Miracle-dz/UniFAR-FTUs huggingface dataset:
Systematic approach to the digital compensation for deterministic nonideal characteristics in /spl Sigma//spl Delta/ modulation

In this work we propose a systematic approach to the digital compensation for nonideal deterministic characteristics in a /spl Sigma//spl Delta/ modulator. It is based on inverting the complete nonlinear deterministic transfer function of the modulator. This operation leads us to a whole set of possible reconstruction estimates from which we systematically pick the geometric center as signal decoding scheme. This approach already proves effective in the particular case of static deviations in the built-in analog-to-digital converter and digital-to-analog converter of the modulator.
A high order differential equation based wall distance solver

A computationally eﬃcient high-order solver is developed to compute the wall distances, which are typically used for turbulence modelling, peripheral ﬂow simulations, Computer Aided Design (CAD) etc. The wall distances are computed by solving the diﬀerential equations namely: Eikonal, Hamilton-Jacobi (H-J) and Poisson. The computational beneﬁt of using high-order schemes (explicit/compact schemes) for wall-distance solvers, both in terms of accuracy and computational time, has been demonstrated. A new H-J formulation based on the localized artiﬁcial diﬀusivity (LAD) approach has been proposed, which has produced results with an accuracy comparable to that of the Eikonal formulation. When compared to the baseline H-J solver using upwind schemes, the solution accuracy has improved by an order of magnitude and the calculations are ≈ 5 times faster using the modiﬁed H-J formulation. A modiﬁed curvature correction has also been implemented into the H-J solver to account for the near-wall errors due to concave/convex wall curvatures. The performance of the solver using diﬀerent schemes has been tested both on the steady canonical test cases and the unsteady test cases like ‘piston-cylinder arrangement’, ‘bouncing cube’ and ‘burning of a star grain propellant’ where the wall-distance evolves with time.
A Wave Approach to the Noise Properties of Linear Microwave Devices

Noise temperature or noise factor are important parameters for many microwave devices. Their dependence on source characteristics is classically established using low-frequency concepts such as impedance, admittance, voltage, and current sources. This paper presents a derivation of the noise properties of linear two-ports in terms of noise waves, which leads to a convenient measurement method in distributed systems.
Invited Keynote Address: Advanced Signal Processing and Condition Monitoring Session

When physical models are of a high complexity, a signal processing approach is helpful for providing accurate information about a system and its failures. The session entitled "Advanced Signal Processing and Condition Monitoring" contains papers who propose new advanced signal processing methods in the context of condition monitoring, diagnostic and fault detection. The methods proposed tackle with the analysis, modelling and/or detection of nonstationary and/or nonlinear signals in time, frequency, time-frequency and/or time-scale domains using parametric, non-parametric and/or statistical approaches. Tools as optimization techniques can cope with the high non-linearity of the system to solve. The methods proposed are successful in decision making and bring on a step up in real-life signal processing applications. Signals or considered models come from domains such as acoustics, vibroacoustics, mechanics and electrical engineering. This keynote address introduces the session papers and gives some insight in spectral and time-frequency analysis.
Leader-follower function for autonomous military convoys

The French Military Robotic Study Program (introduced in Aerosense 2003), sponsored by the French Defense Procurement Agency and managed by Thales Airborne Systems as the prime contractor, focuses on about 15 robotic themes which can provide an immediate "operational added value. " The paper details the "robotic convoy" theme (named TEL1), which main purpose is to develop a robotic leader-follower function so that several unmanned vehicles can autonomously follow teleoperated, autonomous or on-board driven leader. Two modes have been implemented: Perceptive follower: each autonomous follower anticipates the trajectory of the vehicle in front of it, thanks to a dedicated perception equipment. This mode is mainly based on the use of perceptive data, without any communication link between leader and follower (to lower the cost of future mass development and extend the operational capabilities). Delayed follower: the leader records its path and transmits it to the follower; the follower is able to follow the recorded trajectory again at any delayed time. This mode uses localization data got from inertial measurements. The paper presents both modes with detailed algorithms and the results got from the military acceptance tests performed on wheeled 4x4 vehicles (DARDS French ATD).
A Coupled SPH-Spectral Method for the Simulation of Wave Train Impacts on a FPSO

In order to help in achieving a correct design of structures subjected to wave impacts, CFD tools with a sufficient accuracy should be developed. But nowadays, modelling accurately both wave propagations and the resulting impact of a wave train on a complex geometry is still challenging. This paper deals with the introduction of a weak coupling between a Spectral method and Smoothed Particle Hydrodynamics (SPH), used as complementary techniques for modelling respectively wave propagation and high-dynamic impact of a wave train on a complex-shaped floating body. Comparisons with experiments are provided as a validation of these preliminary developments.Copyright © 2014 by ASME
HRnV-Calc: A software package for heart rate n-variability and heart rate variability analysis

Objective: Heart rate variability (HRV) has been proven to be an important indicator of physiological status for numerous applications. Despite the progress and active developments made in HRV metric research over the last few decades, the representation of the heartbeat sequence upon which HRV is based has received relatively little attention. The recently introduced heart rate n-variability (HRnV) offers an alternative to R-to-R peak interval representations which complements conventional HRV analysis by considering HRV behavior on varying scales. Although HRnV has been shown to improve triage in pilot studies, there is
Hardware implementation of series hybrid active power filter using a novel control strategy based on generalised instantaneous power theory

In the area of power quality conditioners, the series hybrid active power filter (SHAPF) has been focused more in recent times because of its multi-functionality and lower inverter rating requirement. Existing control strategies for SHAPF are based on extracting either source current harmonics or load voltage harmonics. In this study, a novel control scheme based on decomposing multiphase voltage vector into different vectors that represent different components of power is proposed for SHAPF. The mathematical formulation of the proposed control scheme with its applications to SHAPF is presented. The validity of the proposed control scheme is verified by simulation as well as experimental study.
A Novel Optimized Asynchronous Federated Learning Framework

Federated Learning (FL) since proposed has been applied in many fields, such as credit assessment, medical, etc. Because of the difference in the network or computing resource, the clients may not update their gradients at the same time that may take a lot of time to wait or idle. That's why Asynchronous Federated Learning (AFL) method is needed. The main bottleneck in AFL is communication. How to find a balance between the model performance and the communication cost is a challenge in AFL. This paper proposed a novel AFL framework VAFL. And we verified the performance of the algorithm through sufficient experiments. The experiments show that VAFL can reduce the communication times about 51.02% with 48.23% average communication compression rate and allow the model to be converged faster. The code is available at https://github.com/RobAI-Lab/NAFL
Alternative method for assessment of southwestern Atlantic humpback whale population status

The population of humpback whales (Megaptera novaeangliae) wintering off eastern South America was exploited by commercial whaling almost to the point of extinction in the mid-twentieth century. Since cessation of whaling in the 1970s it is recovering, but the timing and level of recovery is uncertain. We implemented a Bayesian population dynamics model describing the population’s trajectory from 1901 and projecting it to 2040 to revise a previous population status assessment that used Sampling-Importance-Resampling in a Bayesian framework. Using our alternative method for model fitting (Markov chain Monte Carlo), which is more widely accessible to ecologists, we replicate a “base case scenario” to verify the effect on model results, and introduce additional data to update the status assessment. Our approach allowed us to widen the previous informative prior on carrying capacity to better reflect scientific uncertainty around historical population levels. The updated model provided more precise estimates for population sizes over the period considered (1901–2040) and suggests that carrying capacity (K: median 22,882, mean 22,948, 95% credible interval [CI] 22,711–23,545) and minimum population size (N1958: median 305, mean 319, 95% CI 271–444) might be lower than previously estimated (K: median 24,558, mean 25,110, 95% CI 22,791–31,118; N1958: median 503, mean 850, 95% CI 159–3,943). However, posterior 95% credible intervals of parameters in the updated model overlap those of the previous study. Our approach provides an accessible framework for investigating the status of depleted animal populations for which information is available on historical mortality (e.g., catches) and intermittent estimates of population size and/or trend.
Adaptive Fluorodeoxyglucose-Positron Emission Tomography Based Chemotherapy Selection for Metastatic Non-small Cell Lung Cancer

Objectives The change in tumor fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET) scan after one cycle of platinum-based chemotherapy has been shown to predict progression-free and overall survival (PFS and OS) among advanced non-small cell lung cancer (NSCLC) patients. Using early FDG-PET response to determine subsequent chemotherapy, we aim to evaluate the role that adaptive chemotherapy regimens have on later CT response, PFS, and OS in patients with advanced NSCLC. Materials and Methods Chemotherapy-naïve patients with metastatic NSCLC received carboplatin and paclitaxel (CP) on day one and repeated FDG-PET on day 18. PET-responding patients continued CP chemotherapy for a total of four cycles. PET non-responders were switched to alternate docetaxel and gemcitabine (DG) for three additional cycles. The primary outcome was the CT Response Evaluation Criteria in Solid Tumors (RECIST 1.0) response. Secondary endpoints included PFS and OS. Results Forty-six patients initiated treatment with chemotherapy on trial and were evaluable by PET/CT. Of these, 19 (41%) met the FDG-PET criteria for the response after a single cycle of CP. Only one non-responding patient had a CT response. Despite the lack of CT response in the DG arm, no trend for worse PFS or OS was seen between the two arms. Conclusions This work demonstrates that changing chemotherapy in the event of non-response by PET did not lead to improved CT RECIST response. However, non-responding patients who switched chemotherapy had similar PFS and OS to those who responded by PET and continued the same regimen.
Lidar with Velocity: Motion Distortion Correction of Point Clouds from Oscillating Scanning Lidars

—Lidar point cloud distortion from moving object is an important problem in autonomous driving, and recently becomes even more demanding with the emerging of newer lidars, which feature back-and-forth scanning patterns. Accurately estimating moving object velocity would not only provide a tracking capability but also correct the point cloud distortion with more accurate description of the moving object. Since lidar measures the time-of-ﬂight distance but with a sparse angular resolution, the measurement is precise in the radial measurement but lacks angularly. Camera on the other hand provides a dense angular resolution. In this paper, Gaussian- based lidar and camera fusion is proposed to estimate the full velocity and correct the lidar distortion. A probabilistic Kalman-ﬁlter framework is provided to track the moving objects, estimate their velocities and simultaneously correct the point clouds distortions. The framework is evaluated on real road data and the fusion method outperforms the traditional ICP-based and point-cloud only method. The complete working framework is open-sourced 1 to accelerate the adoption of the emerging lidars.
InspectJS: Leveraging Code Similarity and User-Feedback for Effective Taint Specification Inference for JavaScript

Static analysis has established itself as a weapon of choice for detecting security vulnerabilities. Taint analysis in particular is a very general and powerful technique, where security policies are expressed in terms of forbidden flows, either from untrusted input sources to sensitive sinks (in integrity policies) or from sensitive sources to untrusted sinks (in confidentiality policies). The appeal of this approach is that the taint-tracking mechanism has to be implemented only once, and can then be parameterized with different taint specifications (that is, sets of sources and sinks, as well as any sanitizers that render otherwise problematic flows innocuous) to detect many different kinds of vulnerabilities. But while techniques for implementing scalable inter-procedural static taint tracking are fairly well established, crafting taint specifications is still more of an art than a science, and in practice tends to involve a lot of manual effort. Past work has focussed on automated techniques for inferring taint specifications for libraries either from their implementation or from the way they tend to be used in client code. Among the latter, machine learning-based approaches have shown great promise. In this work we present our experience combining an existing machine-learning approach to mining sink specifications for JavaScript libraries with manual taint modelling in the context of GitHub's CodeQL analysis framework. We show that the machine-learning component can successfully infer many new taint sinks that either are not part of the manual modelling or are not detected due to analysis incompleteness. Moreover, we present techniques for organizing sink predictions using automated ranking and code-similarity metrics that allow an analysis engineer to efficiently sift through large numbers of predictions to identify true positives.
Reflection theorems for number rings generalizing the Ohno-Nakagawa identity

The Ohno-Nakagawa (O-N) reflection theorem is an unexpectedly simple identity relating the number of GL2Z-classes of binary cubic forms (equivalently, cubic rings) of two different discriminants D, −27D; it generalizes cubic reciprocity and the Scholz reflection theorem. In this paper, we present a new approach to this theorem using Fourier analysis on the adelic cohomology H1(AK ,M) of a finite Galois module, modeled after the celebrated Fourier analysis on AK used in Tate’s thesis. This method reduces reflection theorems of O-N type to local identities. We establish reflection theorems of O-N type for cubic forms and rings over arbitrary number fields, and also for quadratic forms counting by a peculiar invariant a(b − 4ac). We also find relations for the number of forms over Z[1/N ] and for forms of highly non-squarefree discriminant (discriminant reduction). In a sequel to this paper, we will deal with reflection theorems for quartic rings, 2× 3× 3 symmetric boxes, and binary quartic forms. In these cases the local step is much more involved. MSC2020 codes: 11R16, 11A15, 11E76, 11R54, 11G20 I have no competing interests. This research was partly supported by a Graduate Research fellowship from the National Science Foundation (grant #DGE-1646566).
On the Effectiveness of Sparsification for Detecting the Deep Unknowns

Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world. Previous methods commonly rely on an OOD score derived from the overparameterized weight space, while largely overlooking the role of sparsiﬁcation . In this paper, we reveal important insights that reliance on unimportant weights and units can directly attribute to the brittleness of OOD detection. To mitigate the issue, we propose a sparsiﬁcation-based OOD detection framework termed DICE . Our key idea is to rank weights based on a measure of contribution, and selectively use the most salient weights to derive the output for OOD detection. We provide both empirical and theoretical insights, characterizing and explaining the mechanism by which DICE improves OOD detection. By pruning away noisy signals, DICE provably reduces the output variance for OOD data, resulting in a sharper output distribution and stronger separability from ID data. DICE establishes superior performance, reducing the FPR95 by up to 24.69% compared to the previous best method 1 .
Information-theoretic formulation of dynamical systems: causality, modeling, and control

The problems of causality, modeling, and control for chaotic, high-dimensional dynamical systems are formulated in the language of information theory. The central quantity of interest is the Shannon entropy, which measures the amount of information in the states of the system. Within this framework, causality is quantified by the information flux among the variables of interest in the dynamical system. Reduced-order modeling is posed as a problem related to the conservation of information in which models aim at preserving the maximum amount of relevant information from the original system. Similarly, control theory is cast in information-theoretic terms by envisioning the tandem sensor-actuator as a device reducing the unknown information of the state to be controlled. The new formulation is used to address three problems about the causality, modeling, and control of turbulence, which stands as a primary example of a chaotic, high-dimensional dynamical system. The applications include the causality of the energy transfer in the turbulent cascade, subgrid-scale modeling for large-eddy simulation, and flow control for drag reduction in wall-bounded turbulence.
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach

Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can be exploited by cybercriminals and cause severe losses to the users. Informing users of their PII exposure in social media is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, advanced techniques are needed to extract users’ exposed PII in social media automatically, whereas most existing studies remain manual. While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency. However, DL-based IE models often require large-scale labeled data for training, but PII-labeled social media posts are difficult to obtain due to privacy concerns. Also, these models rely heavily on pre-trained word embeddings, while PII in social media often varies in forms and thus has no fixed representations in pre-trained word embeddings. In this study, we propose the Deep Transfer Learning for PII Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE transfers knowledge learned from publicly available PII data to social media in order to address the problem of rare PII-labeled data. Moreover, our framework leverages Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide PIIE without relying on pre-trained word embeddings. Evaluation against benchmark IE models indicates that our approach outperforms state-of-the-art DL-based IE models. An ablation analysis further confirms the efficacy of each component in our model. Our proposed framework can facilitate various applications, such as PII misuse prediction and privacy risk assessment, thereby protecting the privacy of internet users.
Aspects of BFKL physics at HERA

Aspects of small x physics at HERA are discussed in honor of the work of Lev Li-patov, who inﬂuenced and motivated a whole generation of physicists to investigate this new region of phase space. The introduction of transverse momentum dependent parton densities is one of the essential aspects of small x and BFKL physics, and its extension to all ﬂavors with application to various processes marks a new milestone.
End-to-End Learned Image Compression with Quantized Weights and Activations

End-to-end Learned image compression (LIC) has reached the traditional hand-crafted methods such as BPG (HEVC intra) in terms of the coding gain. However, the large network size prohibits the usage of LIC on resource-limited embedded systems. This paper reduces the network complexity by quantizing both weights and activations. 1) For the weight quantization, we study different kinds of grouping and quantization scheme at first. A channel-wise non-linear quantization scheme is determined based on the coding gain analysis. After that, we propose a fine tuning scheme to clip the weights within a certain range so that the quantization error can be reduced. 2) For the activation quantization, we first propose multiple non-linear quantization codebooks with different maximum dynamic ranges. By selecting an optimal one through a multiplexer, the quantization range can be saturated to the greatest extent. In addition, we also exploit the mean-removed quantization for the analysis transform outputs in order to reduce the bit-width cost for the specific channel with the large non-zero mean. By quantizing each weight and activation element from 32-bit floating point to 8-bit fixed point, the memory cost for both weight and activation can be reduced by 75% with negligible coding performance loss. As a result, our quantized LIC can still outperform BPG in terms of MS-SSIM. To our best knowledge, this is the first work to give a complete analysis on the coding gain and the memory cost for a quantized LIC network, which validates the feasibility of the hardware implementation.