Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Power Receive-Electronics for a Miniature 3D Ultrasound Probe

This thesis describes the design of a front-end application-specific integrated circuit (ASIC), which will be put into the tip of a miniature ultrasound probe for 3D Trans-Esophageal Echocardiography (TEE). To enable 3D TEE, a matrix piezoelectric ultrasound transducer with more than 2000 elements will be used. Since a gastroscopic tube cannot accommodate the cables needed to connect all the transducer elements directly to an imaging system, local channel-count reduction is necessary. The main task of the ASIC is to provide appropriate signal conditioning in the tip of the probe to achieve channel-count reduction. The main goal of this thesis work is to design such an ASIC using simple, low-power circuits, while still maintaining good image quality. In addition to the electronics design, the interconnection between the matrix transducer and the ASIC is another important parallel research topic.
Regime characteristics of a solar thermoelectric generator and comparison of experimental and calculated data

Experiments undertaken to verify predicted solar thermoelectric generator performance are described. The powder metallurgy process for production of Bi-Te-Se-negative and Bi-Te-Sb positive legs of the thermoelectric generator is reviewed, as is the fabrication of the generator. Illuminance, varied according to flux data from the Azerbaidzhan SSR, was provided by a searchlight with a focal-spot maximum heat of 300 C. Volt-ampere characteristics of the thermopiles were determined for various temperature differentials, and the efficiencies were calculated. Both capacity and efficiency were found to rise linearly with increasing temperature. Performances determined from simulation of three actual 12-hour periods agreed well with predicted values.
Printed uni‐planar dual‐band monopole antenna

A printed dual‐band monopole antenna suitable for wireless local‐area network (WLAN) applications in the 2.4‐ and 5.2‐GHz bands is presented. The monopole antenna including the ground plane is printed on the same (front) surface of a narrow FR4 substrate (with no metallic portion in the back side of the substrate), which makes it easy to fabricate. In addition, the antenna also showed good radiation characteristics. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 37: 452–454, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10947
Dauxois ’ “ Non-Gaussian distributions under scrutiny ” under scrutiny ∗ Constantino Tsallis

A recent paper by T. Dauxois entitled “Non-Gaussian distributions under scrutiny” is submitted to scrutiny. Several comments on its content are made, which constitute, at the same time, a brief state-of-the-art review of nonextensive statistical mechanics, a current generalization of the Boltzmann-Gibbs theory. Some inadvertences and misleading sentences are pointed out as well. ∗ To appear in Astrophysics and Space Science (Springer, 2008), in press. Electronic address: tsallis@cbpf.br
The Kinetic Analysis of Phase-Change Data-Storage Media

The analysis of phase-change kinetics is of clear importance for the optimization of chalcogenide media for data storage, as well as for understanding the limits of performance. Published work on phase-change media mostly relies on analyses developed for phase transformations in bulk materials. Such analyses have problems when applied to thin films, and still more when applied to constrained volumes and to cases with extreme temperature gradients and extreme rates of change of temperature. The limitations of conventional kinetic analyses are examined, as are approaches to making the analyses more relevant to phase-change media.
DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers

In surveillance and search and rescue applications, it is important to perform multi-target tracking (MOT) in real-time on low-end devices. Today's MOT solutions employ deep neural networks, which tend to have high computation complexity. Recognizing the effects of frame sizes on tracking performance, we propose DeepScale, a model agnostic frame size selection approach that operates on top of existing fully convolutional network-based trackers to accelerate tracking throughput. In the training stage, we incorporate detectability scores into a one-shot tracker architecture so that DeepScale can learn representation estimations for different frame sizes in a self-supervised manner. During inference, it can adapt frame sizes according to the complexity of visual contents based on user-controlled parameters. To leverage computation resources on edge servers, we propose two computation partition schemes tailored for MOT, namely, edge server only with adaptive frame-size transmission and edge server-assisted tracking. Extensive experiments and benchmark tests on MOT datasets demonstrate the effectiveness and flexibility of DeepScale. Compared to a state-of-the-art tracker, DeepScale++, a variant of DeepScale achieves 1.57X accelerated with only moderate degradation (∼2.3%) in tracking accuracy on the MOT15 dataset in one configuration. We have implemented and evaluated DeepScale++ and the proposed computation partition schemes on a small-scale testbed consisting of an NVIDIA Jetson TX2 board and a GPU server. The experiments reveal non-trivial trade-offs between tracking performance and latency compared to server-only or smart camera-only solutions.
Learning of Frequency-Time Attention Mechanism for Automatic Modulation Recognition

—Recent learning-based image classiﬁcation and speech recognition approaches make extensive use of attention mechanisms to achieve state-of-the-art recognition power, which demonstrates the effectiveness of attention mechanisms. Motivated by the fact that the frequency and time information of modulated radio signals are crucial for modulation mode recognition, this paper proposes a frequency-time attention mechanism for a convolutional neural network (CNN)-based modulation recognition framework. The proposed frequency-time attention module is designed to learn which channel, frequency and time information is more meaningful in CNN for modulation recognition. We analyze the effectiveness of the proposed frequency-time attention mechanism and compare the proposed method with two existing learning-based methods. Experiments on an open-source modulation recognition dataset show that the recognition performance of the proposed framework is better than those of the framework without frequency-time attention and existing learning-based methods.
The Paschen Jump as a Diagnostic of the Diffuse Nebular Continuum Emission in Active Galactic Nuclei

Photoionization modeling of active galactic nuclei (AGN) predicts that diffuse continuum (DC) emission from the broad-line region makes a substantial contribution to the total continuum emission from ultraviolet through near-infrared wavelengths. Evidence for this DC component is present in the strong Balmer jump feature in AGN spectra, and possibly from reverberation measurements that find longer lags than expected from disk emission alone. However, the Balmer jump region contains numerous blended emission features, making it difficult to isolate the DC emission strength. In contrast, the Paschen jump region near 8200 Å is relatively uncontaminated by other strong emission features. Here, we examine whether the Paschen jump can aid in constraining the DC contribution, using Hubble Space Telescope Space Telescope Imaging Spectrograph spectra of six nearby Seyfert 1 nuclei. The spectra appear smooth across the Paschen edge, and we find no evidence of a Paschen spectral break or jump in total flux. We fit multicomponent spectral models over the range 6800–9700 Å and find that the spectra can still be compatible with a significant DC contribution if the DC Paschen jump is offset by an opposite spectral break resulting from blended high-order Paschen emission lines. The fits imply DC contributions ranging from ∼10% to 50% at 8000 Å, but the fitting results are highly dependent on assumptions made about other model components. These degeneracies can potentially be alleviated by carrying out fits over a broader wavelength range, provided that models can accurately represent the disk continuum shape, Fe ii emission, high-order Balmer line emission, and other components.
BBC-Oxford British Sign Language Dataset

In this work, we introduce the BBC-Oxford British Sign Language (BOBSL) dataset, a large-scale video collection of British Sign Language (BSL). BOBSL is an extended and publicly released dataset based on the BSL-1K dataset introduced in previous work. We describe the motivation for the dataset, together with statistics and available annotations. We conduct experiments to provide baselines for the tasks of sign recognition, sign language alignment, and sign language translation. Finally, we describe several strengths and limitations of the data from the perspectives of machine learning and linguistics, note sources of bias present in the dataset, and discuss potential applications of BOBSL in the context of sign language technology. The dataset is available at https://www.robots. ox.ac. uk/~vgg/data/bobsl/.
A study of low noise JFETs exposed to large doses of gamma-rays and neutrons

The monolithic process has proved able to retain the radiation hardness characteristics of discrete JFETs (junction field-effect transistors) up to 1 MGy (100 Mrad) of /sup 60/Co gamma rays. Neutron irradiation tests on discrete devices confirm that Si JFETs show very moderate damage up to 10/sup 13/ Ncm/sup -2/. In comparative tests Si JFET preamplifiers have been irradiated up to 600 kGy (60 Mrad) of gamma rays and up to 4*10/sup 14/ Ncm/sup -2/. The preamplifiers were able to withstand the tests and have shown less radiation sensitivity than GaAs MESFET preamplifiers in the region of peaking times longer than approximately=40 ns. The results are of interest in connection with the survival of front-end electronics for hadron collider experiments which may be subject to in excess of 30 Mrad of ionizing radiation and 10/sup 14/ Ncm/sup -2/ over a 10-year period. <<ETX>>
Risk-averse Heteroscedastic Bayesian Optimization

Many black-box optimization tasks arising in high-stakes applications require risk-averse decisions. The standard Bayesian optimization (BO) paradigm, however, optimizes the expected value only. We generalize BO to trade mean and input-dependent variance of the objective, both of which we assume to be unknown a priori. In particular, we propose a novel risk-averse heteroscedastic Bayesian optimization algorithm (RAHBO) that aims to identify a solution with high return and low noise variance, while learning the noise distribution on the fly. To this end, we model both expectation and variance as (unknown) RKHS functions, and propose a novel risk-aware acquisition function. We bound the regret for our approach and provide a robust rule to report the final decision point for applications where only a single solution must be identified. We demonstrate the effectiveness of RAHBO on synthetic benchmark functions and hyperparameter tuning tasks.
Real-Time Detection of Drowsiness Among Vehicle Drivers: A Machine Learning Algorithm for Embedded Systems

Numerous studies have established the necessity for developing safety equipment to detect drowsiness among vehicle drivers. However, for reliable implementations, such systems must employ dependable sources of stimuli; through Electrooculography (EOG), the tendencies of drowsiness can be directly sensed by measuring blinks of prolonged durations. While conventional machine learning (ML) algorithms can be utilized for the detection and classification of these prolonged blinks (PB), executing them on microcontroller units (MCU) may prove to be a laborious task. Hence, by keeping resource constraints and practicality in mind, an ML algorithm is proposed in this study to identify PBs executed by an individual with desirable accuracy and precision while being efficient enough to be deployed on portable wearables using economic MCUs. Furthermore, the suggested algorithm is subjected to multiple rounds of testing in this study thereby, establishing its possibility as a feasible drowsiness detection measure for wearable systems.
SE-PIM: In-Memory Acceleration of Data-Intensive Confidential Computing

Demand for data-intensive workloads and confidential computing are the prominent research directions shaping the future of cloud computing. Computer architectures are evolving to accommodate the computing of large data. Meanwhile, a plethora of works has explored protecting the confidentiality of the in-cloud computation in the context of hardware-based secure enclaves. However, the approach has faced challenges in achieving efficient large data computation. In this article, we present a novel design, called se-pim, that retrofits Processing-In-Memory (PIM) as a data-intensive confidential computing accelerator. PIM-accelerated computation renders large data computation highly efficient by minimizing data movement. Based on our observation that moving computation closer to memory can achieve efficiency of computation and confidentiality of the processed information simultaneously, we study the advantages of confidential computing inside memory. We construct our findings into a software-hardware co-design called se-pim. Our design illustrates the advantages of PIM-based confidential computing acceleration. We study the challenges in adapting PIM in confidential computing and propose a set of imperative changes, as well as a programming model that can utilize them. Our evaluation shows se-pim can provide a side-channel resistant secure computation offloading and run data-intensive applications with negligible performance overhead compared to the baseline PIM model.
Dataset of Fake News Detection and Fact Verification: A Survey

The rapid increase in fake news, which causes significant damage to society, triggers many fake news related studies, including the development of fake news detection and fact verification techniques. The resources for these studies are mainly available as public datasets taken from Web data. We surveyed 118 datasets related to fake news research on a large scale from three perspectives: (1) fake news detection, (2) fact verification, and (3) other tasks; for example, the analysis of fake news and satire detection. We also describe in detail their utilization tasks and their characteristics. Finally, we highlight the challenges in the fake news dataset construction and some research opportunities that address these challenges. Our survey facilitates fake news research by helping researchers find suitable datasets without reinventing the wheel, and thereby, improves fake news studies in depth.
Automatic Diversity in the Software Supply Chain

Despite its obvious benefits, the increased adoption of package managers to automate the reuse of libraries has opened the door to a new class of hazards: supply chain attacks. By injecting malicious code in one library, an attacker may compromise all instances of all applications that depend on the library. To mitigate the impact of supply chain attacks, we propose the concept of Library Substitution Framework. This novel concept leverages one key observation: when an application depends on a library, it is very likely that there exists other libraries that provide similar features. The key objective of Library Substitution Framework is to enable the developers of an application to harness this diversity of libraries in their supply chain. The framework lets them generate a population of application variants, each depending on a different alternative library that provides similar functionalities. To investigate the relevance of this concept, we develop ARGO, a proof-of-concept implementation of this framework that harnesses the diversity of JSON suppliers. We study the feasibility of library substitution and its impact on a set of 368 clients. Our empirical results show that for 195 of the 368 java applications tested, we can substitute the original JSON library used by the client by at least 15 other JSON libraries without modifying the client's code. These results show the capacity of a Library Substitution Framework to diversify the supply chain of the client applications of the libraries it targets.
Minimax estimation of low-rank quantum states and their linear functionals

In classical statistics, a well known paradigm consists in establishing asymptotic equivalence between an experiment of i.i. d. observations and a Gaussian shift experiment, with the aim of obtaining optimal estimators in the former complicated model from the latter simpler model. In particular, a statistical experiment consisting of $n$ i.i. d observations from d-dimensional multinomial distributions can be well approximated by an experiment consisting of $d-1$ dimensional Gaussian distributions. In a quantum version of the result, it has been shown that a collection of $n$ qudits (d-dimensional quantum states) of full rank can be well approximated by a quantum system containing a classical part, which is a $d-1$ dimensional Gaussian distribution, and a quantum part containing an ensemble of $d(d-1)/2$ shifted thermal states. In this paper, we obtain a generalization of this result when the qudits are not of full rank. We show that when the rank of the qudits is $r$, then the limiting experiment consists of an $r-1$ dimensional Gaussian distribution and an ensemble of both shifted pure and shifted thermal states. For estimation purposes, we establish an asymptotic minimax result in the limiting Gaussian model. Analogous results are then obtained for estimation of a low-rank qudit from an ensemble of identically prepared, independent quantum systems, using the local asymptotic equivalence result. We also consider the problem of estimation of a linear functional of the quantum state. We construct an estimator for the functional, analyze the risk and use quantum local asymptotic equivalence to show that our estimator is also optimal in the minimax sense.
Simplex Initialization: A Survey of Techniques and Trends

The simplex method is one of the most fundamental technologies for solving linear programming (LP) problems and has been widely applied to different practical applications. In the past literature, how to improve and accelerate the simplex method has attracted plenty of research. One important way to achieve this goal is to find a better initialization method for the simplex. In this survey, we aim to provide an overview about the initialization methods in the primal and dual simplex, respectively. We also propose several potential future directions about how to improve the existing initialization methods with the help of advanced learning technologies.
Safety and efficacy of cervical disc arthroplasty in preventing the adjacent segment disease: a meta-analysis of mid- to long-term outcomes in prospective, randomized, controlled multicenter studies

Objectives Cervical disc arthroplasty (CDA) has become an alternative treatment for cervical radiculopathy and myelopathy. This technique preserves appropriate motion at both the index and adjacent disc levels and consequently may prevent adjacent segment degeneration (ASD). The authors performed a meta-analysis to compare the safety and efficacy of CDA to those of the gold standard, anterior cervical discectomy and fusion (ACDF). Both surgical and clinical parameters were employed to verify the hypothesis that CDA can reduce the risk of ASD. Methods The meta-analysis comprised high-quality randomized controlled trials that compared CDA and ACDF treatments of cervical degenerative disc disease. Included papers reported data for at least one of the following outcomes: 1) surgical parameters, 2) questionnaire clinical indices (pre- and postoperative values), and 3) complication rates at 24 months; in addition, for ASD we analyzed 60 month or longer follow-ups. We used mean differences (MDs) or ORs to compare treatment effects between CDA and ACDF. Results Twenty studies with 3,656 patients (2,140 with CDA and 1,516 with ACDF) met the inclusion criteria. CDA surgery, with mean duration longer than that of ACDF, was associated with higher blood loss. Visual analog scale neck pain score was significantly smaller for CDA (mean difference =−2.30, 95% CI [−3.72; −0.87], P=0.002). The frequency of dysphagia/dysphonia (OR =0.69, 95% CI [0.49; 0.98], P=0.04) as well as the long-term ASD rate for CDA was significantly smaller (OR =0.33, 95% CI [0.21; 0.50], P<0.0001). Conclusion A significantly lower probability of ASD reoperations in the CDA cohort after a 60-month or longer follow-up was the most important finding of this study. Despite the moderate quality of this evidence, the pooled data corroborated for the very first time that CDA was efficacious in preventing ASD.
The Trust Movement in British Industry: A Study of Business Organisation

Industrial Combination [1938] D H Macgregor 270pp The Tendency Towards Industrial Combination [1913] George R Carter 414pp The Trust Movement in British Industry [1907] Henry W Macrosty 414pp Trusts in British Industry [1922] J Morgan Rees 277pp The Dynamics of Industrial Combination [1931] H A Marquand 214pp American Business Enterprises [1907] Douglas Knoop 123pp International Combines in Modern Industry [1934] Alfred Plummer 200pp The Structure of Competititve Industry [1931] E A G Robinson 189pp
Modeling and Control of an Omnidirectional Micro Aerial Vehicle Equipped with a Soft Robotic Arm

Flying manipulators are aerial drones with attached rigid-bodied robotic arms and belong to the latest and most actively developed research areas in robotics. The rigid nature of these arms often lack compliance, flexibility, and smoothness in movement. This work proposes to use a soft-bodied robotic arm attached to an omnidirectional micro aerial vehicle (OMAV) to leverage the compliant and flexible behavior of the arm, while remaining maneuverable and dynamic thanks to the omnidirectional drone as the floating base. The unification of the arm with the drone poses challenges in the modeling and control of such a combined platform; these challenges are addressed with this work. We propose a unified model for the flying manipulator based on three modeling principles: the Piecewise Constant Curvature (PCC) and Augmented Rigid Body Model (ARBM) hypotheses for modeling soft continuum robots and a floating-base approach borrowed from the traditional rigid-body robotics literature. To demonstrate the validity and usefulness of this parametrisation, a hierarchical model-based feedback controller is implemented. The controller is verified and evaluated in simulation on various dynamical tasks, where the nullspace motions, disturbance recovery, and trajectory tracking capabilities of the platform are examined and validated. The soft flying manipulator platform could open new application fields in aerial construction, goods delivery, human assistance, maintenance, and warehouse automation.
Generating Diverse Realistic Laughter for Interactive Art

We propose an interactive art project to make those rendered invisible by the COVID-19 crisis and its concomitant solitude reappear through the welcome melody of laughter, and connections created and explored through advanced laughter synthesis approaches. However, the unconditional generation of the diversity of human emotional responses in high-quality auditory synthesis remains an open problem, with important implications for the application of these approaches in artistic settings. We developed LaughGANter, an approach to reproduce the diversity of human laughter using generative adversarial networks (GANs). When trained on a dataset of diverse laughter samples, LaughGANter generates diverse, high quality laughter samples, and learns a latent space suitable for emotional analysis and novel artistic applications such as latent mixing/interpolation and emotional transfer.