Miracle-dz/UniFAR-FTUs huggingface dataset:
I T ] 1 6 M ay 2 01 9 1 Principal Component Analysis Based Broadband Hybrid Precoding for Millimeter-Wave Massive MIMO Systems

Hybrid analog-digital precoding is challenging for broadband millimeter-wave (mmWave) massive MIMO systems, since the analog precoder is frequency-flat but the mmWave channels are frequencyselective. In this paper, we propose a principal component analysis (PCA)-based broadband hybrid precoder/combiner design, where both the fully-connected array and partially-connected subarray (including the fixed and adaptive subarrays) are investigated. Specifically, we first design the hybrid precoder/combiner for fully-connected array and fixed subarray based on PCA, whereby a low-dimensional frequency-flat precoder/combiner is acquired based on the optimal high-dimensional frequency-selective precoder/combiner. Meanwhile, the near-optimality of our proposed PCA approach is theoretically proven. Moreover, for the adaptive subarray, a low-complexity shared agglomerative hierarchical clustering algorithm is proposed to group the antennas for the further improvement of spectral efficiency (SE) performance. Besides, we theoretically prove that the proposed antenna grouping algorithm is only determined by the slow time-varying channel parameters in the large antenna limit. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art schemes in SE, energy efficiency (EE), bit-error-rate performance, and the robustness to time-varying channels. Our work reveals that the EE advantage of adaptive subarray over fully-connected array is obvious for both active and passive antennas, but the EE advantage of fixed subarray only holds for passive antennas. Index Terms Hybrid precoding, massive MIMO, OFDM, millimeter-wave, adaptive subarray, energy efficiency Y. Sun, Z. Gao, and H. Wang are with Beijing Institute of Technology, Beijing, China (email: gaozhen16@bit.edu.cn). B. Shim is with Institute of New Media and Communications School of Electrical and Computer Engineering, Seoul National University, Seoul, Korea. G. Gui is with College of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China. G. Mao is with School of Computing and Communication, The University of Technology Sydney, Australia. F. Adachi is with Dept. of Electrical and Communications Engineering Tohoku University, Sendai, Japan. This paper was presented in part at the IEEE GLOBECOM’18 [37], [38].
Condensation-Net: Memory-Efficient Network Architecture With Cross-Channel Pooling Layers and Virtual Feature Maps

Lightweight convolutional neural networks is an important research topic in the field of embedded vision. To implement image recognition tasks on a resource-limited hardware platform, it is necessary to reduce the memory size and the computational cost. The contribution of this paper is stated as follows. First, we propose an algorithm to process a specific network architecture (Condensation-Net) without increasing the maximum memory storage for feature maps. The architecture for virtual feature maps saves 26.5% of memory bandwidth by calculating the results of cross-channel pooling before storing the feature map into the memory. Second, we show that cross-channel pooling can improve the accuracy of object detection tasks, such as face detection, because it increases the number of filter weights. Compared with Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and 1.5% for full-precision networks when the false-positive rate is 0.1. Last but not the least, the analysis results show that the overhead to support the cross-channel pooling with the proposed hardware architecture is negligible small. The extra memory cost to support Condensation-Net is 0.2% of the total size, and the extra gate count is only 2.1% of the total size.
Facet-Defining Inequalities for Multi-Module Survivable Network Design Problem

Capacitated survivable network design (SND), i.e., designing a network that can survive edge failures with minimum flow-routing plus capacity-installation cost, is a fundamental problem in network science and its applications. In this paper, we study a highly applicable form of the SND, referred to as the multi-module SND (MM-SND), in which transmission capacities on edges can be sum of integer multiples of differently sized capacity modules. For the first time, we formulate the MM-SND as a mixed integer program (MIP) using preconfigured-cycles (p-cycles) to reroute flow on failed edges. We derive several classes of valid inequalities for this MIP, and show that the valid inequalities previously developed in the literature for the single-module SND are special cases of our inequalities. Furthermore, we show that our valid inequalities are facet-defining for the MM-SND in many cases. Our computational results, using a heuristic separation algorithm, show that these inequalities are very effective in solving the MM-SND. In particular they are more effective than compared to using single-module inequalities alone.
DICOM Implementations for Digital Radiography 1

Cost-effective deployment of digital radiographic (DR) facilities depends on efficient image management and soft-copy reading. Requirements for productivity and quality led the Digital Imaging and Communications in Medicine (DICOM) Standards Committee to extend the standard to include a family of image storage objects that specifically addressed the needs of DR. These digital x-ray (DX) objects incorporate lessons learned from experience with integrating computed radiography (CR), picture archiving and communications systems (PACS), and workstations. Factors addressed include the mandatory incorporation of information required for correct routing of images, appropriate organization of images for display (driving hanging protocols), and consistent appearance of gray-scale contrast in a distributed environment. The use of coded terminology to describe projections, techniques, and anatomic structures was emphasized. This chapter describes the requirements and design of the DX family of image storage objects and the mechanisms by which the equipment can gather the necessary information efficiently. Advice is given to users in the form of purchasing guidelines, which emphasize key factors in the successful deployment of integrated DR equipment. The status and the adoption of the DX objects by vendors are reviewed, and the reasons for the relatively slow adoption of the objects are considered.
The Effect of Rainwater Harvesting on Reducing Poverty ∗

This paper explores the potential of rainwater harvesting as an instrument to reduce poverty in areas without immediate access to an improved water source. Using a unique Brazilian dataset, we find that building rainwater harvesting infrastructure at the homestead does reduce household poverty through three channels: a time allocation effect, an agricultural production effect, and a livestock production effect. Households spend less time on fetching water from distant sources, and savings in time are also allocated to other productive activities. An increase in the households’ water supply allows them to scale-up agriculture and livestock production, thus increasing wealth and alleviating income poverty. JEL Classification: I32, I38, L95, Q25
Chapter 39 Appraisal Theories : 1 How Cognition Shapes Affect into Emotion

1 Support is acknowledged from National Institute of Mental Health grant MH 50074 and National Science Foundation grant BCS 0518835. Correspondence can be directed to Gerald Clore, 102 Gilmer Hall, P.O. Box 400400, Charlottesville, VA 22904-4400. gclore@virginia.edu. deer came quickly, and the whole sequence from startle to categorization took only an instant. But then he experienced a novel, surprising sensation as the hair on the back of his neck stood up. Despite the cognitive reassurance that it was only a deer, the body was apparently still preparing for a nameless threat.
WASP-30b: A 61 MJup BROWN DWARF TRANSITING A V = 12, F8 STAR

We report the discovery of a 61-Jupiter-mass brown dwarf (BD), which transits its F8V host star, WASP-30, every 4.16 days. From a range of age indicators we estimate the system age to be 1–2 Gyr. We derive a radius (0.89 ± 0.02 RJup) for the companion that is consistent with that predicted (0.914 RJup) by a model of a 1 Gyr old, non-irradiated BD with a dusty atmosphere. The location of WASP-30b in the minimum of the mass–radius relation is consistent with the quantitative prediction of Chabrier & Baraffe, thus confirming the theory.
1 3 F eb 2 01 6 Energy-Aware Cooperative Computation in Mobile Devices

Abstract—New data intensive applications, which are continuously emerging in daily routines of mobile devices, signific antly increase the demand for data, and pose a challenge for curren t wireless networks due to scarce resources. Although bandwi dth is traditionally considered as the primary scarce resourcein wireless networks, the developments in communication theo ry shifts the focus from bandwidth to other scarce resources in cluding processing power and energy. Especially, in device-todevice networks, where data rates are increasing rapidly, process ing power and energy are becoming the primary bottlenecks of the network. Thus, it is crucial to develop new networking mechanisms by taking into account the processing power and energy as bottlenecks. In this paper, we develop an energy-a w re cooperative computation framework for mobile devices. In t his setup, a group of cooperative mobile devices, within proximity of each other, (i) use their cellular or Wi-Fi (802.11) links astheir primary networking interfaces, and (ii) exploit their devi ce-todevice connections ( e.g., Wi-Fi Direct) to overcome processing power and energy bottlenecks. We evaluate our energy-aware cooperative computation framework on a testbed consistingof smartphones and tablets, and we show that it brings significa nt performance benefits.
A FPGA based TDEMI measurement system for quasi-peak detection and disturbance analysis

Measurement equipment that allows to measure electromagnetic interference (EMI) in short measurement time allows to reduce the costs and the time-to-market. By a timedomain EMI (TDEMI) measurement the measurement time can be reduced by several orders of magnitude. In the following a novel trigger unit and recording algorithm for a TDEMI measurement system is described. The trigger unit triggers on signal parts that have a maximum amplitude between a certain preselected interval. The maximum detector unit allows to measure and store the maximum amplitude of the signal within a preselected time interval. The maxima of consecutive time intervals are stored. The signal is processed in real-time for several seconds. Recorded signal parts are assigned according to their maximum amplitude to a certain point in time. The original signal is reconstructed. A time-dependent spectrum for several seconds is calculated. The signal corresponding to the envelope of the IF-signal of an EMI Receiver is extracted at each discrete spectral value. An analysis with the Quasi-Peak-Detector as well as the Disturbance Analyzer is now possible. Measurements were performed in the Quasi-Peak-Detector mode and compared with results obtained by an EMI-Receiver. Also the demodulated IFSignal of the EMI-Receiver is compared with the reconstructed IF-Signal of the TDEMI measurement system.
RF BiST to detect IQ Imbalances considering Crosstalk Effects for Advanced Antenna System

Internet of Things (IoT) is a network constituted by uniquely identi able commodity objects or devices equipped with some sensing system. To achieve high data rates required for IoT devices, a modern wireless communication system, known as Advanced Antenna Systems(AAS), leads to signi cant improvement, by increasing not only the data rate but also spectrum e ciency and the channel capacity. A certain type of analog signal distortions, i.e. the in-phase (I) and quadrature-phase(Q) imbalances can severely damage the performance of the Radio Frequency (RF) transceivers. The IQ imbalances refer to gain mismatch, phase mismatch, DC o set in the I and Q branches. IQ Imbalances a ect the e ciency and performance of the RF Transceivers, present in the AAS. Hence a need for test architectures arises. In this thesis an existing BiST circuitry for one transmission chain is implemented and further extended to multiple transmission chains(up to 4). As the number of transmission chains increase, mutual coupling phenomenon leading to crosstalk e ects are to be considered. Along with the designed test signal, the BiST circuitry enables us to detect the transmitter impairment with low computational complexity. We have considered 3 di erent cases, that have been implemented. They are one antenna setup, 2x1 antenna array setup and 4x1 antenna array setup. The rst case has one transmission chain. We have utilized the Generalized Memory Polynomial(GMP) power ampli er model, and the existing BiST technique, called as Self-Mixing Envelope Detector method is implemented in Matlab. In the case of 2x1 Antenna array, implementation is partly carried out using the RF equipment for the signal generation stage, power ampli er stage, crosstalk e ects( due to mutual coupling), and BiST circuitry stage, was previously implemented in Matlab is utilized. The 4x1 antenna array setup, just like the one antenna setup, is entirely implemented in Matlab. Additionally, the coupling coe cients are extracted to model the mutual coupling phenomenon. In the one antenna setup & the 4x1 antenna array setup, the RMSE% of IQ imbalances are calculated. In the 2x1 antenna array setup, gain mismatch, phase mismatch, both the DC o sets are measured up to a certain accuracy. The results obtained in all the three cases advocate that this method, successfully detect the IQ impairments present in the advanced antenna system, even when the crosstalk e ects are considered.
Load Weight Based Energy Distribution and Dynamic Pricing using Fuzzy Logic in a Smart Grid Tree Network

— The smart energy distribution according to the growing demand during peak hours at smart grid is very essential. The dynamic pricing per unit energy should depend on the demand and peak load hours of a day. In this paper, a new quantitative technique using Load Weight Based Energy Distribution (LWBED) is proposing to balance the dynamically varying loads at various levels and optimize the power distribution efficiency of the grid. Initially, LWBED Algorithm is applied at various junctions and the power is dynamically distributed to the sub junctions, connected to each junction which optimizes the power distribution efficiency of the grid. Later, a Dynamic Price per unit energy is charged based on the demand and time using Fuzzy logic. Incentives are provided during off peak hours to improve the user demand during non peak hours. The proposed algorithms handle the demand on the grid very smoothly during peak and non peak hours and the users are charged based on their energy utilization in a day. From the simulation results it is clearly observed that the Fuzzy based pricing algorithm considers load variations, peak traffic hours, and leads to effective pricing charges.
Supplementary Material Cycle-Consistency for Robust Visual Question Answering

The supplementary material is organized as follows: • Section 1 covers information about the dataset collection pipeline, user interface and provides some dataset statistics. • Section 2 shows qualitative examples of how attention over image regions varies for VQA models when different rephrasings of the same question are used as input. • Section 3 describes an attention based consistency strategy that we experimented with, but did not improve performance (and so was not a part of our final model presented in the paper). • Section 4 shows qualitative examples of answer conditioned questions generated by our VQG module. • Section 5 lists the hyperparameters used for each base VQA model. • Section 6 presents extended analysis of our failure prediction module.
Utility of Pre-Schools Learning Environment in Kenya : It ’ s Role in Enhancing Pupil Enrolment

DOI: 10.21276/sjhss.2018. 3.1. 18 Abstract: The overall goal of the Pre-school (Early Childhood Education Development) investment programme is to enhance access, equity and quality of education for all Kenyan children aged 3-5 years, especially the most vulnerable living in ASALs, urban slums and pockets of poverty. The importance of Preschool Education includes development of mental functions of children in areas such as language, motor skills, psychosocial, cognitive and learning. This study was designed to evaluate the effect of learning resources in public preschools pupil enrolment in Kenya. The study target population was 45 Head teachers, 91 Teachers and one DICECE Programme officer. Simple random sampling, stratified and purposive sampling technique were used in choosing the sample size in Eldoret East Sub-County, Kenya. The data of the study was collected using questionnaires and checklist. The obtained data was analyzed using thematic approach, Pearson’s Product Moment Correlation and Simple linear regression. The findings indicate that there is a positive and significant relationship between learning resources and pupil enrolment level this implies that with every unit increase in physical infrastructure there is increase in the enrolment level. It is recommends that the ministry of education should establish community related factors and psychosocial factors across the pre-school education sector. It is hoped that the findings of this study will be of great importance to policy makers on understanding the best strategies that will promote efficacies in the administration of Pre-School Education.
Visual Imitation Learning for Robot Manipulation

Imitation learning has been successfully applied to solve a variety of tasks in complex domains where an explicit reward function is not available. However, most imitation learning methods require access to the robot’s actions during demonstration. This stands in a stark contrast to how we humans imitate: we acquire new skills by simply observing other humans perform a task, mostly relying on the visual information of the scene. In this thesis, we examine how we can endow a robotic agent with this capability, i.e., how to acquire a new skill via visual imitation learning. A key challenge in learning from raw visual inputs is to extract meaningful information from the input scene, and enabling the robotic agent to learn the demonstrated skill based on the accessible input data. We present a framework that encodes the visual input of a scene into a factorized graph representation, casting one-shot visual imitation of manipulation skills as a visual correspondence learning problem. We show how we detect corresponding visual entities of various granularities in both the demonstration video and the visual input of the learner during the imitation process. We build upon multi-view self-supervised visual feature learning, data augmentation by synthesis, and category-agnostic object detection to learn scene-specific visual entity detectors. We then measure perceptual similarity between demonstration and imitation based on matching of the spatial arrangements of corresponding detected visual entities, encoded as a dynamic graph during demonstration and imitation. Using different visual entities such as human keypoints and object-centric pixel features, we show how these relational inductive biases regarding object fixations and arrangements can provide accurate perceptual rewards for visual imitation. We show how the proposed image graph encoding drives successful imitation of a variety of manipulation skills within minutes, using a single demonstration and without any environment instrumentation.
Providing Privateness via Transaction Splitting Using PFP Growth Algorithm

Frequent itemset mining is one of the most necessary problems in data extraction. The chance of devious a discrepancy private frequent itemset mining algorithm which can not only accomplish high data usefulness and a high level of secrecy, but also offer high time effectiveness. To this end, offer a discrepancy frequent itemset mining algorithm based on the Frequent Pattern-growth algorithm, which is referred to as Private Frequent Pattern-growth. The Private frequent Pattern -growth algorithm consists of a preprocessing phase and a mining phase. To improve the utility and secrecy tradeoff, a innovative smart splitting method is proposed to change the database in preprocessing phase. It needs to be performed only once for a given database. To compensate the information loss caused by transaction splitting, To estimate the definite support of itemsets in the original database in mining segment utilize run time estimation method. In accumulation, develop a dynamic reduction method to dynamically reduce the amount of noise added to guarantee privacy during the extracting process by leveraging the downward closure property. Private common pattern-growth algorithm is shown it is e-differentially private through formal privacy analysis; explain that PFP-growth algorithm is e- discrepancy secrecy. Extensive experiments on real datasets exemplify that our PFP-growth algorithm considerably outperforms the state-of-the-art techniques.
CONFIGURATION OF SELF-ORGANIZING INFORMALITY: Socio-spatial dynamic in favelas

This paper aims to discuss on spatial patterns found in favelas throughout different cities worldwide, as they seem to reproduce similar configurations and urban dynamics despite its diverse local contexts. The purpose is to explore these patterns in order to understand its social behaviour and address urban informality issues through it. To see how resilience seems to be inherent to such spaces, that grow vibrant, complex and dynamic global structures emerging and self-organizing from segregation in city space. It pursues the acknowledgement of structural morphological patterns of informality production, those genotypic characteristics that seem to be independent on culture, and might be representative of these social patterns commonly observed worldwide. Favela is observed in this study as a complex, self-organized entity, whose contrast to official city relies on its “bottom up” structure. It tends to follow natural rules of organization instead of formal urban strategies. Space Syntax (Hillier and Hanson, 1984) is the theoretical and methodological approach applied, through which it is possible to investigate these peculiar spatial patterns in favela, comparing several cases in Latin America, Africa and Asia. The analyses are based on axial and segment maps. Investigated variables are connectivity, local and global integration, mean depth, synergy, intelligibility, angular Choice, number and length of axes, and number and length of segments, compactness, normalized angular integration and choice for segment comparison. Findings show favela as an entity that maximises use and space into strong fragmented spatial structures, which provides the labyrinthic perception of users, but also accentuates spatial hierarchy. Topography is critical to the understanding of favela’s performance. The more accentuated, the more fragmented, labyrinthic and endogenous. Nevertheless, most analysed favelas locate in flat areas and, therefore, present a tendency to better articulate with the surroundings, resulting in a softer inand-out transition. Such topological performance seems better than Brazilian cities (Medeiros, 2013), which points out favela’s organizing structure as a possible model that could be adopted to refine the configurational performance of cities.