Miracle-dz/UniFAR-FTUs huggingface dataset:
Insulin-Regulated Aminopeptidase in Women with Breast Cancer: A Role beyond the Regulation of Oxytocin and Vasopressin

Simple Summary Insulin-regulated aminopeptidase (IRAP) is a well-known enzyme involved mainly in the regulation of the peptide hormones, oxytocin and vasopressin. However, this enzyme activity has hardly been analyzed in breast cancer patients. Additionally, the influence of both the hormonal status (pre or postmenopause) and the administration of neoadjuvant chemotherapy have rarely been studied. We show that there is a weak association between IRAP activity and the circulating levels of peptide hormones with variations depending on the hormonal status and the neoadjuvant treatment, and propose a role beyond oxytocin and vasopressin regulation that is related to the local mammary renin-angiotensin system and glucose transportation to the cells. Abstract Insulin-regulated aminopeptidase (IRAP) is the only enzyme known to cleave oxytocin and vasopressin; however, it is also the high-affinity binding site for angiotensin IV (AngIV) receptor type 4 (AT4) ligands and it is related to insulin-dependent glucose transporters through the translocation of the glucose transporter type 4 (GLUT4). Previous studies have demonstrated an association between IRAP activity and the number and size of mammary tumors in an animal model of breast cancer (BC). Also, a highly significant increase in IRAP activity has been found in BC tissue from women patients. Here, we found no changes in circulating IRAP in premenopausal (preMP) women, but it increased significantly in postmenopausal (postMP) women not treated with neoadjuvant chemotherapy (NACH). However, in women treated with NACH, IRAP activity increased in both preMP and postMP women. Two years of follow-up indicated lower levels of IRAP activity in untreated preMP women, but a return to control levels in untreated postMP women, while IRAP activity returned to control levels in women treated with NACH. Circulating oxytocin decreased in both preMP and postMP women during the follow-up period. Differences in Oxytocin appeared between preMP and postMP women treated with NACH, but not in women who were not treated with NACH. On the contrary, circulating vasopressin increased in untreated and treated preMP and postMP women, with most of the differences related to the hormonal status as well as the neoadjuvant treatment during the two year follow-up We propose that IRAP is involved in mechanisms related not only to oxytocin and/or vasopressin regulation, but also to the local mammary RAS through AngIV and its role in glucose transportation through the IRAP/GLUT4 system.
Results of a portfolio approach to intramural research funding at an academic medical center

In response to stagnant Federal grant funding levels and to catalyze early stage or high-risk research not currently supported by the NIH, many academic medical centers (AMCs) provide supplemental intramural funding to faculty investigators. However, it can be challenging to decide how to deploy these funds for maximum impact. We conducted a retrospective, descriptive analysis to explore trends in applications and awards associated with an institution-wide intramural funding center at a major U.S. AMC. From 2010 to 2017, the Brigham Research Institute at Brigham and Women’s Hospital awarded a total of 354 grants totaling over $9 million to affiliated researchers through six distinct and complementary grant programs. The number of applicants remained essentially stable, despite expansion of the funding program portfolio. Distribution of applicants and awardees by academic rank and gender generally reflected that of medical school faculty at large. This descriptive analysis demonstrates interest in a diverse range of intramural funding programs among AMC faculty, and a lack of overt rank or gender bias in the programs’ awardees. However, it highlights the institution’s need to better understand the amount of residual unmet demand for intramural funding; the degree to which underrepresented constituencies can and should be actively supported; and the “return on investment” of these grants.
An optimized Geospatial Data Storage and Retrieval Using SpatialHadoop in Cloud Environment

Technology advancement increase the volume of remote sensing data growth exponentially increase processing capability. Objective of this research proposes a secured and efficient way of storing and retrieval the geospatial data in public cloud using Spatial Hadoop mechanism in cloud computing environment. To secure the geospatial data retrieval in the public cloud Attribute based authentication protocol. The spatialHadoop-GIS is a high performance and secure accessing spatial data for running gauge spatial queries on spatialHadoop.
Greedy InfoMax for Self-Supervised Representation Learning

We propose a novel deep learning method for lo-cal self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without back-propagating a global error signal, we split a deep neural network into a stack of gradient-isolated modules. Each module is trained to maximize the mutual information between its consecutive outputs using the InfoNCE bound from Oord et al. (2018). Despite this greedy training, we demonstrate that each module improves upon the output of its predecessor, and that these representation are competitive with end-to-end optimized models on downstream classiﬁcation tasks in the audio domain. The proposal enables optimizing modules asynchronously, allowing large-scale distributed training of very deep neural networks on unlabelled datasets.
Saccadic intrusions in amyotrophic lateral sclerosis (ALS)

The attempt to quietly fixate at a small visual object is continuously interrupted by a variety of fixational eye movements comprising, among others, a continuum of saccadic intrusions (SI) which range in size from microsaccades with amplitudes ≤0.25° to larger refixation saccades of up to about 2°. The size and frequency of SI varies considerably among individuals and is known to increase in neurodegenerative diseases such as progressive supranuclear palsy (PSP), and amyotrophic lateral sclerosis (ALS). However, studies of ALS disagree whether also the frequency of SI increases. We undertook an analysis of SI in 119 ALS patients and 47 age-matched healthy controls whose eye movements during fixation and tests of executive functions (e.g antisaccades) had been recorded by video-oculography according to standardised procedures. SI were categorised according to their spatio-temporal patterns as stair case, back-and-forth and square wave jerks (a subcategory of back-and-forth). The SI of patients and controls were qualitatively similar (same direction preferences, similar differences between patterns), but were enlarged in ALS. Notably however, no increase of SI frequency could be demonstrated. Yet, there were clear correlations with parameters such as eye blink rate or errors in a delayed saccade task that suggest an impairment of inhibitory mechanisms, in keeping with the notion of a frontal dysfunction in ALS. However, it remains unclear how the impairment of inhibitory mechanisms in ALS could selectively increase the amplitude of intrusions without changing their frequency of occurrence.
Small Intelligent Home System with Speech Recognition Based On ARM Processor

The research is to design and implement the speaker independent speech recognition home system, and the final function achieves the recognition of isolated words. Through the module function integration, the speech recognition module LD3320 and the development board OK6410-A are connected. The results are processed, analyzed and judged after the speech module recognition results have been received by the ARM processor. Then the processor calls its own module function to indicate the recognition results. The program is implemented by C language in cross system compiler under Linux system. The downloading program is completed by the debugging function of the uboot. The opening and closing of the LED and the buzzer on the development board is completed by using 4 simple speech commands, including "kai deng", "la ba", "guan deng" and "fou jue". Then the intelligent home environment was simulated by 10 complex speech commands, including "wo shi", "chu fang", "ke ting", "dian shi" and so on. The results shows the small intelligent home system has been achieved.
On Speech Recognition Algorithms

  Abstract —We use speech recognition algorithms daily with our phones, computers, home assistants, and more. Each of these systems use algorithms to convert the sound waves into useful data for processing which is [then interpreted by the machine. Some of these machines use older algorithms while the newer systems use neural networks to interpret this data. These systems then produce an output generated in the form of text to be used. A large amount of training data is needed to make these algorithms and neural networks function effectively.
University of Birmingham Pattern of DNA methylation in Daphnia

DNA methylation is an evolutionary ancient epigenetic modification that is phylogenetically widespread. Comparative studies of the methylome across a diverse range of non-conventional and conventional model organisms is expected to help reveal how the landscape of DNA methylation and its functions have evolved. Here, we explore the DNA methylation profile of two species of the crustacean Daphnia using whole genome bisulfite sequencing. We then compare our data with the methylomes of two insects and two mammals to achieve a better understanding of the function of DNA methylation in Daphnia. Using RNA-sequencing data for all six species, we investigate the correlation between DNA methylation and gene expression. DNA methylation in Daphnia is mainly enriched within the coding regions of genes, with the highest methylation levels observed at exons 2–4. In contrast, vertebrate genomes are globally methylated, and increase towards the highest methylation levels observed at exon 2, and maintained across the rest of the gene body. Although DNA methylation patterns differ among all species, their methylation profiles share a bimodal distribution across the genomes. Genes with low levels of CpG methylation and gene expression are mainly enriched for species specific genes. In contrast, genes associated with high methylated CpG sites are highly transcribed and evolutionary conserved across all species. Finally, the positive correlation between internal exons and gene expression potentially points to an evolutionary conserved mechanism, whereas the negative regulation of gene expression via methylation of promoters and exon 1 is potentially a secondary mechanism that has been evolved in vertebrates.
Silver Tape: Inkjet-Printed Circuits Peeled-and-Transferred on Versatile Substrates

We propose Silver Tape, a simple yet novel fabrication technique to transfer inkjet-printed silver traces from paper onto versatile substrates, without time-/space-consuming processes such as screen printing or heat sintering. This allows users to quickly implement silver traces with a variety of properties by exploiting a wide range of substrates. For instance, high flexibility can be achieved with Scotch tape, high transparency with polydimethylsiloxane (PDMS), heat durability with Kapton polyimide tape, water solubility with 3M water-soluble tape, and beyond. Many of these properties are not achievable with conventional substrates that are used for inkjet-printing conductive traces. Specifically, our technique leverages the commonly undesired low adhesion property of the inkjet printing films and repurposes these films as temporary transfer media. We describe our fabrication methods with a library of materials we can utilize, evaluate the mechanical and electrical properties of the transferred traces, and conclude with several demonstrative applications. We believe Silver Tape enriches novel interactions for the ubiquitous computing domain, by enabling digital fabrication of electronics on versatile materials, surfaces, and shapes. CCS
Crossed-beam laser spectroscopy of atomic ruthenium

High resolution crossed-beam laser spectroscopy has been used to measure the isotope shifts and hyperfine parameters of nineteen transitions in atomic ruthenium. These results have been used in conjunction with four other existing transition measurements to determine accurate values for the change in mean-square charge radius between the isotopes of ruthenium. The new charge radii measurements exhibit up to an order of magnitude improvement in accuracy compared to the previously published results. These accurate charge radii systematics in ruthenium provide additional data for the interesting N=60 region of the nuclear chart. The transitions measured have been assessed in terms of their suitability for use in future collinear laser spectroscopy measurements of radioactive ruthenium isotopes. One transition in particular, the 349.8942nm Ocm-1 to 28571.890cm-1 transition, has the potential to be highly efficient.
Topology brought to the coast

Since the pioneering work of Kelvin on Laplace tidal equations, a zoology of trapped waves have been found in the context of coastal dynamics. Among them, the one originally computed by Kelvin plays a particular role, as it is an unidirectional mode filling a frequency gap between different bands of delocalized bulk modes. The existence of such Kelvin waves is robust to changes in the boundary shape and in changes of the underlying model for the coast. This suggests a topological interpretation that has yet up to now remained elusive. Here we rectify the situation, by taking advantage of a reformulation of the shallow water dynamics that highlights the interplay between the Coriolis parameter that breaks time-reversal symmetry and a new parameter describing the gradient of bottom topography that breaks inversion symmetry. This leads to a fluid analogue of the celebrated Haldane model in condensed matter physics that is based on the same symmetries breaking, and allows for a new classification of coastal waves based on a correspondence between degeneracy points for bulk waves and a spectral flow for interface problems with varying topography.
Extending Kolmogorov’s Axioms for a Generalized Probability Theory on Collections of Contexts

Kolmogorov’s axioms of probability theory are extended to conditional probabilities among distinct (and sometimes intertwining) contexts. Formally, this amounts to row stochastic matrices whose entries characterize the conditional probability to find some observable (postselection) in one context, given an observable (preselection) in another context. As the respective probabilities need not (but, depending on the physical/model realization, can) be of the Born rule type, this generalizes approaches to quantum probabilities by Aufféves and Grangier, which in turn are inspired by Gleason’s theorem.
Evaluating the Performance of Twitter-based Exploit Detectors

Patch prioritization is a crucial aspect of information systems security, and knowledge of which vulnerabilities were exploited in the wild is a powerful tool to help systems administrators accomplish this task. The analysis of social media for this specific application can enhance the results and bring more agility by collecting data from online discussions and applying machine learning techniques to detect real-world exploits. In this paper, we use a technique that combines Twitter data with public database information to classify vulnerabilities as exploited or not-exploited. We analyze the behavior of different classifying algorithms, investigate the influence of different antivirus data as ground truth, and experiment with various time window sizes. Our findings suggest that using a Light Gradient Boosting Machine (LightGBM) can benefit the results, and for most cases, the statistics related to a tweet and the users who tweeted are more meaningful than the text tweeted. We also demonstrate the importance of using ground-truth data from security companies not mentioned in previous works.
Ellipse Loss for Scene-Compliant Motion Prediction

Motion prediction is a critical part of self-driving technology, responsible for inferring future behavior of traffic actors in autonomous vehicle’s surroundings. In order to ensure safe and efficient operations, prediction models need to output accurate trajectories that obey the map constraints. In this paper, we address this task and propose a novel ellipse loss that allows the models to better reason about scene compliance and predict more realistic trajectories. Ellipse loss penalizes off-road predictions directly in a supervised manner, by projecting the output trajectories into the top-down map frame using a differentiable trajectory rasterizer module. Moreover, it takes into account actor dimensions and orientation, providing more direct training signals to the model. We applied ellipse loss to a recently proposed state-of-the-art joint detection-prediction model to showcase its benefits. Evaluation on large-scale autonomous driving data strongly indicates that the method allows for more accurate and more realistic trajectory predictions.
OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still Rocks Semantic Change Detection

We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian. We exploit one of the earliest and most influential semantic change detection models based on Skip-Gram with Negative Sampling, Orthogonal Procrustes alignment and Cosine Distance and obtain the winning submission of the shared task with near to perfect accuracy . 94. Our results once more indicate that, within the present task setup in lexical semantic change detection, the traditional type-based approaches yield excellent performance.
Learning Multiple‐Scattering Solutions for Sphere‐Tracing of Volumetric Subsurface Effects

Accurate subsurface scattering solutions require the integration of optical material properties along many complicated light paths. We present a method that learns a simple geometric approximation of random paths in a homogeneous volume with translucent material. The generated representation allows determining the absorption along the path as well as a direct lighting contribution, which is representative of all scatter events along the path. A sequence of conditional variational auto‐encoders (CVAEs) is trained to model the statistical distribution of the photon paths inside a spherical region in the presence of multiple scattering events. A first CVAE learns how to sample the number of scatter events, occurring on a ray path inside the sphere, which effectively determines the probability of this ray to be absorbed. Conditioned on this, a second model predicts the exit position and direction of the light particle. Finally, a third model generates a representative sample of photon position and direction along the path, which is used to approximate the contribution of direct illumination due to in‐scattering. To accelerate the tracing of the light path through the volumetric medium toward the solid boundary, we employ a sphere‐tracing strategy that considers the light absorption and can perform a statistically accurate next‐event estimation. We demonstrate efficient learning using shallow networks of only three layers and no more than 16 nodes. In combination with a GPU shader that evaluates the CVAEs’ predictions, performance gains can be demonstrated for a variety of different scenarios. We analyze the approximation error that is introduced by the data‐driven scattering simulation and shed light on the major sources of error.
Complex Query Answering with Neural Link Predictors

Neural link predictors are useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries containing logical conjunctions (∧), disjunctions (∨), and existential quantifiers (∃). We propose a framework for efficiently answering complex queries on in- complete Knowledge Graphs. We translate each query into an end-to-end differentiable objective, where the truth value of each atom is computed by a pre-trained neural link predictor. We then analyse two solutions to the optimisation problem, including gradient-based and combinatorial search. In our experiments, the proposed approach produces more accurate results than state-of-the-art methods — black-box models trained on millions of generated queries — without the need for training on a large and diverse set of complex queries. Using orders of magnitude less training data, we obtain relative improvements ranging from 8% up to 40% in Hits@3 across multiple knowledge graphs. We find that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms. All our source code and datasets are available online (https://github.com/uclnlp/cqd).
Automatic differentiation in PCF

We study the correctness of automatic differentiation (AD) in the context of a higher-order, Turing-complete language (PCF with real numbers), both in forward and reverse mode. Our main result is that, under mild hypotheses on the primitive functions included in the language, AD is almost everywhere correct, that is, it computes the derivative or gradient of the program under consideration except for a set of Lebesgue measure zero. Stated otherwise, there are inputs on which AD is incorrect, but the probability of randomly choosing one such input is zero. Our result is in fact more precise, in that the set of failure points admits a more explicit description: for example, in case the primitive functions are just constants, addition and multiplication, the set of points where AD fails is contained in a countable union of zero sets of polynomials.
Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph

The third version of the open-domain dialogue system Alquist developed within the Alexa Prize 2020 competition is designed to conduct coherent and engaging conversations on popular topics. The main novel contribution is the introduction of a system leveraging an innovative approach based on a conversational knowledge graph and adjacency pairs. The conversational knowledge graph allows the system to utilize knowledge expressed during the dialogue in consequent turns and across conversations. Dialogue adjacency pairs divide the conversation into small conversational structures, which can be combined and allow the system to react to a wide range of user inputs flexibly. We discuss and describe Alquist's pipeline, data acquisition and processing, dialogue manager, NLG, knowledge aggregation, and a hierarchy of adjacency pairs. We present the experimental results of the individual parts of the system.
Diversity patterns of native and exotic fish species suggest homogenization processes, but partly fail to highlight extinction threats

Exotic species are a major threat to biodiversity and have modified native communities worldwide. Invasion processes have been extensively studied, but studies on species richness and beta diversity patterns of exotic and native species are rare. We investigate such patterns among exotic and native fish communities in upland and lowland rivers to explore their relationship with environmental drivers.