Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.
A High-Resolution Supply Voltage Sensor Design and Its Performance Evaluation on an FPGA

Nowadays, integrated circuits (ICs) have higher integration densities. The simultaneous switching activities that are synchronized with a single global clock is one of the main causes that produce voltage drops across the power distribution network (PDN) of the ICs. Since the drop increase the delay of circuits, such drops may cause the timing failures that lead to the malfunctions of an IC. Therefore, a tool that supports for debugging the failures is essential. This paper presents a high-resolution supply voltage sensor that can be applicable for debugging timing failures at real-time, or determining the timing margins for the timing critical designs. Our supply voltage sensor is made up by using standard Look-up Table (LUT) cells, and dedicated carry cells with high-speed propagation delays. The experiments show that the voltage resolution of our supply voltage sensor is about 3.3mV on a Xilinx FPGA Spartan-6 (XC6SLX9).
Implementation of an Autonomous Small-scale Car with Indoor Positioning using UWB and IMU

Robotics have had a major impact in the current generation as they have a wide range of uses in manufacturing and automation; therefore, researching new technology related to robotics is currently at a high demand. Indoor robotics, such as automatic guided vehicles or humanoids, is a section of robotics that are mobile and need accurate positioning in order to navigate properly. Thus, research into indoor positioning systems (IPS) has become an interesting research topic to be able to provide a standard in indoor positioning. This thesis tests an ultrawideband (UWB) based IPS and fuses the data from an inertial measurement unit (IMU) using an extended Kalman filter (EKF). The testing platform was implemented using Robot Operating System (ROS) and a Beaglebone Black as the microcontroller for the sensors. However, the main processing was done on a separate laptop. As a result, a proposed smoothing technique was able to provide consistent velocity commands to the vehicle platform without affecting the data output rate of the UWB based IPS. In line-of-sight (LOS) conditions and a travel length of about 13 m, the best results produced an error of only 0.111 m at the final point, and an error of up to 0.603 m during travel.
Design and experimental operation of a control strategy for the buck-boost DC-AC inverter

The buck–boost DC–AC inverter generates an alternating output voltage as the differential voltage of two DC–DC individual buck–boost converters that are driven with two 1801 phase-shifted DC-biased sinusoidal references. The peak value of the inverter alternating output voltage does not depend on the direct input voltage. In addition, an advantage over the Boost DC– AC inverter is that the output voltages of both Buck–Boost converters are also independent of the direct input voltage. The difficulty of the topology lies in the control of both buck–boost DC–DC converters as they are required to work with variable operating-points. A double-loop control strategy is proposed for the buck–boost DC–DC converter with a new inner control loop for the inductor current and also a new outer control loop for the output voltage. These control schemes include different compensations that make possible a fast and accurate control of both converters with variable operating points. With these compensations, the controllers are easy to design, implement and develop. Furthermore, feedforward loops are included to increase the robustness of the inverter to external disturbances in both the input voltage and output load. The proposed control strategy is designed and implemented on a prototype 1.5kW buck–boost DC–AC inverter. The control strategy is validated by means of both simulation and experimental tests. The results show that the proposed strategy achieves a robust, reliable and accurate control of the inverter even in hard situations such as nonlinear loads, sudden load changes and transient short circuits.
Non-self-adjoint model of a two-dimensional noncommutative space with an unbound metric

We demonstrate that a non-self-adjoint Hamiltonian of harmonic-oscillator type defined on a two-dimensional noncommutative space can be diagonalized exactly by making use of pseudobosonic operators. The model admits an antilinear symmetry and is of the type studied in the context of PT-symmetric quantum mechanics. Its eigenvalues are computed to be real for the entire range of the coupling constants and the biorthogonal sets of eigenstates for the Hamiltonian and its adjoint are explicitly constructed. We show that despite the fact that these sets are complete and biorthogonal, they involve an unbounded metric operator and therefore do not constitute (Riesz) bases for the Hilbert space L2(R2), but instead only D quasibases. As recently proved by one of us, this is sufficient to deduce several interesting consequences.
A Histological and Histochemical Investigation on Oocyte Development in Gryllus bimaculatus (Orthoptera: Gryllidae)

This study investigated the oocyte development in the ovary of two spotted black cricket, Gryllus bimaculatus. Oocytes mature through three developmental stages. These stages include (i) previtellogenic, (ii) vitellogenic and (iii) maturation. In previtellogenic stage, oocytes are encircled by squamous follicle cells. In vitellogenic stage, yolk granules within the oocyte were firstly observed just beneath the oolemma. As vitellogenesis proceeds, yolk granules cover the whole oocyte. In addition, small sized lipid droplets were firstly seen under the yolk granules, then spreads to oocyte. This time, cubic and binucleated follicle epithelial cells encircled the oocyte. In maturation stage, oocytes are full of large yolk granules. Apart from Hematoxylin& Eosin, Periodic Acid Schiff, Bromophenol Blue and Mallory Trichrome reactions were also demonstrated. According to these three histochemical methods, yolk granules showed positive reactivity. Due to the oosorption, the resorption of oocytes, some atretic oocytes were observed. Apoptosis was also observed in atretic follicles. This study indicated that ovary type of Gryllus bimaculatus is panoistic as reported in other Gryllid species. Key Words: Cricket, ovary, oocyte, oosorption, reproduction, vitellogenesis Gryllus bimaculatus ’ta (Orthoptera: Gryllidae) Oosit Gelisimi Uzerine Histolojik ve Histokimyasal Arastirma OZET: Bu calismada iki noktali kara cekirge, Gryllus bimaculatus turune ait bireylerin ovaryumunda histolojik ve histokimyasal incelemeler yapilmistir. Oositler uc asamada olgunlasir, bunlar: (i) previtellojenik, (ii) vitellojenik ve (iii) olgunluk asamalaridir. Previtellojenik evrede oositler yassi folikul epitel hucreleri tarafindan kusatilmaktadir. Vitellojenik evrede yolk granulleri ilk olarak oolemmanin hemen altinda gozlenir. Vitellogenez ilerledikce oositin icini doldururlar. Ayrica kucuk boyutlu lipid damlalari ilkin bu yolk granullerinin altinda bulunur ve daha sonra oositi kaplar. Bu sefer kubik ve cift nukleuslu folikul epitel hucreleri oositi kusatir. Olgunluk evresinde oositler iri yolk granulleriyle kaplidir. Histolojik preparatlar Hematoksilen Eosin boyamasi disinda Periodic Acid Schiff, Bromophenol Blue ve Mallory Trichrome ile boyanarak incelenmistir. Bu uc histokimyasal yontemle yolk granulleri pozitif reaksiyon gostermistir. Oositlerin resorpsiyonu anlamindaki oosit oosorpsiyonu sebebiyle bazi atretik oositler gozlenmistir. Ayrica, apoptozis bu folikullerde belirlenmistir. Bu calisma Gryllus bimaculatus ovaryum tipinin diger Gryllid turlerinde bildirildigi gibi panoistik tipte oldugunu gostermistir. Anahtar Kelimeler: Cekirge, ovaryum, oosit, ureme, vitellogenez
CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.
Research Through the App Store: Understanding Participant Behavior on a Mobile English Learning App

In this study, we analyzed usages behaviors of 53,825 active users from 12 countries in a mobile app called “English Practice. ” The data indicated that the app was used most often in the evening from 8 p.m. to 9 p.m. , and especially midweek, with less use at weekends and on Mondays. Learners used the app for about 5 minutes each session and averaged 10 uses before uninstalling it. The data also provided evidence that there were differences in each country. From the findings, it is suggested that (a) learning tasks be simplified and content reduced for each session, (b) announcements not be made in the early morning or on the weekend as these are easily overlooked, and (c) as user behavior in each country is different, each group be better understood when delivering learning content and designing the app interface. Finally, we concluded that publishing apps in the app store can provide valuable data regarding app users’ behaviors.
The relations of children's dispositional empathy-related responding to their emotionality, regulation, and social functioning.

The relations of kindergartners' to 2nd graders' dispositional sympathy to individual differences in emotionality, regulation, and social functioning were examined. Sympathy was assessed with teacher- and self-reports ; contemporaneously and 2 years earlier, parents and teachers reported on children's emotionality, regulation, and social functioning. Social functioning also was assessed with peer evaluations and children's enacted puppet behavior, and negative arousability-personal distress was assessed with physiological responses. In general, sympathy was associated with relatively high levels of regulation, teacher-reported positive emotionality and general emotional intensity, and especially for boys, high social functioning and low levels of negative emotionality, including physiological reactivity to a distress stimulus. Vagal tone was positively related to boys' self-reported sympathy, whereas the pattern was reversed for girls.
Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient

Deep neural networks provide state-of-the-art performance for many applications of interest. Unfortunately they are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs. Moreover, the perturbations can transfer across models: adversarial examples generated for a specific model will often mislead other unseen models. Consequently the adversary can leverage it to attack against the deployed black-box systems. In this work, we demonstrate that the adversarial perturbation can be decomposed into two components: model-specific and data-dependent one, and it is the latter that mainly contributes to the transferability. Motivated by this understanding, we propose to craft adversarial examples by utilizing the noise reduced gradient (NRG) which approximates the data-dependent component. Experiments on various classification models trained on ImageNet demonstrates that the new approach enhances the transferability dramatically. We also find that low-capacity models have more powerful attack capability than high-capacity counterparts, under the condition that they have comparable test performance. These insights give rise to a principled manner to construct adversarial examples with high success rates and could potentially provide us guidance for designing effective defense approaches against black-box attacks.
Creating A Basis For Three Dimensional Sketching

The design process usually starts with rough doodles and sketches to create a basic visual representation of the solution to a problem. The processes from this conceptual stage to more detailed specifications has often been segmented between incompatible physical and virtual representations. This work explores digital 3D sketching using large touch panel displays and active pen technology for the use in the early stages of the design process. We focus on rendering high quality 3D curves in a free-form sketching environment. There are two major challenges we provide approaches for; transforming our low resolution input data to a resolution independent format, and rendering dense curve representations at high quality in real time.
A Nonconvex Optimization Approach to IMRT Planning with Dose-Volume Constraints

Fluence map optimization for intensity-modulated radiation therapy planning can be formulated as a large-scale inverse problem with competing objectives and constraints associated with the tumors and organs at risk. Unfortunately, clinically relevant dose–volume constraints are nonconvex, so standard algorithms for convex problems cannot be directly applied. Although prior work focuses on convex approximations for these constraints, we propose a novel relaxation approach to handle nonconvex dose–volume constraints. We develop efficient, provably convergent algorithms based on partial minimization, and show how to adapt them to handle maximum-dose constraints and infeasible problems. We demonstrate our approach using the CORT data set and show that it is easily adaptable to radiation treatment planning with dose–volume constraints for multiple tumors and organs at risk. Summary of Contribution: This paper proposes a novel approach to deal with dose–volume constraints in radiation treatment planning optimization, which is inherently nonconvex, mixed-integer programming. The authors tackle this NP-hard problem using auxiliary variables and continuous optimization while preserving the problem’s nonconvexity. Algorithms to efficiently solve the nonconvex optimization problem presented in this paper yield computation speeds suitable for a busy clinical setting.
Bayesian Analysis of Spatial Generalized Linear Mixed Models with Laplace Random Fields

Gaussian random field (GRF) models are widely used in spatial statistics to capture spatially correlated error. We investigate the results of replacing Gaussian processes with Laplace moving averages (LMAs) in spatial generalized linear mixed models (SGLMMs). We demonstrate that LMAs offer improved predictive power when the data exhibits localized spikes in the response. SGLMMs with LMAs are shown to maintain analogous parameter inference and similar computing to Gaussian SGLMMs. We propose a novel discrete space LMA model for irregular lattices and construct conjugate samplers for LMAs with georeferenced and areal support. We provide a Bayesian analysis of SGLMMs with LMAs and GRFs over multiple data support and response types.
Sparse label propagation: a robust domain adaptation learning method

Sparse representation has received an increasing amount of interest in pattern classification due to its robustness. In this paper, a domain adaptation learning (DAL) approach is explored based on a sparsity preserving model, which assumes that each data point can be sparsely reconstructed. The proposed robust DAL algorithm, called sparse label propagation domain adaptation learning (SLPDAL), propagates the labels from labeled points in the source domain to the unlabeled dataset in the target domain using those sparsely reconstructed objects with sufficient smoothness. SLPDAL consists of three steps. First, it finds an optimal kernel space in which all samples from both source and target domains can be embedded by minimizing the mean discrepancy between these two domains. Then, it ∗ 基金项目: 教育部人文社会科学研究规划基金(13YJAZH084); 浙江省自然科学基金(LY14F020009); 宁波市自然科学基金 (2013A610065, 2013A610072); 香港理工大学基金(G-UA68) 收稿时间: 2013-01-21; 修改时间: 2013-09-04; 定稿时间: 2014-01-10; jos 在线出版时间: 2014-08-19 CNKI 网络优先出版: 2014-08-19 14:39, http://www.cnki. net/kcms/doi/10.13328/j.cnki. jos.004575. html 978 Journal of Software 软件学报 Vol.26, No.5, May 2015 computes the best kernel sparse reconstructed coefficients for each data point in the kernel space by using l1-norm minimization. Finally, it propagates the labels of source domain to the target domain by preserving the kernel sparse reconstructed coefficients. The paper also derives an easy way to extend SLPDAL to out-of-sample data and multiple kernel learning respectively. Promising experimental results have been obtained for several DAL problems such as face recognition, visual video detection and text classification tasks.
IMPLEMENTASI ALGORITMA HIDDEN MARKOV MODEL SEBAGAI PENGENALAN PERINTAH SUARA PADA APLIKASI WINAMP

Salah satu teknologi yang sedang berkembang saat ini adalah speech recognition yang mengolah suara menjadi suatu perintah yang dapat dikenal oleh suatu mesin sehingga memungkinkan seseorang melakukan remote pada aplikasi yang ada pada komputer dengan menggunakan suara yang masuk melalui microphones. Tujuan dari penelitian ini yaitu membuat Media Player Winamp dapat dioperasikan menggunakan suara dengan Algoritma Hidden Markov Model. Sinyal suara dicuplik sehingga menjadi sinyal digital kemudian dinormalisasi dan diproses dengan preprocessing signal menggunakan metode LPC (Linear Predective Coding) selanjutnya dianalisis dan dicari nilai probabilitas yang maksimum sehingga bisa dikenali menggunakan Hidden Markov Model (HMM). Hasil penelitian menunjukan algoritma Hidden Markov Model dapat digunakan untuk mengeksekusi fungsi aplikasi winamp melalui perintah suara. Kata kunci: Speech Recognition, Sinyal Suara, Linear Predective Coding, Hidden Markov Model.
Distributed Consensus for Multiple Lagrangian Systems With Parametric Uncertainties and External Disturbances Under Directed Graphs

In this paper, we study the leaderless consensus problem for multiple Lagrangian systems in the presence of parametric uncertainties and external disturbances under directed graphs. To achieve asymptotic behavior, a robust continuous term with adaptive varying gains is added to alleviate the effects of the external disturbances with unknown bounds. In the case of a fixed directed graph, by introducing an integrate term in the auxiliary variable design, the final consensus equilibrium can be explicitly derived. We show that the agents achieve weighted average consensus, where the final equilibrium is dependent on three factors, namely, the interactive topology, the initial positions of the agents, and the control gains of the proposed control algorithm. In the case of switching directed graphs, a model-reference-adaptive-consensus-based algorithm is proposed such that the agents achieve leaderless consensus if the infinite sequence of switching graphs is uniformly jointly connected. Motivated by the fact that the relative velocity information is difficult to obtain accurately, we further propose a leaderless consensus algorithm with gain adaptation for multiple Lagrangian systems without using neighbors’ velocity information. We also propose a model-reference-adaptive-consensus-based algorithm without using neighbors’ velocity information for switching directed graphs. The proposed algorithms are distributed in the sense of using local information from its neighbors and using no common control gains. Numerical simulations are performed to show the effectiveness of the proposed algorithms.
Sculpting Eccentric Debris Disks with Eccentric Gas Rings

Many debris disks seen in scattered light have shapes that imply their dust grains trace highly eccentric, apsidally aligned orbits. Apsidal alignment is surprising, especially for dust. Even when born from an apse-aligned ring of parent bodies, dust grains have their periastra dispersed in all directions by stellar radiation pressure. The periastra cannot be reoriented by planets within the short dust lifetimes at the bottom of the collisional cascade. We propose that what realigns dust orbits is drag exerted by second-generation gas. Gas is largely immune to radiation pressure, and when released by photodesorption or collisions within an eccentric ring of parent bodies should occupy a similarly eccentric, apse-aligned ring. Dust grains launched onto misaligned orbits cross the eccentric gas ring supersonically and can become dragged into alignment within collisional lifetimes. The resultant dust configurations, viewed nearly but not exactly edge-on, with periastra pointing away from the observer, appear moth-like, with kinked wings and even doubled pairs of wings, explaining otherwise mysterious features in HD 61005 (“The Moth”) and HD 32297, including their central bulbs when we account for strong forward scattering from irregularly shaped particles. Around these systems we predict gas at Kuiper-belt-like distances to move on highly elliptical streamlines that owe their elongation, ultimately, to highly eccentric planets. Unresolved issues and an alternative explanation for apsidal alignment are outlined.
q-deformed 3D loop gravity on the torus

The q-deformed loop gravity framework was introduced as a canonical formalism for the Turaev–Viro model (with ), allowing to quantize 3D Euclidean gravity with a (negative) cosmological constant using a quantum deformation of the gauge group. We describe its application to the 2-torus, explicitly writing the q-deformed gauge symmetries and deriving the reduced physical phase space of Dirac observables, which leads back to the Goldman brackets for the moduli space of flat connections. Furthermore it turns out that the q-deformed loop gravity can be derived through a gauge fixing from the Fock–Rosly bracket, which provides an explicit link between loop quantum gravity (for q real) and the combinatorial quantization of 3D gravity as a Chern–Simons theory with non-vanishing cosmological constant . A side-product is the reformulation of the loop quantum gravity phase space for vanishing cosmological constant , based on holonomies and fluxes, in terms of Poincaré holonomies. Although we focus on the case of the torus as an example, our results outline the general equivalence between 3D q-deformed loop quantum gravity and the combinatorial quantization of Chern–Simons theory for arbitrary graph and topology.
The Mathematical Foundations of Mixing: The Linked Twist Map as a Paradigm in Applications: Micro to Macro, Fluids to Solids (Cambridge Monographs on Applied and Computational Mathematics)

Preface 1. Mixing: physical issues 2. Linked twist maps 3. The ergodic hierarchy 4. Existence of a horseshoe 5. Hyperbolicity 6. The ergodic partition for toral LTMs 7. Ergodicity and Bernoulli for TLTMs 8. Linked twist maps on the plane 9. Further directions and open problems Bibliography Index.