Miracle-dz/UniFAR-FTUs huggingface dataset:
The Hα Luminosity Function of the Galaxy Cluster A521 at z = 0.25

We present an optical multicolor-imaging study of the galaxy cluster A521 at z = 0.25, using Suprime-Cam on the Subaru Telescope, covering an area of 32′ × 20′ (9.4 × 5.8 h Mpc2 at z = 0.25). Our imaging data taken with both a narrowband filter, NB816 (λ0 = 8150 Å and Δλ = 120 Å), and broadband filters, B,V,RC,i′, and z′, allow us to find 165 Hα emitters. We obtain the Hα luminosity function (LF) for the cluster galaxies within 2 Mpc; the Schechter parameters are α = -0.75 ± 0.23, ϕ* = 10-0.45 to 10-0.05 Mpc-3, and L* = 1041.86 to 1042.2 ergs s-1. Although the faint-end slope α is consistent with that of the local cluster Hα LFs, the characteristic luminosity L* is about 6 times (or ≈2 mag) brighter. This strong evolution implies that A521 contains more active star-forming galaxies than the local clusters, being consistent with the observed Butcher-Oemler effect. However, the bright L* of A521 may be, at least in part, due to the dynamical condition of this cluster.
Demonstrating adequate safety for a concrete column exposed to fire, using probabilistic methods

Demonstrating adequate safety for exceptional designs and new design applications requires an explicit evaluation of the safety level, considering the uncertainties associated with the design parameters. The recently published PD 7974‐7:2019 provides five routes to demonstrate adequate safety through probabilistic methods but does not include worked examples. The case study in this paper presents three state‐of‐the‐art approaches for demonstrating achievement of an absolute safety target (acceptance concept “AC3” in PD 7974‐7:2019) for a concrete column in an office building with stringent reliability requirements, specified on the level of individual members and with respect to a standardized heating regime. The case study shows how fragility curves listed by, for example, industry organizations can support probabilistic approaches and a more comprehensive understanding of design performance.
Demodulation of Frequency-Shifting Signals of Automatic Block System with PLL

Signal demodulation of automatic block with audio frequency shift modulated track circuits was implemented with phase-locked loop (PLL) technique and a singe chip microcomputer. Experiment showed that the demodulation system can not only reduce the low-frequency pulses from the received signals, but also effectively depress the noises induced in the track circuits and achieve high signal-noise ratio.
An energy efficient noise-shaping SAR ADC in 28 nm FDSOI

In a noise-shaping sar adc, oversampling and noise shaping are used to increase the conversion accuracy beyond that the sar exhibits alone. To implement the noise shaping, the residue voltage present at the sar dac plates after each conversion is exploited, and fed into a loop filter connected to an extra input of the sar comparator. In this thesis, an energy efficient noise-shaping sar adc for medical ultrasound applications is designed in 28 nm fdsoi. The design specification is minimum 11.0 bit enob of accuracy, signal bandwidth of minimum 2 MHz, and sample rate of minimum 32MHz. According to post-layout Monte Carlo simulations, the designed adc has an accuracy of 11.1 bit enob, and thus satisfies the accuracy requirement. The signal bandwidth and sample rate are the same as in the design specification. Specifically, the topics of this thesis are the design of the loop filter and its interfacing towards the sar, as well as the overall high level design. The 9-bit sar used in the system is an already existing implementation. A cascaded fir-iir filter topology is used for the loop filter. In this work, the circuit implementation of this topology is improved, most importantly through the introduction of chopped buffers at the filter input. This eliminates signal attenuation due to charge sharing, and a dac capacitance that is smaller than the sampling capacitance in the loop filter can therefore be used. Also, auto-zeroed, cascoded inverters rather than a standard ota are used as gain elements in the switched-capacitor filter structure, and this leads to better energy efficiency. The designed adc achieves a figure of merit (fom) of 7.5fJ/conv-step in post-layout Monte Carlo simulations, and to the best of the author’s knowledge, this is better than the current state-of-the-art of noise-shaping adcs. When all kinds of adcs are taken into consideration, the achieved fom seems to be similar to the current state-of-the-art in the same specification range.
Low-Complexity Power Allocation in NOMA Systems With Imperfect SIC for Maximizing Weighted Sum-Rate

In this paper, we investigate the power allocation for maximizing weighted sum rate (WSR) in downlink multiple carriers non-orthogonal multiple access (MC-NOMA) systems with imperfect successive interference cancellation (SIC). We formulate the power allocation problem as a non-convex optimization problem with the total power constraint of all sub-channels while considering often-neglected issues of SIC error and power order constraints at users. First, we discuss that the optimization problem assuming receivers can perform perfect SIC, and we provide a concavity condition of the WSR maximization problem for the MC-NOMA system. When the concavity condition is not satisfied, a fractional quadratic transformation is used to overcome the difficulty of problem non-convexity. Based on the transformation, we propose an iterative power allocation algorithm. Then, we consider the SIC error and the power order constraints in the optimization problem and present a power allocation method with imperfect SIC. Moreover, for both the perfect and imperfect SIC, we derive some propositions of the optimal power allocation solution to the WSR maximization problem and propose a low-complexity power allocation algorithm based on these propositions. Finally, we provide a joint user scheduling and power allocation algorithm for maximizing the WSR. The simulation results illustrate that the proposed resource allocation methods have a better performance than the existing schemes.
Bayesian Benefits for Binomial Applications in Practice

Introduction: When it comes to the practice, and teaching, of statistics, the world has primarily focused on what is known as classical or frequentist methods, rather than Bayesian methods. Scope of the Study: This paper demonstrates some beneficial properties of Bayesian methods within the commonly practiced domain of inference by utilizing consultancy case studies, one concerning an unusual sample size question and one on the detection of mail items with high biosecurity risk material. Methods: We introduce through practical applications two more aspects of the Bayesian approach which we believe are invaluable to practitioners and instructors. Having in mind readers who may be less familiar with statistical software, we have added Excel instructions which are easy to translate for those who are familiar with any such software. Findings: These cases reflect two valuable aspects for both practitioners and instructors which are unique to the Bayesian paradigm. They are: 1. including prior information to improve inference and how to apply sensitivity analysis to this inclusion and 2. the effortless inference for functions of parameters, compared with frequentist approaches. These examples involving the binomial parameter have not been considered from this perspective before, are of significant practical value and thus benefit students and instructors of courses teaching Bayesian techniques and endeavoring to include authentic learning experiences.
Cloud broker proposal based on multicriteria decision‐making and virtual infrastructure migration

The adoption of infrastructure as a Service (IaaS) is a reality for academic, industrial, and governmental institutions. Cloud tenants request dynamically provisioned virtual infrastructures (VIs) tailored to their application requirements, detailing not only the virtual compute/storage resources but also the network components, topology, and services. The creation of a large number of cloud providers came along with the widespread use of VIs. The selection of an appropriate provider is a challenging task due to the diversity of the IaaS market and formally is a multicriteria analysis (NP‐hard). Notwithstanding the provider selection complexity, the mobility of VI‐hosted applications is limited due to the optimization anchors introduced by providers. Although the existing IaaS cloud brokers can indicate a hosting provider, they lack on conceptual and technical skills to migrate a VI and all its internal components between providers. This work enhances the state‐of‐the‐art on IaaS cloud brokerage by proposing virtual infrastructure multicriteria allocation and migration–based broker (VIMAM), which performs a multicriteria analysis of providers and VI migration. VIMAM is driven by an analytic hierarchy process (AHP) to select an IaaS provider, offering a set of predefined weighting schemas to represent distinct tenant perspectives. Moreover, to migrate a VI, VIMAM takes into account the virtual machines, containers, switches, and other topology elements. In addition to discussing the AHP ranking weights and frequency of providers selection, the experimental analysis details the implementation of an OpenStack and Docker–based prototype for VI migration.
The price of connectedness in graph partitioning problems

In this work, we present some results coming from the comparison of two classes of problems of optimal graph partitioning. In the first class of problem, called further constrained problem, ones impose the nodes belonging to a same cluster to be connected. In the second class of problem, no constraint of connectedness is imposed. Obviously, the optimal solution of an unconstrained problem is always better than or equal to the solution of the corresponding constrained problem. It is thus interesting to estimate how much the connectedness constraint degrades the optimal solution of the partitioning problem. This degradation is what we call here the price of connectedness. Motivated by a numerical example, we propose to estimate the price of connectedness by comparing the cardinalities of the feasible sets in the unconstrained and constrained optimisation problems. We present then a tight upper-bound on the ratio of these two cardinalities for a directed Erdős-Rényi graph. On our way to set this result we also derive an upper-bound on the probability that an Erdős-Rényi graph is connected and the exact probability that an Erdős-Rényi graph contains isolated nodes.
Towards energy-efficient convolutional neural network inference

Over the last few years, deep learning and particularly convolutional neural networks (CNNs) have become the method of choice for most computer vision tasks. The achieved leap in accuracy has dramatically increased the range of possibilities and meaningful application has created a demand for running these compute and memory intensive algorithms on embedded and mobile devices for reliable and costefficient deployment. In this thesis, we first evaluate the capabilities of off-the-shelf software-programmable hardware before diving into specialized hardware accelerators and exploring the potential of extremely quantized CNNs. We give special consideration to external memory bandwidth, which dominates the overall energy cost, in the form of a conceptually different hardware architecture and with a hardware-friendly de-/compression scheme. We establish that—including I/O—softwareprogrammable platforms can achieve 10–40GOp/s/W, our specialized accelerator for fixed-point CNNs achieves 630GOp/s/W, binary-weight CNNs can be implemented with up to 5.9TOp/s/W and very small binarized neural networks implementable with purely combinational logic for e.g. a first classification stage could be run directly on the sensor with 670TOp/s/W. For applications in video surveillance, we further explore the effect of fusing the RGB images with multispectral data to trade-off accuracy
Influence of samarium and iron substitution on structural and electrical properties of barium zirconate titanate solid solutions

ABSTRACT Solid solutions of (x)SmFeO3-(1-x)Ba(Zr0.05Ti0.95)O3: x = 0, 0.21, 0.22 and 0.23 were synthesized by the solid-state reaction route. Le-Bail analysis of the X-ray diffraction data and Raman spectra revealed the presence of the tetragonal phase (P4mm space group 99). The remnant polarization (Pr) was ~4.08 µC/cm2 and 1.04 µC/cm2 for x= 0 and x= 0.21, respectively, while lossy loops were observed for x≥ 0.22. Canting of the antiferromagnetic spins and ferromagnetic exchange mechanism was responsible for the ferromagnetic feature in the samples. An increase in remnant magnetization (Mr) was observed with increases in x. The Nyquist plots revealed the existence of grain and grain boundaries in the overall impedance. Separation of the normalized plots of impedance (Z˝) and modulus (M˝) spectra indicated localized motion of the charge carriers in the sample. The Maxwell–Wagner model fits very well in the low-frequency region (105 Hz). The ac conductivity obeyed the Universal Jonscher’s Power law. The variations in the exponent n suggest that the conduction mechanism conforms to the small polaron hopping mechanism.
A Novel Hybrid SVM-CNN Method for Extracting Characteristics and Classifying Cattle Branding

A tool that can perform the automatic identification of cattle brandings is essential for the government agencies responsible for the record, control and inspection of this activity. This article presents a novel hybrid method that uses Convolutional Neural Networks (CNN) to extract features from images and Support Vector Machines (SVM) to classify the brandings. The experiments were performed using a cattle branding image set provided by the City Hall of Bage, Brazil. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93.11% in the first experiment with 39 brandings and 1,950 sample images, and 95.34% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 31.661s and 41.749s, respectively.
A Study on Web Search based on Coordinate Relationships

Existing search engines always return content similar web pages or images in accordance with inputted queries. However, such similarity-based search does not always result in good results. This thesis aims at establishing methodologies to complement the deficiencies of similarity-based search by taking into account coordinate relationships. Coordinate relationships exist at different levels, such as term, sentence and document levels. However, many previous studies focus on coordinate relationship at the term level, especially between two terms. We extend to study on coordinate relationship between pairs of terms, moreover, coordinate relationships at the sentence and document levels and demonstrate the effectiveness for Web search. Besides, our concentrations are not limited to the text retrieval field, but also the image retrieval field. More specifically, we address the following three research topics in this thesis: 1. Paraphrasing Sentential Queries based on Coordinate Relationships The effectiveness of retrieval decreases with the increase in query length. We target at sentential queries, a type of long queries, and propose a method called sentential query paraphrasing for improving their retrieval performance, especially on recall. Briefly, given a sentential query, our method acquires paraphrases from the noisy Web and uses them to avoid returning no answers. We are motivated by the assumption that a relation can be represented either intensionally (referred to as paraphrase templates) or extensionally (referred to as coordinate tuples) and propose a mutual reinforcement algorithm based on it. Experimental results show that our method can acquire more paraphrases from the noisy Web. Besides, with the help of paraphrases, more Web pages can be retrieved, especially for those sentential queries that could not find any answers with its original expression. 2. Structuring Search Results based on Coordinate Relationships We propose a method to structure search results of a user-given query by distinguishing coordinate relationships from similarity relationships in the documents. We take into account documents that are mutually exclusive in semantics (called coordinate documents) and assume that such documents should not be grouped into the same cluster. Correspond-
AN EMPIRICAL ANALYSIS OF WEB OF THINGS (WOT)

The IoT has primarily focused on establishing connectivity in a diversify of constrained networking environments, and the next logical aim is to build on top of network connectivity by focusing on the application layer. In the Web of Things (WoT), we are thinking about smart things as first-class citizens of the Web. We position the Web of Things as a purification of the Internet of Things by integrating smart things not only into the Internet, for instance, the network, but into the Web (the application layer). The Web of Things is a computing concept that describes a future where day-to-day objects are fully integrated with the Web. The WoT is very homogeneous to the IoT in some ways and in others it is drastically different. The stipulation for WoT is for the "things" to have embedded computer systems that enable communication with the Web. This type of smart devices would then be able to communicate with each other using current Web standards. For instance, renowned Web languages PHP, HTML, Python, and JavaScript can be used to easily build applications involving smart things and users can leverage well-known Web mechanisms such as caching, browsing, searching, and bookmarking to communicate and share these devices. In this paper, aim to demonstrate a close-up view about Web of Things, including Web of things architecture, Open platform in Web of Thing, Web-enabling devices, Web of Thing security, use cases of Web of Things. The WoT concept, smart things, and their services are fully integrated into the Web by reusing and conforming technologies and patterns commonly used for conventional Web content.
ARCHITECTURE RECOGNITION BY MEANS OF CONVOLUTIONAL NEURAL NETWORKS

Abstract. The use of mobile computing technologies can change the experience of visiting cultural sites by making vast digital heritage collections accessible on site. The spread of machine learning technologies on mobile devices is encouraging the interaction of artificial intelligence with the shape of the built environment. However, while some research already applies deep learning image recognition in an urban context, the literature on how to develop effective neural networks to detect architectural features is still limited, as well as the availability of architecture-related datasets. This work presents the steps and results of the prototype development of a mobile app to perform monument recognition using convolutional neural networks. The tool allows users to interact with the physical space and access a digital archive of texts, models, images and other data.
A New Method of Block Allocation Used in Two-Stage Operating Rooms Scheduling

This paper studies a weekly operation scheduling problem of elective surgery according to the block scheduling policy, to balance overtime (time used beyond the block) and undertime (time unused within the block) of each surgeon’s blocks. The one week operation problem is divided into two stages: the first stage is to allocate patients to blocks and achieve the balance of the blocks to the same surgeon. Patients who can be scheduled on next week are determined according to the operating room (OR) capacity, surgeon’s available time and patient priority. In the second stage, the operation date, operating room, and operation sequence are determined. When sequencing operations in blocks, we focus on surgical type and priority the two indicators. We present two integer programming (IP) models to solve the two stage problems, with the objects of 1) minimize the sum of all blocks’ overtime and undertime penalty and 2) minimize the waiting cost for all patients. The two indicators are helpful to balance surgeons’ workload among blocks and can improve patient’s satisfaction. Directing at the first stage model, different from the previous studies, we will give a fast method to calculate the number of blocks for each surgeon, which can reduce the problem scale. The computational experiments are conducted illustrating its applicability to the problem in the operating theatre.
Automatic Detection of Version Differences Among Ancient Chinese Texts

An automatic approach to find the version differences among ancient Chinese text is proposed.First,we try to find sentence beads with the highest similarity by comparing the bigrams in the pair of sentences.Second,we iteratively remove the longest common substrings in the pair of different sentences and output differences remained.We take three versions of CHUNQIU as a running case.The results of the experiment indicate that our approach succeeds in finding all sentence beads and identifying all text differences definedas the version differences in this paper.
A Theoretical Study of Hardware Performance Counters-Based Malware Detection

Malware can range from simple adware to stealthy kernel control-flow modifying rootkits. Although anti-virus software is popular, an ongoing cat-and-mouse cycle of anti-virus development and malware that thwarts the anti-virus has ensued. More recently, trusted hardware-based malware detection techniques are being developed on the premise that it is easier to bypass software-based defenses than hardware-based counterparts. One such approach is the use of hardware performance counters (HPCs) to detect malware for Linux and Android platforms. This paper, for the first time, presents an analytical framework to investigate the security provided by HPC-based malware detection techniques. The HPC readings are periodically monitored over the duration of the program execution for comparison with a golden HPC reading. We develop a mathematical framework to investigate the probability of malware detection, when HPCs are monitored at a pre-determined sampling interval. In other words, given a program, a set of HPCs, and a sampling rate, the framework can be employed to analyze the probability of malware detection.
Hierarchical Clustering of Music towards Human Mood

This paper presents the methods for mining the music, based on mood dimension. Mood is an emerging metadata type and access point in music digital libraries (MDL) and online music repositories. There is a growing interest in developing and evaluating Music Information Retrieval (MIR) systems that can provide automated access to the mood dimension of music. Music is nice thing to all. Mood as a music access feature that is not well understood as well as not standardized. To better understanding we develop method to evaluate automated mood access techniques. This paper explore the relationships that mood has with genre, artist and usage metadata. There is an important consistency within the genre-mood and artist-mood relationships. These consistencies lead to us to develop a cluster based approach by creating a relatively small set of data derived. The emotional component of music has been recognized as the most important factor. Music information behavior studies have also identified music mood as an important criterion used by people in music. Music evokes various human emotions or creates music moods through low level musical features. In fact, typical music consists of one or more moods and this can be used as an important factor for determining the similarity between music. In this paper, we propose a new music retrieval scheme based on the mood change pattern. Keywords: music mood classification, audio features, mood labels.