Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of the large scale HV-CMOS pixel sensor MuPix8

The Mu3e experiment is searching for the charged lepton flavour violating decay\break μ+→ e+ e− e+, aiming for an ultimate sensitivity of one in 1016 decays. In an environment of up to 109 muon decays per second the detector needs to provide precise vertex, time and momentum information to suppress accidental and physics background. The detector consists of cylindrical layers of 50 μm thin High Voltage Monolithic Active Pixel Sensors (HV-MAPS) placed in a 1 T magnetic field. The measurement of the trajectories of the decay particles allows for a precise vertex and momentum reconstruction. Additional layers of fast scintillating fibre and tile detectors provide sub-nanosecond time resolution. The MuPix8 chip is the first large scale prototype, proving the scalability of the HV-MAPS technology. It is produced in the AMS aH18 180 nm HV-CMOS process. It consists of three sub-matrices, each providing an untriggered datastream of more than 10 MHits/s. The latest results from laboratory and testbeam characterisation are presented, showing an excellent performance with efficiencies >99.6 % and a time resolution better than 10 ns achieved with time walk correction.
Use of Ceftriaxone in Treating Cognitive and Neuronal Deficits Associated With Dementia With Lewy Bodies

Dementia with Lewy bodies (DLB) is caused by accumulation of Lewy bodies, destruction of mitochondria, and excess of glutamate in synapses, which eventually leads to excitotoxicity, neurodegeneration, and cognitive impairments. Ceftriaxone (CEF) reduces excitotoxicity by increasing glutamate transporter 1 expression and glutamate reuptake. We investigated whether CEF can prevent cognitive decline and neurological deficits and increase neurogenesis in DLB rats. Male Wistar rats infused with viral vector containing human alpha-synuclein (α-syn) gene, SNCA, in the lateral ventricle were used as a rat model of DLB. CEF (100 mg/kg/day, i.p.) was injected in these rats for 27 days. The active avoidance test and object recognition test was performed. Finally, the brains of all the rats were immunohistochemically stained to measure α-syn, neuronal density, and newborn cells in the hippocampus and substantia nigra. The results revealed that DLB rats had learning and object recognition impairments and exhibited cell loss in the nigrostriatal dopaminergic system, and hippocampal CA1, and dentate gyrus (DG). Additionally, DLB rats had fewer newborn cells in the DG and substantia nigra pars reticulata and more α-syn immune-positive cells in the DG. Treatment with CEF improved cognitive function, reduced cell loss, and increased the number of newborn cells in the brain. To our knowledge, this is the first study showing that CEF prevents loss of neurogenesis in the brain of DLB rats. CEF may therefore has clinical potential for treating DLB.
Open Source Tesseract in Re-OCR of Finnish Fraktur from 19th and Early 20th Century Newspapers and Journals - Collected Notes on Quality Improvement

This paper presents work that has been carried out in the National Library of Finland to improve optical character recognition (OCR) quality of a Finnish historical newspaper and journal collection 1771–1910. Work and results reported in the paper are based on a 500 000 word ground truth (GT) sample of the Finnish language part of the whole collection. The sample has three different parallel parts: a manually corrected ground truth version, original OCR with ABBYY FineReader v. 7 or v. 8, and an ABBYY FineReader v. 11 reOCRed version. Based on this sample and its page image originals we have developed a re-OCRing process using the open source software package Tesseract v. 3.04. 01. Our methods in the re-OCR include image preprocessing techniques, usage of morphological analyzers and a set of weighting rules for resulting candidate words. Besides results based on the GT sample we present also results of re-OCR for a 29 year period of one newspaper of our collection, Uusi Suometar. The paper describes the results of our re-OCR process including the latest results. We also state some of the main lessons learned during the development work.
ER stress stimulates production of the key antimicrobial peptide, cathelicidin, by forming a previously unidentified intracellular S1P signaling complex

Significance The cathelicidin antimicrobial peptide (CAMP) is an innate immune element that promotes antimicrobial defense, but excessive CAMP can stimulate inflammation and tumorigenesis. We recently discovered that external perturbations that induce subtoxic levels of endoplasmic reticulum (ER) stress increase sphingosine-1-phosphate (S1P) production, in turn activating NF-κB–mediated CAMP synthesis. We report here that S1P interacts with the heat shock proteins (HSP90α and GRP94) through a previously unidentified S1P receptor-independent intracellular mechanism, followed by the activation of NF-κB leading to stimulation of CAMP production. These studies illuminate the critical role of both ER stress and S1P in orchestrating stress-specific signals that enhance innate immunity. We recently identified a previously unidentified sphingosine-1-phosphate (S1P) signaling mechanism that stimulates production of a key innate immune element, cathelicidin antimicrobial peptide (CAMP), in mammalian cells exposed to external perturbations, such as UVB irradiation and other oxidative stressors that provoke subapoptotic levels of endoplasmic reticulum (ER) stress, independent of the well-known vitamin D receptor-dependent mechanism. ER stress increases cellular ceramide and one of its distal metabolites, S1P, which activates NF-κB followed by C/EBPα activation, leading to CAMP production, but in a S1P receptor-independent fashion. We now show that S1P activates NF-κB through formation of a previously unidentified signaling complex, consisting of S1P, TRAF2, and RIP1 that further associates with three stress-responsive proteins; i.e., heat shock proteins (GRP94 and HSP90α) and IRE1α. S1P specifically interacts with the N-terminal domain of heat shock proteins. Because this ER stress-initiated mechanism is operative in both epithelial cells and macrophages, it appears to be a universal, highly conserved response, broadly protective against diverse external perturbations that lead to increased ER stress. Finally, these studies further illuminate how ER stress and S1P orchestrate critical stress-specific signals that regulate production of one protective response by stimulating production of the key innate immune element, CAMP.
Observational signature of a wind bubble environment for double neutron star mergers

During the in-spiral stage of a compact binary, a wind bubble could be blown into the interstellar medium, if electromagnetic radiation due to the binary orbital motion is strong enough. Therefore, short-duration gamma-ray bursts (SGRBs) due to double neutron star mergers would in principle happen in a wind bubble environment, which can influence the propagation of the SGRB jet and consequent afterglow emission. By calculating the dynamics and synchrotron radiation of the jet-driven external shock, we reveal that an abrupt jump could appear in the afterglow light curves of SGRBs and the observational time of the jump is dependent on the viewing angle. This light curve jump provides an observational signature to constrain the radius of the wind bubble and thus the power of the electromagnetic radiation of the binary, by combining with gravitational wave detection.
Eigenmode Analysis of a Two Element Segmented Capped Monopole Antenna

The behavior of a two element segmented capped monopole antenna is described in terms of three natural resonances of the antenna structure. A numerical eigenmode solver is used to derive the resonant frequency and Q-factor of the natural resonances, as well as the impedance properties of these modes when excited individually. The impedance of the antenna is described as a lumped element network, where the component values of the network are determined from the impedance parameters of the resonant modes and the static properties of the antenna structure. The model is applied to predict the behavior of the antenna when a lumped element inductor is used to connect the two segments. The inductor tunes the properties of one of the three resonances, enabling the impedance bandwidth to be optimized. The analysis is then extended to predict the behavior of the antenna when the two vertical elements have different radii, a configuration that enables wide bandwidth operation without the use of an inductor. The modal analysis accurately predicts the impedance and far-field properties of the antennas. Analyzing the antenna in terms of its natural resonant modes provides physical insights into both its behavior and the fundamental limitations of its performance.
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions

Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs, and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present {\em MR-GNN}, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (LSTMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.
SynFuzz: Efficient Concolic Execution via Branch Condition Synthesis

Concolic execution is a powerful program analysis technique for exploring execution paths in a systematic manner. Compare to random-mutation-based fuzzing, concolic execution is especially good at exploring paths that are guarded by complex and tight branch predicates (e.g., (a*b) == 0xdeadbeef). The drawback, however, is that concolic execution engines are much slower than native execution. One major source of the slowness is that concolic execution engines have to the interpret instructions to maintain the symbolic expression of program variables. In this work, we propose SynFuzz, a novel approach to perform scalable concolic execution. SynFuzz achieves this goal by replacing interpretation with dynamic taint analysis and program synthesis. In particular, to flip a conditional branch, SynFuzz first uses operation-aware taint analysis to record a partial expression (i.e., a sketch) of its branch predicate. Then it uses oracle-guided program synthesis to reconstruct the symbolic expression based on input-output pairs. The last step is the same as traditional concolic execution - SynFuzz consults a SMT solver to generate an input that can flip the target branch. By doing so, SynFuzz can achieve an execution speed that is close to fuzzing while retain concolic execution's capability of flipping complex branch predicates. We have implemented a prototype of SynFuzz and evaluated it with three sets of programs: real-world applications, the LAVA-M benchmark, and the Google Fuzzer Test Suite (FTS). The evaluation results showed that SynFuzz was much more scalable than traditional concolic execution engines, was able to find more bugs in LAVA-M than most state-of-the-art concolic execution engine (QSYM), and achieved better code coverage on real-world applications and FTS.
Zero-shot Knowledge Transfer via Adversarial Belief Matching

Performing knowledge transfer from a large teacher network to a smaller student is a popular task in modern deep learning applications. However, due to growing dataset sizes and stricter privacy regulations, it is increasingly common not to have access to the data that was used to train the teacher. We propose a novel method which trains a student to match the predictions of its teacher without using any data or metadata. We achieve this by training an adversarial generator to search for images on which the student poorly matches the teacher, and then using them to train the student. Our resulting student closely approximates its teacher for simple datasets like SVHN, and on CIFAR10 we improve on the state-of-the-art for few-shot distillation (with 100 images per class), despite using no data. Finally, we also propose a metric to quantify the degree of belief matching between teacher and student in the vicinity of decision boundaries, and observe a significantly higher match between our zero-shot student and the teacher, than between a student distilled with real data and the teacher. Code available at: this https URL
Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Graph neural networks have become one of the most important techniques to solve machine learning problems on graph-structured data. Recent work on vertex classification proposed deep and distributed learning models to achieve high performance and scalability. However, we find that the feature vectors of benchmark datasets are already quite informative for the classification task, and the graph structure only provides a means to denoise the data. In this paper, we develop a theoretical framework based on graph signal processing for analyzing graph neural networks. Our results indicate that graph neural networks only perform low-pass filtering on feature vectors and do not have the non-linear manifold learning property. We further investigate their resilience to feature noise and propose some insights on GCN-based graph neural network design.
Clinical Trial: CYP2D6 Related Dose Escalation of Tamoxifen in Breast Cancer Patients With Iranian Ethnic Background Resulted in Increased Concentrations of Tamoxifen and Its Metabolites

Introduction: The polymorphic enzyme cytochrome P450 2D6 (CYP2D6) catalyzes a major step in the bioactivation of tamoxifen. Genotyping of clinically relevant CYP2D6 alleles and subsequent dose adjustment is a promising approach to individualize breast cancer therapy. The aim of this study was to investigate the relationship between the plasma levels of tamoxifen and its metabolites and different CYP2D6 genotypes under standard (20 mg/day) and dose-adjusted therapy (Registration ID in Iranian Registry of Clinical Trials: IRCT2015082323734N1). Materials and Methods: Using TaqMan® assays common alleles of CYP2D6 (∗1, ∗2, ∗4, ∗5, ∗6, ∗10, ∗17, and ∗41) and gene duplication were identified in 134 breast cancer patients. Based on CYP2D6 genotypes patients with an activity score 1 (n = 15) and 0–0.5 (n = 2) were treated with tamoxifen adjusted dosage of 30 and 40 mg/day, respectively. The concentration of tamoxifen and its metabolites before and after 4 and 8 months of dose adjustment were measured using LC-MS/MS technology. Results: At baseline, (Z)-endoxifen plasma concentrations (33 ± 15.5, 28.1 ± 14, 26.6 ± 23.4, 14.3 ± 8.6, and 10.7 ± 5.5 nmol/l for EM/EM, EM/IM, EM/PM, IM/IM and PM/PM, respectively) and the metabolic ratio (Z)-Endoxifen/N-desmethyltamoxifen (0.0558 ± 0.02, 0.0396 ± 0.0111, 0.0332 ± 0.0222, 0.0149 ± 0.0026, and 0.0169 ± 0.0177 for EM/EM, EM/IM, EM/PM, IM/IM, and PM/PM, respectively) correlated with CYP2D6 genotype (Kruskal–Wallis p = 0.013 and p < 0.0001, respectively). Dose escalation to 30 and 40 mg/day in patients with a CYP2D6 activity score of 1 (n = 15) and 0–0.5 (n = 2) resulted in a significant increase in (Z)-endoxifen plasma levels (22.17 ± 24.42, 34.43 ± 26.54, and 35.77 ± 28.89 nmol/l at baseline, after 4 and 8 months, respectively, Friedman p = 0.0388) along with the plasma concentrations of tamoxifen and its other metabolites. No severe side effects were recorded during dose escalation. Conclusion: For the first time, we show the feasibility of dose escalation of tamoxifen in breast cancer patients with compromised CYP2D6 activity and Iranian ethnic background to increase the plasma concentrations of (Z)-endoxifen.
Robust Point Cloud Based Reconstruction of Large-Scale Outdoor Scenes

Outlier feature matches and loop-closures that survived front-end data association can lead to catastrophic failures in the back-end optimization of large-scale point cloud based 3D reconstruction. To alleviate this problem, we propose a probabilistic approach for robust back-end optimization in the presence of outliers. More specifically, we model the problem as a Bayesian network and solve it using the Expectation-Maximization algorithm. Our approach leverages on a long-tail Cauchy distribution to suppress outlier feature matches in the odometry constraints, and a Cauchy-Uniform mixture model with a set of binary latent variables to simultaneously suppress outlier loop-closure constraints and outlier feature matches in the inlier loop-closure constraints. Furthermore, we show that by using a Gaussian-Uniform mixture model, our approach degenerates to the formulation of a state-of-the-art approach for robust indoor reconstruction. Experimental results demonstrate that our approach has comparable performance with the state-of-the-art on a benchmark indoor dataset, and outperforms it on a large-scale outdoor dataset. Our source code can be found on the project website.
Ball speed and spin estimation in table tennis using a racket-mounted inertial sensor

In this paper, we present an approach for ball speed and spin estimation in table tennis based on a single racket-mounted inertial sensor. We conducted a research study comprising eight subjects performing different stroke types resulting in various ball speeds and spins. All ball-racket impacts were recorded with a high-speed camera for further evaluation. Firstly, several assumptions and simplifications of the unknown initial ball properties and the movements of player and racket had to be made. Secondly, the racket blade velocity right after impact was calculated. This was combined with a rebound model between the ball and the rubber to predict the speed and spin of the ball. Overall, the ball speed of strokes with forward spin could be estimated with an accuracy of 79.4% and for strokes with backward spin with an accuracy of 87.4%. The spin was estimated with an accuracy of 73.5% and 75.0%, respectively. To our knowledge, this contribution is the first attempt to estimate characteristics of rebounded balls with a single racket-mounted inertial sensor considering unknown initial conditions and constraints.
Image-Based Techniques in a Hybrid Collision Detector

Most collision detection methods developed so far are based on geometrical object-space interference tests. While this remains the basic mode of investigation for geometric algorithms, the requirements for interactive rates and complex geometry predominate in commercial applications. In this article, we propose a new mode of collision detection based on an image-space approach. This approach breaks the object-space collision detection bottleneck by distributing the computational load onto the hardware graphics pipeline. The image-space approach, in conjunction with efficient bounding-box strategies in the object-space, has the potential to handle complex object interactions at interactive rates.
Performance Evaluation of PLC Under the Combined Effect of Background and Impulsive Noises

Power line communication (PLC) is the use of power lines for the purpose of electronic data transmission. The presence of additive noise, namely, background noise and impulsive noise, significantly affects the performance of a PLC system. While the background noise is modeled by Nakagami-m distribution, the impulsive noise is modeled using Middleton class A distribution. In this letter, we study the performance of a PLC system under the combined effect of Nakagami-m background noise and Middleton class A impulsive noise assuming binary phase shift keying signaling. The probability density function of decision variable under the influence of additive noise (sum of background noise and impulsive noise) is derived. We also derive an analytical expression for the average bit error rate of the considered PLC system. The analytical expressions are validated by close matching to the simulation results. The analysis presented in this letter closely predicts the behavior of the PLC system under the combined effect of background and impulsive noises.
Teichmüller Curves in Genus Two: Square-Tiled Surfaces and Modular Curves

This work is a contribution to the classification of Teichmuller curves in the moduli space $\M_2$ of Riemann surfaces of genus 2. While the classification of primitive Teichmuller curves in $\M_2$ is complete, the classification of the imprimitive curves, which is related to branched torus covers and square-tiled surfaces, remains open. Conjecturally, the classification is completed as follows. Let $W_{d^2}[n] \subset \M_2$ be the 1-dimensional subvariety consisting of those $X \in \M_2$ that admit a primitive degree $d$ holomorphic map $\pi: X \to E$ to an elliptic curve $E$, branched over torsion points of order $n$. It is known that every imprimitive Teichmuller curve in $\M_2$ is a component of some $W_{d^2}[n]$. The {\em parity conjecture} states that (with minor exceptions) $W_{d^2}[n]$ has two components when $n$ is odd, and one when $n$ is even. In particular, the number of components of $W_{d^2}[n]$ does not depend on $d$. In this work we establish the parity conjecture in the following three cases: (1) for all $n$ when $d=2,3,4,5$; (2) when $d$ and $n$ are prime and $n > (d^3-d)/4$; and (3) when $d$ is prime and $n > C_d$, where $C_d$ is an implicit constant that depends on $d$. In the course of the proof we will see that the modular curve $X(d) = \overline{\Hyp \big/ \Gamma(d)}$ is itself a square-tiled surface equipped with a natural action of $\SLZ$. The parity conjecture is equivalent to the classification of the finite orbits of this action. It is also closely related to the following {\em illumination conjecture}: light sources at the cusps of the modular curve illuminate all of $X(d)$, except possibly some vertices of the square-tiling. Our results show that the illumination conjecture is true for $d \le 5$.
Machine-learning-based interatomic potential for phonon transport in perfect crystalline Si and crystalline Si with vacancies

We report that single interatomic potential, developed using Gaussian regression of density functional theory calculation data, has high accuracy and flexibility to describe phonon transport with ab initio accuracy in two different atomistic configurations: perfect crystalline Si and crystalline Si with vacancies. The high accuracy of second- and third-order force constants from the Gaussian approximation potential (GAP) are demonstrated with phonon dispersion, Gruneisen parameter, three-phonon scattering rate, phonon-vacancy scattering rate, and thermal conductivity, all of which are very close to the results from density functional theory calculation. We also show that the widely used empirical potentials (Stillinger-Weber and Tersoff) produce much larger errors compared to the GAP. The computational cost of GAP is higher than the two empirical potentials, but five orders of magnitude lower than the density functional theory calculation. Our work shows that GAP can provide a new opportunity for studying phonon transport in partially disordered crystalline phases with the high predictive power of ab initio calculation but at a feasible computational cost.