Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Learning for Automated Visual Inspection of Manufactured Products

Quality control is a key activity performed by manufacturing enterprises to ensure products meet quality standards and avoid potential damage to the brand's reputation. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing. In addition, artificial intelligence enables higher degrees of automation, reducing overall costs and time required for defect inspection. In this research, we compare three active learning approaches and five machine learning algorithms applied to visual defect inspection with real-world data provided by Philips Consumer Lifestyle BV. Our results show that active learning reduces the data labeling effort without detriment to the models' performance.
PGIR 20eid (SN2020qmp): A Type IIP Supernova at 15.6 Mpc discovered by the Palomar Gattini-IR survey

Aims. We present a detailed analysis of SN 2020qmp, a nearby Type IIP core-collapse supernova (CCSN) that was discovered by the Palomar Gattini-IR survey in the galaxy UGC07125 (distance of ≈ 15 . 6 ± 4Mpc). We illustrate how the multiwavelength study of this event helps our general understanding of stellar progenitors and circumstellar medium (CSM) interactions in CCSNe. We highlight the importance of near-infrared (NIR) surveys for detections of supernovae in dusty environments. Methods. We analyze data from observations in various bands: radio, NIR, optical, and X-rays. We use optical and NIR data for a spectroscopic and spectro-polarimetric study of the supernova and to model its light curve (LC). We obtain an estimate of the zero-age main-sequence (ZAMS) progenitor mass from the luminosity of the [O i ] doublet lines ( λλ 6300 , 6364) normalized to the decay power of 56 Co. We also independently estimate the explosion energy and ZAMS progenitor mass through hydrodynamical LC modeling. From radio and X-ray observations, we derive the mass-loss rate and microphysical parameters of the progenitor star, and we investigate possible deviations from energy equipartition of magnetic ﬁelds and electrons in a standard CSM interaction model. Finally, we simulate a sample of CCSNe with plausible distributions of brightness and extinction, within 40Mpc, and test what fraction of the sample is detectable at peak light by NIR surveys versus optical surveys. Results. SN 2020qmp displays characteristic hydrogen lines in its optical spectra as well as a plateau in its optical LC, hallmarks of a Type IIP supernova. We do not detect linear polarization during the plateau phase, with a 3 σ upper limit of 0.78%. Through hydrodynamical LC modeling and an analysis of its nebular spectra, we estimate a ZAMS progenitor mass of around 11.0 M (cid:12) and an explosion energy of around 0 . 8 × 10 51 erg. We ﬁnd that the spectral energy distribution cannot be explained by a simple CSM interaction model, assuming a constant shock velocity and a steady mass-loss rate. In particular, the excess X-ray luminosity compared with the synchrotron radio luminosity suggests deviations from equipartition. Finally, we demonstrate the advantages of NIR surveys over optical surveys for the detection of dust-obscured CCSNe in the local Universe. Speciﬁcally, our simulations show that the Wide- Field Infrared Transient Explorer will detect up to 14 more CCSNe (out of the 75 expected in its footprint) within 40Mpc over ﬁve years than would an optical survey equivalent to the Zwicky Transient Facility. Conclusions. We have determined or constrained the main properties of SN 2020qmp and its progenitor, highlighting the value of multiwavelength follow-up observations of nearby CCSNe. We have shown that forthcoming NIR surveys will enable us to improve constraints on the local CCSN rate by detecting obscured supernovae that would be missed by optical searches.
Find bugs in static bug finders

Static bug finders (also known as static code analyzers, e.g., Find-Bugs, SonarQube) have been widely-adopted by developers to find bugs in real-world software projects. They leverage predefined heuristic static analysis rules to scan source code or binary code of a software project, and report violations to these rules as warnings to be verified. However, the advantages of static bug finders are overshadowed by such issues as uncovered obvious bugs, false positives, etc. To improve these tools, many techniques have been proposed to filter out false positives reported or design new static analysis rules. Nevertheless, the under-performance of bug finders can also be caused by the incorrectness of current rules contained in the static bug finders, which is not explored yet. In this work, we propose a differential testing approach to detect bugs in the rules of four widely-used static bug finders, i.e., SonarQube, PMD, SpotBugs, and ErrorProne, and conduct a qualitative study about the bugs found. The experiment on 2,728 open source projects reveals 46 bugs in the static bug finders, among which 30 are fixed or confirmed and the left are awaiting confirmation. We also summarize 13 bug patterns in the static analysis rules based on their context and root causes, which can serve as the checklist for designing and implementing other rules and/or in other tools. This study indicates that the commonly-used static bug finders are not as reliable as they might have been envisaged. It not only demonstrates the effectiveness of our approach, but also highlights the need to continue improving the reliability of the static bug finders.
Non-Euclidean Analysis of Joint Variations in Multi-Object Shapes

This paper considers joint analysis of multiple functionally related structures in classification tasks. In particular, our method developed is driven by how functionally correlated brain structures vary together between autism and control groups. To do so, we devised a method based on a novel combination of (1) non-Euclidean statistics that can faithfully represent non-Euclidean data in Euclidean spaces and (2) a non-parametric integrative analysis method that can decompose multi-block Euclidean data into joint, individual, and residual structures. We find that the resulting joint structure is effective, robust, and interpretable in recognizing the underlying patterns of the joint variation of multi-block non-Euclidean data. We verified the method in classifying the structural shape data collected from cases that developed and did not develop into Autistic Spectrum Disorder (ASD).
Minimal hypersurfaces in manifolds of Ricci curvature bounded below

Abstract In this paper, we study the angle estimate of distance functions from minimal hypersurfaces in manifolds of Ricci curvature bounded from below using Colding’s method in [T. H. Colding, Ricci curvature and volume convergence, Ann. of Math. (2) 145 1997, 3, 477–501]. With Cheeger–Colding theory, we obtain the Laplacian comparison for limits of distance functions from minimal hypersurfaces in the version of Ricci limit space. As an application, if a sequence of minimal hypersurfaces converges to a metric cone C ⁢ Y × ℝ n - k {CY\times\mathbb{R}^{n-k}} ( 2 ≤ k ≤ n {2\leq k\leq n} ) in a non-collapsing metric cone C ⁢ X × ℝ n - k {CX\times\mathbb{R}^{n-k}} obtained from ambient manifolds of almost nonnegative Ricci curvature, then we can prove a Frankel property for the cross section Y of CY. Namely, Y has only one connected component in X.
Efficient Estimation of Sensor Biases for the 3-D Asynchronous Multi-Sensor System

An important preliminary procedure in multi-sensor data fusion is sensor registration, and the key step in this procedure is to estimate sensor biases from their noisy measurements. There are generally two difficulties in this bias estimation problem: one is the unknown target states which serve as the nuisance variables in the estimation problem, and the other is the highly nonlinear coordinate transformation between the local and global coordinate systems of the sensors. In this article, we focus on the 3-dimensional asynchronous multi-sensor scenario and propose a weighted nonlinear least squares (NLS) formulation by assuming that there is a target moving with a nearly constant velocity. We propose two possible choices of the weighting matrix in the NLS formulation, which correspond to classical and weighted NLS estimation, respectively. To address the intrinsic nonlinearity, we propose a block coordinate descent (BCD) algorithm for solving the formulated problem, which alternately updates different kinds of bias estimates. Specifically, the proposed BCD algorithm involves solving linear LS problems and nonconvex quadratically constrained quadratic program (QCQP) problems with special structures. Instead of adopting the semidefinite relaxation technique, we develop a much more computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve the nonconvex QCQP subproblems. The convergence of the ADMM to the global solution of the QCQP subproblems is established under mild conditions. The effectiveness and efficiency of the proposed BCD algorithm are demonstrated via numerical simulations.
Underwater 3D Reconstruction Using Light Fields

Underwater 3D reconstruction is challenging due to the refraction of light at the water-air interface (most electronic devices cannot be directly submerged in water). In this paper, we present an underwater 3D reconstruction solution using light ﬁeld cameras. We ﬁrst develop a light ﬁeld camera calibration algorithm that simultaneously estimates the camera parameters and the geometry of the water-air interface. We then design a novel depth estimation algorithm for 3D reconstruction. Speciﬁcally, we match correspon-dences on curved epipolar lines caused by water refraction. We also observe that the view-dependent specular reﬂection is very weak in the underwater environment, resulting the angularly sampled rays in light ﬁeld has uniform in-tensity. We therefore propose an angular uniformity constraint for depth optimization. We also develop a fast algorithm for locating the angular patches in presence of non-linear light paths. Extensive synthetic and real experiments demonstrate that our method can perform underwater 3D reconstruction with high accuracy.
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks

In federated learning (FL), reducing the communication overhead is one of the most critical challenges since the parameter server and the mobile devices share the training parameters over wireless links. With such consideration, we adopt the idea of SignSGD in which only the signs of the gradients are exchanged. Moreover, most of the existing works assume Channel State Information (CSI) available at both the mobile devices and the parameter server, and thus the mobile devices can adopt fixed transmission rates dictated by the channel capacity. In this work, only the parameter server side CSI is assumed, and channel capacity with outage is considered. In this case, an essential problem for the mobile devices is to select appropriate local processing and communication parameters (including the transmission rates) to achieve a desired balance between the overall learning performance and their energy consumption. Two optimization problems are formulated and solved, which optimize the learning performance given the energy consumption requirement, and vice versa. Furthermore, considering that the data may be distributed across the mobile devices in a highly uneven fashion in FL, a stochastic sign-based algorithm is proposed. Extensive simulations are performed to demonstrate the effectiveness of the proposed methods.
AC-current induced quench protection system

An apparatus (100) for quenching at least part (110) of a superconductor in a superconducting state in reply to a quench signal to initiate a transition from the superconducting state into a normal-conducting state comprises: means (120) for providing an alternating (AC) current of a predetermined strength and/or predetermined frequency to the at least part (110) of the superconductor, wherein the means (120) for providing the AC current comprises a control terminal (130) configured to receive the quench signal. The means (120) for providing the AC current is configured to be activated in response of receiving the quench signal at the control terminal (130) so that the AC current flows through the at least part (110) of the superconductor, wherein the predetermined strength and/or the predetermined frequency is selected such that the transition from the superconducting state into the normal-conducting state is triggered.
A Fokker–Planck Approach for Modeling the Stochastic Phenomena in Magnetic and Resistive Random Access Memory Devices

Embedded nonvolatile memory technologies, such as resistive random access memory (RRAM) and spin-transfer torque magnetic RAM (STT MRAM), are increasingly being researched for application in neuromorphic computing and hardware accelerators for artificial intelligence (AI). However, the stochastic write processes in these memory technologies affect their yield and need to be studied alongside process variations, which drastically increase the complexity of yield analysis using the Monte Carlo approach. Therefore, we propose an approach based on the Fokker–Planck equation for modeling the stochastic write processes in STT MRAM and RRAM devices. Moreover, we show that our proposed approach can reproduce the experimental results for both STT-MRAM and RRAM devices.
Second-Order Asymptotically Optimal Universal Outlier Hypothesis Testing

We revisit the universal outlier hypothesis testing (Li et al., TIT 2014) and derive fundamental limits for the optimal test. In outlying hypothesis testing, one is given multiple observed sequences, where most sequences are generated i.i. d. from a nominal distribution. The task is to discern the set of outlying sequences that are generated according to anomalous distributions. The nominal and anomalous distributions are unknown. We study the tradeoff among the probabilities of misclassification error, false alarm and false reject for tests that satisfy weak conditions on the rate of decrease of these error probabilities as a function of sequence length. Specifically, we propose a threshold-based universal test that ensures exponential decay of misclassification error and false alarm probabilities. We study two constraints on the false reject probabilities, one is that it be a non-vanishing constant and the other is that it have an exponential decay rate. For both cases, we characterize bounds on the false reject probability, as a function of the threshold, for each pair of nominal and anomalous distributions and demonstrate the optimality of our test in the generalized Neyman-Pearson sense. We first consider the case of at most one outlier and then generalize our results to the case of multiple outliers where the number of outliers is unknown and each outlier can follow a different anomalous distribution. Index Terms Second-order asymptotic approximations, Error exponents, Misclassification error, False alarm, False reject,
Attitudes Towards Digital Health Technology: Introducing the Digital Health Scale

Objective: The study sought to investigate the relationship between attitude towards digital health technology and age, gender and frequency of use of digital health technology and to consider whether age, gender and frequency of use present potential barriers to accessing future healthcare in the UK. Differences in technological affinity are likely to lead to differences in the adoption of digital health technology and subsequent inequalities in healthcare between older and younger people and between men and women. Design: The study represents an example of a technology adoption study employing a survey-based cross sectional correlational design. Attitude towards digital health technology was measured using the 20 item Digital Health Scale. Age, gender, frequency of use of health technology and employment status data were gathered using a demographics questionnaire. The opportunity sample (N = 247) included volunteer participants aged 16-84 years (M = 31.7, SD = 19.35, 156 females and 91 males). Results: Results indicated a significant negative correlation between age and positive attitude towards digital health technology (r = -0.24, p < .01). Gender differences in attitudes towards digital health technology were non-significant (p > .05). Significant differences in frequency of use were also found, where occasional and frequent use resulted in more positive attitudes than never having used digital health technology (p < 0.05) and participants reporting frequent use were significantly older than those reporting never or occasional use (p < .05) Conclusion: Findings identified age, but not gender, as a significant factor in attitude towards digital health technology, suggesting that continued and increased reliance on digital technology in healthcare may lead to age, but not gender, related inequalities in access to healthcare in the UK. That frequent users of digital health technology were also older, highlights the greater demand for healthcare services by older individuals and is further evidence for the potential of digital healthcare to lead to age related inequalities in access to and provision of healthcare. Recommendations for successful application of digital healthcare technology are considered in the light of these findings.
From Screening to Treatment of Pancreatic Cancer: A Comprehensive Review.

Introduction Pancreatic adenocarcinoma is a devastating malignancy, associated with a grim prognosis, due to its silent presentation and lack of diagnostic tests. In addition, treatment options are limited to few agents, such as 5-FU, irinotecan, oxaliplatin, gemcitabine and nab-paclitaxel. Methods We performed a literature search for relevant published clinical trials, abstracts of trials in progress and ongoing or planned trials for the treatment of APC using Pubmed.com, ClinicalTrials.gov and American Society of Clinical Oncology (ASCO) abstract search as sources. We present an in-depth analysis of the phase I-III clinical trials determining the role and efficacy of different modalities. We also describe rationale for future investigation. Discussion Despite advances in first-line and second-line therapies for APC, median OS remains short of a year. We need collaborative efforts between the cooperative groups, institutions, community practices and industry to work together in enrolling these patients in clinical trials. In addition to use new technologies, such as organoids, we must pay attention to the palliative aspect of care for these patients from the beginning including nutritionist, social worker and supportive care health providers to assist with goals of care, symptom management and end of life discussions.
Successful percutaneous treatment of late outflow graft failure of the left ventricular assist device: a long‐term follow‐up

Continuous flow left ventricular assist device (LVAD) outflow graft stenosis constitutes a severe complication. Treatment options include surgical pump exchange, transcatheter procedures, or systemic thrombolysis. We present a case of a spontaneous mechanical twisting of the outflow graft at two distinct points, which was treated by a two‐step percutaneous stent implantation. Self‐expanding stents were used during the first procedure. We also ensured distal bilateral percutaneous neuroprotection against cerebrovascular embolism. During the second, previously unpredicted procedure, we used balloon‐expandable bare‐metal stents to overcome the torque of the graft, because of their higher radial force. It was assumed that the external, self‐expanding layer of the stent might protect the graft from the bare‐metal stents. The effects of the treatment were monitored both clinically and through computed tomography angiography. The check‐up 12 months later revealed nothing of note. Interventional transcatheter procedures are a safe treatment option for outflow graft stenosis, with good long‐term effects. Both self‐expanding and balloon‐expandable stents can be used for transcatheter intervention. Both the incidence of the graft twisting and the radial force of the implanted stent seem to be critical and robust predictors of the long‐term result of percutaneous therapy.
Axillary ultrasound-guided approach for the brachial plexus in pig cadavers: A descriptive study

Effective multimodal analgesia techniques are required when pigs are used as models in orthopedic human research. Regional anesthesia is a widely used technique to provide perioperative analgesia in animals undergoing orthopedic surgery. The brachial plexus (BP) block is indicated to desensitize the forelimb in many species but has not been yet described in pigs. The main aims of this study were to develop an ultrasound-guided axillary approach for the BP and to evaluate injectate spread and nerve staining in pig cadavers. Eight fresh F1 cross breed German Large White and German Landrace male pig cadavers were enrolled. Two cadavers were used for anatomical dissection of the axillary space and to determine the disposition of the BP. Six cadavers were used to perform a bilateral axillary ultrasound-guided approach for the BP, and after injecting 0.3 ml/kg of a solution of 2% lidocaine and new methylene blue (L-NMB), these were dissected to determine injectate spread and nerve staining. Upon dissection, the BP was observed in all the cases surrounded by the axillary sheath and in close proximity to the axillary artery. Ultrasonographic scanning and guidance for the approach to the BP was feasible in all cadavers and upon dissection, all the nerves forming the BP were stained in all their quadrants and in all the cases. In conclusion, the injection of 0.3 ml/kg of L-NMB through an ultrasound-guided axillary approach to the BP was feasible and adequate to entirely stain the BP in all pig cadavers. Further clinical studies are required to evaluate the effectiveness of this technique in live animals.