed with this method, which decreases the height of the array by 62%. Thirty-two arrays conformal with a cylinder with a diameter of 70mm of equal distance and a 1 point 32 equal power feed network are designed to feed $3\times 32$ microstrip conformal array antenna. The conformal antenna array can achieve omnidirectional radiation on the plane with the angle of 60° between main lobe and axis of the cylinder. The maximum gain difference of the directional diagram is 1.2dB.
Using Digital Smart Board to Overcome Higher Order Thinking Skills Learning Difficulties in Data Handling among Primary School Students

Data Handling at primary school involves gathering and recording information and then presenting it in a way that is meaningful to others. It is a fundamental part of student's mathematical understanding. Initial concepts of Data Handling need to be exposed to students in the early years as these skills will be used and built upon throughout their education. Due to its importance, Data Handling is included in the curriculum at primary level in Malaysia. However results of international assessments which focused on the use of higher order thinking skills showed that Malaysian students performed poorly in Data Handling. Therefore, this study was conducted to identify students’ learning difficulties in Data Handling at each level of higher order thinking skills (HOTS) namely applying, analysing, evaluating and creating. In addition, this study also investigated the use of smart board in overcoming HOTS learning difficulties in Data Handling. Numerous teachers recommend the use of smart board at primary schools since students have the opportunity to explore and learn novel concepts using the technology. Subsequently, it helps to generate dynamic learning experiences and student in school become more motivated about learning when the smart board is used during their lessons. Four experienced year five mathematics teachers and thirty year five students from a primary school were involved in the study. The students and teachers were selected based on purposive sampling. An interview protocol and rubric for cognitive domain in HOTS were used during the data collection phase. In addition, semi-structured interview sessions with the teachers and students as well as document analysis of the students’ mathematics exercise sheets in Data Handling were carried out by the researcher to investigate the students’ learning difficulties at each level of HOTS in Data Handling. It is hoped that the results from this study can contribute towards development of strategies, methods or programs using smart board to enhance HOTS in Data Handling among primary school students in Malaysia.
CFL-1, a novel F-box protein with leucine-rich repeat may interact with UNC-10 for the regulation of defecation and daumone response in Caenorhabditis elegans

ABSTRACT Previously we reported that CFL-1, the single LRR-type F-box protein in the Caenorhabditis elegans genome, affected defecation behavior and daumone response. CFL-1 is highly homologous to the FBXL20 in mammals, which regulates synaptic vesicle release by targeting its substrate Rim1 for ubiquitin-mediated degradation. The worm homolog of Rim1 is UNC-10, a presynaptic membrane protein that triggers synaptic vesicle fusion through interaction with RAB-3 GTPase. To examine if CFL-1 exerts its modulatory effect on the defecation and daumone response via ubiquitination of UNC-10, we performed RNAi knock-down of CFL-1 in the unc-10(e102) mutant background. We noticed additive increase in defecation interval when the activities of both CFL-1 and UNC-10 were compromised. Also, the degree of dauer formation upon daumone treatment in unc-10 mutants treated with CFL-1 RNAi decreased further than the level observed in untreated mutants or wild type N2 worms with CFL-1 RNAi knock-down. Our data suggest that CFL-1 affects defecation frequency and daumone response in C. elegans through the ubiquitination of UNC-10.
Congestion Management Using Optimal Transmission Switching

In this paper, a multiobjective-based congestion management (CM) methodology is proposed using the optimal transmission switching (OTS) strategies considering the minimization of the total operating cost and the maximization of probabilistic reliability as two conflicting objectives. To improve the efficiency of the transmission network, this paper considers the possibility of changing the topology of the transmission system using the transmission switching strategy. The OTS can increase the economic efficiency of power dispatch with the existing infrastructure. The objective of OTS is to find the most influential lines as candidate lines for the disconnection. System operators can change the network topology to increase the transfer capacity, to improve voltage profiles, and also to improve the reliability of the power system. In this paper, the loss of load probability reliability index is used to indicate the probability that an electrical power system is not able to serve the prescribed load within the concerned period of time. Here, the transmission lines that should be switched are determined by using the security-constrained ac optimal power flow. The proposed optimization problem is solved using the hybrid of evolutionary and stochastic programming approaches. The output of the proposed OTS is to find the transmission lines that have to be switched so that the congestion in the system can be relieved. A multiobjective optimization approach is used to generate the Pareto optimal solutions. The suitability and effectiveness of the proposed CM approach have been examined on IEEE 30- and 300-bus test systems.
Classifying Positive or Negative Text Using Features Based on Opinion Words and Term Frequency - Inverse Document Frequency

The contents in website and social networks are rapidly generated. The opinions and reviews can be analyzed and classified into two classes, positive or negative opinions, by machine learning methods. However, the main issue is how to representing each text as a proper set of variables, a p-feature vector, so that the successful classifiers can be obtained by one of the supervised learning approaches with its suitable parameter setting. In this study, a two-feature vector representing positive and negative moods in each text was prepared by using lists of positive and negative words, and then combined with term frequency - inverse document frequency (TF-IDF) features. kNN and SVM classifiers were comparatively built by this set and also other baseline set to predict each test vector and measure their effectiveness. Data of text Reviews from Yelp, Amazon and IMDB, were experimented with 10-fold cross validation in parameter variation and feature set reduction using PCA. The best Accuracy results across these three datasets, ~0.81-0.87, were yielded by SVM classifiers with each size of the reduced feature sets that is very smaller than the original size.
Finding the pose of an object in a hand

The author addresses the problem of finding the pose of an object grasped by a hand. The problem can be stated simply: given a hand grasping an object, and given a model of that object, determine the position and orientation of the object with respect to the hand. The method described uses only joint angle and torque sensing, and is able to localize two-dimensional objects grasped by the Utah-MIT dextrous hand. <<ETX>>
Brain Atrophy Is Associated with Disability Progression in Patients with MS followed in a Clinical Routine

BACKGROUND AND PURPOSE: The assessment of brain atrophy in a clinical routine is not performed routinely in multiple sclerosis. Our aim was to determine the feasibility of brain atrophy measurement and its association with disability progression in patients with MS followed in a clinical routine for 5 years. MATERIALS AND METHODS: A total of 1815 subjects, 1514 with MS and 137 with clinically isolated syndrome and 164 healthy individuals, were collected retrospectively. Of 11,794 MR imaging brain scans included in the analysis, 8423 MRIs were performed on a 3T, and 3371 MRIs, on a 1.5T scanner. All patients underwent 3D T1WI and T2-FLAIR examinations at all time points of the study. Whole-brain volume changes were measured by percentage brain volume change/normalized brain volume change using SIENA/SIENAX on 3D T1WI and percentage lateral ventricle volume change using NeuroSTREAM on T2-FLAIR. RESULTS: Percentage brain volume change failed in 36.7% of the subjects; percentage normalized brain volume change, in 19.2%; and percentage lateral ventricle volume change, in 3.3% because of protocol changes, poor scan quality, artifacts, and anatomic variations. Annualized brain volume changes were significantly different between those with MS and healthy individuals for percentage brain volume change (P < .001), percentage normalized brain volume change (P = .002), and percentage lateral ventricle volume change (P = .01). In patients with MS, mixed-effects model analysis showed that disability progression was associated with a 21.9% annualized decrease in percentage brain volume change (P < .001) and normalized brain volume (P = .002) and a 33% increase in lateral ventricle volume (P = .004). CONCLUSIONS: All brain volume measures differentiated MS and healthy individuals and were associated with disability progression, but the lateral ventricle volume assessment was the most feasible.
Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data

In recent decade, many state-of-the-art algorithms on image classification as well as audio classification have achieved noticeable successes with the development of deep convolutional neural network (CNN). However, most of the works only exploit single type of training data. In this paper, we present a study on classifying bird species by exploiting the combination of both visual (images) and audio (sounds) data using CNN, which has been sparsely treated so far. Specifically, we propose CNN-based multimodal learning models in three types of fusion strategies (early, middle, late) to settle the issues of combining training data cross domains. The advantage of our proposed method lies on the fact that We can utilize CNN not only to extract features from image and audio data (spectrogram) but also to combine the features across modalities. In the experiment, we train and evaluate the network structure on a comprehensive CUB-200-2011 standard data set combing our originally collected audio data set with respect to the data species. We observe that a model which utilizes the combination of both data outperforms models trained with only an either type of data. We also show that transfer learning can significantly increase the classification performance.
Grasp Constraint Satisfaction for Object Manipulation using Robotic Hands

For successful object manipulation with robotic hands, it is important to ensure that the object remains in the grasp at all times. In addition to grasp constraints associated with slipping and singular hand configurations, excessive rolling is an important grasp concern where the contact points roll off of the fingertip surface. Related literature focus only on a subset of grasp constraints, or assume grasp constraint satisfaction without providing guarantees of such a claim. In this paper, we propose a control approach that systematically handles all grasp constraints. The proposed controller ensures that the object does not slip, joints do not exceed joint angle constraints (e.g. reach singular configurations), and the contact points remain in the fingertip workspace. The proposed controller accepts a nominal manipulation control, and ensures the grasping constraints are satisfied to support the assumptions made in the literature. Simulation results validate the proposed approach.
The Perceived Benefits of an Artificial Intelligence–Embedded Mobile App Implementing Evidence-Based Guidelines for the Self-Management of Chronic Neck and Back Pain: Observational Study

Background Chronic musculoskeletal neck and back pain are disabling conditions among adults. Use of technology has been suggested as an alternative way to increase adherence to exercise therapy, which may improve clinical outcomes. Objective The aim was to investigate the self-perceived benefits of an artificial intelligence (AI)–embedded mobile app to self-manage chronic neck and back pain. Methods A total of 161 participants responded to the invitation. The evaluation questionnaire included 14 questions that were intended to explore if using the AI rehabilitation system may (1) increase time spent on therapeutic exercise, (2) affect pain level (assessed by the 0-10 Numerical Pain Rating Scale), and (3) reduce the need for other interventions. Results An increase in time spent on therapeutic exercise per day was observed. The median Numerical Pain Rating Scale scores were 6 (interquartile range [IQR] 5-8) before and 4 (IQR 3-6) after using the AI-embedded mobile app (95% CI 1.18-1.81). A 3-point reduction was reported by the participants who used the AI-embedded mobile app for more than 6 months. Reduction in the usage of other interventions while using the AI-embedded mobile app was also reported. Conclusions This study demonstrated the positive self-perceived beneficiary effect of using the AI-embedded mobile app to provide a personalized therapeutic exercise program. The positive results suggest that it at least warrants further study to investigate the physiological effect of the AI-embedded mobile app and how it compares with routine clinical care.
Specific Management Applications Inside a SAP System for Oil and Gas Company

The accounting activities environment, the business itself is changing on a daily basis and the technologies are in constant update. The beginning of the discussions, for this paper, goes to how we can extract data from an ERP system, how we can calculate KPIs (Key Performance Indicators) based on these data and how we can adapt and create one tool in this respect. According with management’s decision of the oil and gas company the old KPI tool will be moved to SAP – ZKPI application. The Functional and Technical Specification was adapted for SAP (System, Applications and Products) environment, because this new tool will be adapted in this system. For ZKPI tool, the programming language used was ABAP (Web Dynpro technologies). In this article, it is presented a concept (a particular one) which is already used in oil &gas industry, a model of collecting data from an ERP (invoices, orders, data from papers regarding fixed assets, etc.), and how we can use these data for KPI’s. Why do we need these collected data? In order to reach the target for accounting employees and managers regarding personal development goals – PDS, these data are used. One of the major aspects in an accounting department is the number of data entered in one SAP ERP system, based on team composition, as well as knowledge of economic activity and SAP knowledge. All accounting activities in a calendar month are monitored and counted in order to meet certain KPI’s agreed with superiors at the beginning of the year. In the following, we will present, contextually, the data that led to the implementation of this application.
RF Engineering for Wireless Networks: Hardware, Antennas, and Propagation (Communications Engineering)

Introduction Basics of Wireless Communications Basics of Wireless Local Area Networks Radio Transmitters and Receivers Antennas Propagation Indoor Networks Outdoor Networks Afterword Appendix 1: Regulatory Issues Appendix 2: Measurement Appendix 3: Reflection and Appendix 4: The Lorentz Gauge Appendix 5: Power Density Appendix 6: Conventional E & M Appendix 7: Table of Symbols Used in the Text Index
A Refined Simple Power Analysis Attack on ECC with Countermeasures

Elliptic Curve Cryptography (ECC) is becoming widely deployed in embedded cryptographic devices. However, simple power analysis (SPA) attacks may retrieve secret keys by exploiting the power consumption of ECC devices. To resist the SPA attack there appear a number of countermeasures and the most widely used methods are Montgomery ladder and doubleand-add-always algorithm. This paper proposes a refined simple power analysis attack to these countermeasures. Experimental results on smart cards demonstrate that this attack method can retrieve secret keys by distinguishing the power consumption of different jump instructions if the implementation of ECC’s countermeasure is not carefully designed. The experiments also demonstrate that the proposed countermeasure in this paper can resist this kind of SPA attack. Keywords—ECC; cryptography; SPA; scalar multiplication