Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffraction effects in sonar array

Humans can localize a sound source with the help of three effects, Head Related Transfer Function (HRTF), Interaural Time Difference (ITD) and Interaural Level Difference (ILD). With help of these three effects humans can sense where in the space the signal is coming from. The space can be described by three planes: vertical-, horizontaland median plane. Sound localization is described by three dimensions: azimuth angle, elevation angle and distance or velocity detection for static or moving source. Diffraction of sound by human head is described by the diffraction formula. The sound is diffracted by the human head if the dimension of the head is smaller compared to 2λ/3. Sonar means Sound Navigation and Ranging, and has its roots from as early as the beginning of World War I. Sonar technology was actively used under World War I and World War II, and had an increase of interest among the scientists after this period. Sound is pressure perturbations that travels as a wave spreads spherically or cylindrically in the water by describing the decrease of the signal. Sound propagation is affected by absorption, refraction, reflection and scattering. There are three types of sonar equations described in this thesis, the active sonar equation for noise background, the active sonar equation for reverberation background and the passive sonar equation. In sonar the distance to the sound source is calculated by the travel time and the sound velocity of the incoming sound wave. Sound velocity in water is divided into four different regions and is temperature dependent. For circular and spherical arrays, (just like the human head), the sound wave travels direct to the receiver as long as the elements has a "direct" path to the receiver. Otherwise the sound signal is diffracted and travels along the surface of the transducer (with different sound velocity from as in water) until it has a "direct" path to the receiver. This has its limitations, and for some rotational angle on the transducer the sound wave is not possible to detect. This angle limitation depends on the normalized frequency μ = ka = 2πa λ value. where a is radius of the circular transducer. In this thesis I’m going to study at which rotational angle α the signal disappears when sending a sound signal pulse with frequency f = 100kH z and a normalized frequency μ= 25, and compare it with human listening.
The Markov chain embedding problem in a low jump frequency context

We consider the problem of finding the transition rates of a continuous-time homogeneous Markov chain under the empirical condition that the state changes at most once during a time interval of unit length. It is proven that this conditional embedding approach results in a unique intensity matrix for a transition matrix with non-zero diagonal entries. Hence, the presented conditional embedding approach has the merit to avoid the identification phase as well as regularization for the embedding problem. The resulting intensity matrix is compared to the approximation for the Markov generator found by Jarrow.
Generalized Veto Core and a Practical Voting Rule with Optimal Metric Distortion

We revisit the recent breakthrough result of Gkatzelis et al. on (single-winner) metric voting, which showed that the optimal distortion of 3 can be achieved by a mechanism called PluralityMatching. The rule picks an arbitrary candidate for whom a certain candidate-specific bipartite graph contains a perfect matching. Subsequently, a much simpler rule called PluralityVeto was shown to achieve distortion 3 as well; this rule only constructs such a matching implicitly, but it, too, makes some arbitrary decisions affecting the outcome. Our point of departure is the question whether there is an intuitive interpretation of this matching, with the goal of identifying the underlying source of arbitrariness in these rules. We first observe directly from Hall's condition that a matching for candidate c certifies that there is no coalition of voters that can jointly counterbalance the number of first-place votes c received, along with the first-place votes of all candidates ranked lower than c by any voter in this coalition. This condition closely mirrors the classical definition of the (proportional) veto core in social choice theory, except that coalitions can veto a candidate c whenever their size exceeds the plurality score of c, rather than the average number of voters per candidate. Based on this connection, we define a general notion of the veto core with arbitrary weights for voters and candidates which respectively represent the veto power and the public support they have. This connection opens up a number of immediate consequences. Previous methods for electing a candidate from the veto core can be interpreted simply as matching algorithms. Different election methods realize different matchings, in turn witnessing different sets of candidates as winners. Viewed through this lens, we first resolve nontrivial tie breaking issues contributing to the inherent arbitrariness of the above rules. Our approach to ties reveals a novel characterization of the (general) veto core, showing it to be identical to the set of candidates who can emerge as winners under a natural class of matching algorithms reminiscent of SerialDictatorship. Then, we extend this class of voting rules into continuous time, and obtain a highly practical voting rule with optimal distortion 3, which is also intuitive and easy to explain: Each candidate starts off with public support equal to his plurality score. From time 0 to 1, every voter continuously brings down, at rate 1, the support of her bottom choice among not-yet-eliminated candidates. A candidate is eliminated if he is opposed by a voter after his support reaches 0. On top of being anonymous and neutral, the absence of arbitrary non-deterministic choices in this rule allows for the study of other axiomatic properties that are desirable in practice. We show that the canonical voting rule we propose for electing from the (plurality) veto core also satisfies resolvability, monotonicity, majority, majority loser, mutual majority and reversal symmetry, in addition to still guaranteeing metric distortion 3.
Sentence Simplification Using Paraphrase Corpus for Initialization

Neural sentence simplification method based on sequence-to-sequence framework has become the mainstream method for sentence simplification (SS) task. Unfortunately, these methods are currently limited by the scarcity of parallel SS corpus. In this paper, we focus on how to reduce the dependence on parallel corpus by leveraging a careful initialization for neural SS methods from paraphrase corpus. Our work is motivated by the following two findings: (1) Paraphrase corpus includes a large proportion of sentence pairs belonging to SS corpus. (2) We can construct large-scale pseudo parallel SS data by keeping these sentence pairs with a higher complexity difference. Therefore, we propose two strategies to initialize neural SS methods using paraphrase corpus. We train three different neural SS methods with our initialization, which can obtain substantial improvements on the available WikiLarge data compared with themselves without initialization.
Machine Learning Based IoT Adaptive Architecture for Epilepsy Seizure Detection: Anatomy and Analysis

A seizure tracking system is crucial for monitoring and evaluating epilepsy treatments. Caretaker seizure diaries are used in epilepsy care today, but clinical seizure monitoring may miss seizures. Monitoring devices that can be worn may be better tolerated and more suitable for long-term ambulatory use. Many techniques and methods are proposed for seizure detection; However, simplicity and affordability are key concepts for daily use while preserving the accuracy of the detection. In this study, we propose a versal, affordable noninvasive based on a simple real-time k-Nearest-Neighbors (kNN) machine learning that can be customized and adapted to individual users in less than four seconds of training time; the system was verified and validated using 500 subjects, with seizure detection data sampled at 178 Hz, the operated with a mean accuracy of (94.5%).
What can online reinforcement learning with function approximation benefit from general coverage conditions?

In online reinforcement learning (RL), instead of employing standard structural assumptions on Markov decision processes (MDPs), using a certain coverage condition (original from offline RL) is enough to ensure sample-efficient guarantees (Xie et al. 2023). In this work, we focus on this new direction by digging more possible and general coverage conditions, and study the potential and the utility of them in efficient online RL. We identify more concepts, including the $L^p$ variant of concentrability, the density ratio realizability, and trade-off on the partial/rest coverage condition, that can be also beneficial to sample-efficient online RL, achieving improved regret bound. Furthermore, if exploratory offline data are used, under our coverage conditions, both statistically and computationally efficient guarantees can be achieved for online RL. Besides, even though the MDP structure is given, e.g., linear MDP, we elucidate that, good coverage conditions are still beneficial to obtain faster regret bound beyond $\widetilde{O}(\sqrt{T})$ and even a logarithmic order regret. These results provide a good justification for the usage of general coverage conditions in efficient online RL.
Bioactive compounds in cruciferous sprouts and microgreens and the effects of sulfur nutrition.

Cruciferous sprouts and microgreens are a good source of bioactive compounds for human health since they are rich in glucosinolates, polyphenols, carotenoids, vitamins, etc. Sulfur-containing bioactive phytochemicals called glucosinolates are attractive to humans owing to its cancer chemoprevention. They mainly exist in cruciferous vegetables. Sulfur is one of the essential elements for plant and is also an indispensable component of glucosinolates. In this paper, the nutritional value of cruciferous spouts and microgreens is summarized, along with the effects of sulfur nutrition on bioactive phytochemical compounds of cruciferous sprouts and microgreens, especially glucosinolates. The review aims to provide nutritious information so that people may consume cruciferous sprouts and microgreens wisely, and to offer helpful cues for the research on cruciferous sprouts and microgreens products. This article is protected by copyright. All rights reserved.
Unwrapping algorithm based on least-squares, iterations, and phase calibration to unwrap phase highly corrupted by decorrelation noise

A phase unwrapping algorithm based on least-squares, iterations and phase calibration is proposed. We demonstrate that the proposed method is able to process phase data including high amount of decorrelation phase noise. The algorithm is validated by numerical simulations, and comparison to other algorithms shows that the proposed algorithm has better accuracy. The proposed algorithm is applied in digital holography and the unwrapped results demonstrate its practical effectiveness.
The reporting of ethical review and ethical considerations in articles published in medical education journals: A literature review

Health professions education research (HPE‐R) must be ethically sound. Ethical review decisions and other ethical considerations should be clearly reported in journal articles to allow readers to assess the ethical soundness of the research. We explored and evaluated how ethical review decisions and ethical considerations for HPE‐R are reported in health professions education (HPE) journal articles.
Motor potentials evoked by transcranial magnetic stimulation: interpreting a simple measure of a complex system

Transcranial magnetic stimulation (TMS) is a non‐invasive technique that is increasingly used to study the human brain. One of the principal outcome measures is the motor‐evoked potential (MEP) elicited in a muscle following TMS over the primary motor cortex (M1), where it is used to estimate changes in corticospinal excitability. However, multiple elements play a role in MEP generation, so even apparently simple measures such as peak‐to‐peak amplitude have a complex interpretation. Here, we summarize what is currently known regarding the neural pathways and circuits that contribute to the MEP and discuss the factors that should be considered when interpreting MEP amplitude measured at rest in the context of motor processing and patients with neurological conditions. In the last part of this work, we also discuss how emerging technological approaches can be combined with TMS to improve our understanding of neural substrates that can influence MEPs. Overall, this review aims to highlight the capabilities and limitations of TMS that are important to recognize when attempting to disentangle sources that contribute to the physiological state‐related changes in corticomotor excitability.
Nuclear Spaces of Generalized Test Functions

It is well known that a large proportion of the locally convex spaces encountered in distribution theory are nuclear (Grothendieck [4], Treves [10], Schaeffer [8].) In [1] Beurling introduced spaces of test functions more general than those previously used. In this paper we shall show that many of these spaces, and resulting spaces of distributions, are also nuclear spaces.
Applying GP-EKF and GP-SCKF for non-linear state estimation and fault detection in a continuous stirred-tank reactor system

This paper proposes two novel methods for fault detection in non-linear processes. These methods apply a Gaussian process (GP) to model the underlying process, and then the extended Kalman filter (EKF) and square root cubature Kalman filter (SCKF) are used to detect faults. Accordingly, two approaches called the Gaussian process–extended Kalman filter (GP-EKF) and Gaussian process–square root cubature Kalman filter (GP-SCKF) are proposed. The most important characteristic of these proposed methods is that there is no need for an accurate model of the system. Therefore, these methods are considered non-parametric approaches of fault detection in non-linear systems. To illustrate the performance of these algorithms in fault detection, they have been used in a continuous stirred-tank reactor system (CSTR). Both proposed methods are able to detect sensor faults at an early stage.
Response to letter to editor for ‘Quantifying alcohol consumption in the general population by analyzing phosphatidylethanol concentrations in whole blood: results from 24 574 subjects included in the HUNT4 study’

© The Author(s) 2023. Medical Council on Alcohol and Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/lice nses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Alcohol and Alcoholism, 2023, 58(4), 452 https://doi.org/10.1093/alcalc/agad038 Advance access publication date 30 May 2023 Letter to Editor
Chromophore supply modulates cone function and survival in retinitis pigmentosa mouse models

Significance Retinitis pigmentosa (RP) is a blinding disease affecting 2 million people worldwide. The night vision of RP patients is affected first due to the expression of a disease gene in rod photoreceptors. This loss of rod-mediated night vision is followed by a bystander effect on cone photoreceptors, which results in loss of daylight vision. The mechanism of the secondary cone degeneration is unclear. We used genetically modified mice and electrophysiology to explore this question. We found that slowing down the recycling of vitamin A derivatives for the regeneration of opsin, a GPCR, could protect RP cone function and extend their survival. These studies reveal a unique pathway for RP cone degeneration and suggest potential therapies that could benefit RP patients.
A Pilot Controlled Feeding Trial Modifying Protein Intake in Healthy Subjects to Assess Adherence and the Metabolome

Abstract Dietary protein has been shown to impact physiology and pathophysiology, including inflammation and cancer, effects believed to occur through host and microbe-mediated mechanisms. However, the majority of studies investigating this concept have been conducted in animal models, with less information on the optimal approach, tolerability and biologic effects of modifying protein intake in humans. The current study presents a longitudinal controlled feeding trial carried out in healthy humans to acutely modulate protein intake using individualized diets. Adherence to study diets was monitored through subject-reported electronic picture-based assessments and global metabolomic analysis was performed on serum and stool, following each diet stage. Subjects exhibited strong adherence to study diets, with macronutrient intake meeting study goals during each stage. Metabolomic analysis revealed shifts in both serum and feces in association with modifying protein intake, including reciprocal changes in the abundance of amino acids and amino-acid related compounds, when comparing high to reduced protein stages. Additional fecal metabolite changes consisted of reduced microbial fermentation products following the reduced protein diet stage. Collectively, this study provides a robust method to precisely modify and monitor protein intake in humans, as well as assess corresponding metabolomic alterations.
How to use AI in pathology

AI plays an important role in pathology, both in clinical practice supporting pathologists in their daily work, and in research discovering novel biomarkers for improved patient care. Still, AI is in its starting phase, and many pathology labs still need to transition to a digital workflow to be able to enjoy the benefits of AI. In this perspective, we explain the major benefits of AI in pathology, highlight key requirements that need to be met and example how to use it in a typical workflow.
3D shape deformation based on edge collapse mesh simplification

Based on edge collapse mesh simplification,an interactive 3D shape deformation algorithm is proposed.By collapsing edges of triangle mesh to extract key points from original 3D model,the reduced 3D model is generated,and then fixed or movement constraints are forced to the reduced model interactively.By constructing and solving nonlinear optimization equation,optimal solutions of affine transformations are found for all key points.On the basis of affine transformations of key points,deformation mappings for all nodes in original model are calculated.Examples show that edge collapse mesh simplification can be effectively used to reduce the number of polygons and computation,and combining it with nonlinear optimization solution,a real-time display and detail-preserved 3D shape deformation can be realized interactively.
The influence of information processing speed on benefit from learning and memory rehabilitation in TBI: a sub-analysis of the TBI-MEM trial

ABSTRACT Objective This study examined the influence of processing speed (PS) on benefit from treatment with the Kessler Foundation modified Story Memory Technique® (KF-mSMT®), a cognitive rehabilitation intervention shown to improve new learning and memory in traumatic brain injury (TBI). Setting Non-profit research center Participants 62 participants with moderate-to-severe TBI were included, 31 assigned to the treatment group and 31 to the placebo-control group. Design Double-blind, placebo-controlled, randomized clinical trial. The present study represents a post-hoc analysis to examine the role of PS on treatment efficacy. Main Measures Baseline and follow-up neuropsychological assessment including the California Verbal Learning Test, Second Edition (CVLT-II), Memory Assessment Scales – Prose Memory (MAS-PM) and the Symbol Digit Modalities Test (SDMT). Results A treatment effect was not observed on the CVLT-II learning slope in the treatment group relative to the placebo group post-treatment, after co-varying for education, baseline CVLT slope and PS performance. However, performance on a measure of PS, the SDMT, was a significant predictor of post-treatment change following KF-mSMT® treatment. PS was not a significant predictor of benefit from treatment as documented by the MAS-PM immediate or delayed recall score, both of which showed a significant treatment effect. Conclusion Performance on measures of cognitive dysfunction aside from learning and memory impact the benefit from KF-mSMT® treatment. Implications for cognitive rehabilitation for individuals with TBI are discussed.
The protein methylation network in yeast: A landmark in completeness for a eukaryotic post-translational modification

Defining all sites for a post-translational modification in the cell, and identifying their upstream modifying enzymes, is essential for a complete understanding of a modification’s function. However, the complete mapping of a modification in the proteome and definition of its associated enzyme–substrate network is rarely achieved. Here, we present the protein methylation network for Saccharomyces cerevisiae. Through a formal process of defining and quantifying all potential sources of incompleteness, for both the methylation sites in the proteome and also protein methyltransferases, we prove that this protein methylation network is now near-complete. It contains 33 methylated proteins and 28 methyltransferases, comprising 44 enzyme-substrate relationships, and a predicted further three enzymes. While the precise molecular function of most methylation sites is unknown, and it remains possible that other sites and enzymes remain undiscovered, the completeness of this protein modification network is unprecedented and allows us to holistically explore the role and evolution of protein methylation in the eukaryotic cell. We show that while no single protein methylation event is essential in yeast, the vast majority of methylated proteins are themselves essential, being primarily involved in the core cellular processes of transcription, RNA processing, and translation. This suggests that protein methylation in lower eukaryotes exists to fine-tune proteins whose sequences are evolutionarily constrained, providing an improvement in the efficiency of their cognate processes. The approach described here, for the construction and evaluation of post-translational modification networks and their constituent enzymes and substrates, defines a formal process of utility for other post-translational modifications.
Nonlinear data fusion over Entity–Relation graphs for Drug–Target Interaction prediction

Abstract Motivation The prediction of reliable Drug–Target Interactions (DTIs) is a key task in computer-aided drug design and repurposing. Here, we present a new approach based on data fusion for DTI prediction built on top of the NXTfusion library, which generalizes the Matrix Factorization paradigm by extending it to the nonlinear inference over Entity–Relation graphs. Results We benchmarked our approach on five datasets and we compared our models against state-of-the-art methods. Our models outperform most of the existing methods and, simultaneously, retain the flexibility to predict both DTIs as binary classification and regression of the real-valued drug–target affinity, competing with models built explicitly for each task. Moreover, our findings suggest that the validation of DTI methods should be stricter than what has been proposed in some previous studies, focusing more on mimicking real-life DTI settings where predictions for previously unseen drugs, proteins, and drug–protein pairs are needed. These settings are exactly the context in which the benefit of integrating heterogeneous information with our Entity–Relation data fusion approach is the most evident. Availability and implementation All software and data are available at https://github.com/eugeniomazzone/CPI-NXTFusion and https://pypi.org/project/NXTfusion/.