Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends in Anatomy and Physiology Research

Introduction and objectives: The torrent guasp hypothesis considers that the myocardium consists of a continuous muscular band beginning at the level of the pulmonary valve and ending at the level of the aortic root limiting both ventricular chambers. The anatomy investigated in this work provides the interpretation for fundamental aspects of left ventricular dynamics. Methods Ten hearts of young bovine animals, a ten-year-old human heart obtained from a heart transplant and a heart that corresponds to a human fetus of 23 weeks of gestation were used. After a preparatory action proceeded to the comprehensive examination of its architecture, subsequently the muscle band was unrolled in the helix that conforms it, being the last step the dissection of its muscular bundles. Results The muscle band describes two spiral turns with insertion of one of its ends in the path that extends from the pulmonary artery to the orifice of the tricuspid in the so-called lung-tricuspid cord, in front of the aorta; while the other is in the aortic root, in the right and left trigones. This arrangement of the ventricular myocardial band confers ventricular chambers a leading role in cardiac function. The myocardial band is constituted by two so-called basal loop (right and left segments) and apical loop (descending and ascending segments). In all the examined hearts, at the anteroinferior level of the right trigone, a bone core of was found facing downwards to meet the ascending segment muscle fibers of the myocardial band that are inserted on its surface. The microscopic analysis revealed in the hearts bovine and human a trabecular osteochondral matrix. We have called it cardiac fulcrum. Conclusions In summary the myocardium consists of three regions: 1) free wall of the right ventricle; 2) free wall of the left ventricle and 3) interventricular septum. The wall of the right ventricle is formed by the right segment of the basal loop whereas that of the left is formed by both the basal loop (left segment) and the descending and ascending segments of the apical loop. The interventricular septum consists of a ventral and a dorsal part. The first portion consists of the left descending segment, the intraseptal band (the final segment of the muscular band) and the anterior septal band. The first two belong to the left ventricle and to the right ventricle the remainder. The posterior region of the septum is composed of the left descending segment (left ventricle dependence) and the posterior septum, corresponding to the right ventricle. The spatial muscle structure adopted by the myocardial muscle band has a double function: a) to limit the ventricular chambers; b) fulfill the suction and ejection action in its role of cardiac pump. The anatomical description of cardiac fulcrum would put an end to the issue of the lack of support of the muscle band to perform its rotational function, as in the cardiac osteo-chondral fulcrum, it finds its insertion point to achieve the necessary lever just as a muscle does with its bone insertion.
Cognitive Control in Schizophrenia Teal S. Eich Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences

Cognitive Control in Schizophrenia Teal S. Eich Schizophrenia is the ninth leading cause of disability worldwide (e.g., Lopez et al., 2006), and is a devastating psychiatric illness. Although diagnosis is made based upon the occurrence of positive and negative symptoms (First, Spitzer, Gibbon & Williams, 1995), it is the cognitive symptoms that are most strongly associated with functional outcome (Green, 1996 ). Cognitive control, including the ability to appropriately update relevant information and resist interference from irrelevant information, is critical for flexible and adaptive goal-directed behavior, and is among the most frequently noted of the cognitive symptoms in schizoprenia (Barch, 2005; Barch & Smith, 2008). Despite this, deficits in cognitive control are unaffected by medications used to treat the clinical symptoms of the disorder (Greene et al, 2008). Understanding both the behavioral and the neural mechanisms that comprise this deficit is thus of paramount importance. Although deficits in cognitive control in schizoprenia have been extensively studied, a number of questions still remain. Here, I ask two main questions: First, is cognitive control impaired globally, or are only certain aspects of cognitive control impaired in schizophrenia? I found that that there are (at least) two different selection mechanisms, and that people with schizophrenia are impaired in only one of these: dysregulation in left posterior ventrolateral prefrontal cortex correlates with impaired behavioral performance on a working memory task, suggesting that deficits in inhibiting irrelevant information from working memory is the crux of the deficit. Second, I asked whether the nature of the information affects cognitive control. I found that people with schizophrenia are able to deploy cognitive control processes more effectively than healthy controls in cases in which salient, emotional information competes with active cognitive goals, suggesting specific underlying deficits in emotional processing.
Morphometric variability of wild honey bees of the mountain forest zone of Crimea as a material for breeding

The results of studies of worker honey bees ( Apis mellifera L.) from two points are presented: from a bee colony captured near the Tankovoye village (this swarm was found by a local resident - an entrepreneur, the owner of the private nursery “Orekhovod – practitioner” I.S. Emirsinov) and bees captured in the vicinity of Sugut-Oba mountain (Burus) from a swarm captured about 25 km to the southeast of Belogorsk and west of the Feodosia height. Measurements of morphometric features were carried out using the method of V.V. Alpatov (1948), the method of F. Ruttner (2006) was used to identify the morphotypes and the width of the hairline of worker bees. The phenotypic homogeneity of bee colonies from the Tankovoye village was revealed by morphotypes and the width of the hairline on the abdomen of worker bees in the presence of a variety of classical morphometric characters. At the same time, one phenotype characterized by morphotype O (light brown coloration of chitinous integuments on the abdomen) and a narrow hairline f was registered. The presence of this phenotype characterizes the compliance with the breed standards of Apis mellifera caucasica (gray mountain Caucasian), Apis mellifera carpatica (Carpathian) or Apis mellifera carnica (Carniolan), i.e. breeds with a gray abdomen. Molecular genetic analysis of the mtDNA COI site showed the homogeneity of the sample of wild bees captured near the Tankovoye village and their proximity to the Vuchkovskaya line of the Carpathian breed.
Does size and buoyancy affect the long-distance transport of floating debris?

Floating persistent debris, primarily made from plastic, disperses long distances from source areas and accumulates in oceanic gyres. However, biofouling can increase the density of debris items to the point where they sink. Buoyancy is related to item volume, whereas fouling is related to surface area, so small items ( which have high surface area to volume ratios ) should start to sink sooner than large items. Empirical observations off South Africa support this prediction: moving offshore from coastal source areas there is an increase in the size of ﬂ oating debris, an increase in the proportion of highly buoyant items ( e.g. sealed bottles, ﬂ oats and foamed plastics ) , and a decrease in the proportion of thin items such as plastic bags and ﬂ exible packaging which have high surface area to volume ratios. Size-speci ﬁ c sedimentation rates may be one reason for the apparent paucity of small plastic items ﬂ oating in the world ’ s oceans.
Pharmacological Rescue of Ductal CFTR Rescue Pancreatic and Salivary glands Acinar Cells and Tissue Function in Mouse Models of Autoimmune diseases

BACKGROUND & AIMS— Sjögren’s syndrome and autoimmune pancreatitis (AIP) are disorders with decreased function of salivary, lacrimal glands, and the exocrine pancreas. NOD/ ShiLTJ mice and mice transduced with the cytokine BMP6 develop Sjögren’s syndrome and chronic pancreatitis and MRL/Mp mice are models of AIP. CFTR is a ductal Cl − channel essential for ductal fluid and HCO 3− secretion. We used these models to ask: is CFTR expression altered in these diseases, does correction of CFTR correct gland function, and most notably, does correcting ductal function correct acinar function. Methods— We treated the mice models with the CFTR corrector C18 and the potentiator VX770. Glandular, ductal and acinar cells damage, infiltration of immune cells, and function were measured in vivo and in isolated duct/acini. Results— In the disease models, CFTR expression is markedly reduced. The salivary glands and pancreas are inflamed with increased fibrosis and tissue damage. Treatment with VX770 and, in particular C18 restored salivation, rescued CFTR expression and localization, nearly eliminated the inflammation and tissue damage. Transgenic over-expression of CFTR exclusively in the duct had similar effects. Most notably, the markedly reduced acinar cell Ca 2+ signaling, Orai1, IP 3 receptors, AQP5 Conclusions— Our findings reveal that correcting ductal function is sufficient to rescue acinar cell function and suggests that CFTR correctors are strong candidates for the treatment of Sjögren’s syndrome and pancreatitis. cell ER Ca 2+ release, Ca 2+ influx, expression of IP 3 Rs, Orai1, and AQP5 are markedly reduced. Yet, remarkably, restoring ductal CFTR function increased acinar cells AQP5 expression, Ca 2+ signaling, and fluid secretion. Pharmacological rescue of CFTR also restored Ca 2+ signaling and fluid secretion in pancreatic acinar cells of NOD/ShiLTJ and MRL/Mp mice. These findings are the first to show that restoring ductal function is sufficient to restore acinar cell function in exocrine gland diseases and suggests that the FDA approved CFTR correctors are strong candidates for the treatment of Sjögren’s syndrome and pancreatitis.
Using Argumentation to Evaluate Concept Blends in Combinatorial Creativity

This paper motivates the use of computational argumentation for evaluating ‘concept blends’ and other forms of combinatorial creativity. We exemplify our approach in the domain of computer icon design, where icons are understood as creative artefacts generated through concept blending. We present a semiotic system for representing icons, showing how they can be described in terms of interpretations and how they are related by sign patterns. The interpretation of a sign pattern conveys an intended meaning for an icon. This intended meaning is subjective, and depends on the way concept blending for creating the icon is realised. We show how the intended meaning of icons can be discussed in an explicit and social argumentation process modeled as a dialogue game, and show examples of these following the style of Lakatos (1976). In this way, we are able to evaluate concept blends through an open-ended and dynamic discussion in which concept blends can be improved and the reasons behind a speciﬁc evaluation are made explicit. In the closing section, we explore argumentation and the potential roles that can play at different stages of the concept blending process.
THE INFLUENCE OF SECOND LANGUAGE EXPERIENCE IN THE PATTERN OF LAN- GUAGE PROCESSING OF THE BILINGUAL LEXI- CON1

-_, . The present experimental research studies whether Thai-English bilinguals' language experience in their non-native language influences the pattern of language processing of the bilingual lexicon. Two groups of 100 native Thai bilingual speakers with high or low English language experience were asked to perform Stroop Interference Tasks, with the processing of word forms being either Thai or English and the processing in colour naming also being either Thai or English. The results showed that when the processing of word forms was
Imitating Past Successes can be Very Suboptimal

Prior work has proposed a simple strategy for reinforcement learning (RL): label experience with the outcomes achieved in that experience, and then imitate the relabeled experience. These outcome-conditioned imitation learning methods are appealing because of their simplicity, strong performance, and close ties with supervised learning. However, it remains unclear how these methods relate to the standard RL objective, reward maximization. In this paper, we formally relate outcome-conditioned imitation learning to reward maximization, drawing a precise relationship between the learned policy and Q-values and explaining the close connections between these methods and prior EM-based policy search methods. This analysis shows that existing outcome-conditioned imitation learning methods do not necessarily improve the policy, but a simple modification results in a method that does guarantee policy improvement, under some assumptions.
On Outer Bi-Lipschitz Extensions of Linear Johnson-Lindenstrauss Embeddings of Low-Dimensional Submanifolds of RN

Let $\mathcal{M}$ be a compact $d$-dimensional submanifold of $\mathbb{R}^N$ with reach $\tau$ and volume $V_{\mathcal M}$. Fix $\epsilon \in (0,1)$. In this paper we prove that a nonlinear function $f: \mathbb{R}^N \rightarrow \mathbb{R}^{m}$ exists with $m \leq C \left(d / \epsilon^2 \right) \log \left(\frac{\sqrt[d]{V_{\mathcal M}}}{\tau} \right)$ such that $$(1 - \epsilon) \| {\bf x} - {\bf y} \|_2 \leq \left\| f({\bf x}) - f({\bf y}) \right\|_2 \leq (1 + \epsilon) \| {\bf x} - {\bf y} \|_2$$ holds for all ${\bf x} \in \mathcal{M}$ and ${\bf y} \in \mathbb{R}^N$. In effect, $f$ not only serves as a bi-Lipschitz function from $\mathcal{M}$ into $\mathbb{R}^{m}$ with bi-Lipschitz constants close to one, but also approximately preserves all distances from points not in $\mathcal{M}$ to all points in $\mathcal{M}$ in its image. Furthermore, the proof is constructive and yields an algorithm which works well in practice. In particular, it is empirically demonstrated herein that such nonlinear functions allow for more accurate compressive nearest neighbor classification than standard linear Johnson-Lindenstrauss embeddings do in practice.
Inverse Boundary Value and Optimal Control Problems on Graphs: A Neural and Numerical Synthesis

A general setup for deterministic system identification problems on graphs with Dirichlet and Neumann boundary conditions is introduced. When control nodes are available along the boundary, we apply a discretize-then-optimize method to estimate an optimal control. A key piece in the present architecture is our boundary injected message passing neural network. This will produce more accurate predictions that are considerably more stable in proximity of the boundary. Also, a regularization technique based on graphical distance is introduced that helps with stabilizing the predictions at nodes far from the boundary.
Achievements and Lessons Learned From Successful Small Satellite Missions for Space Weather‐Oriented Research

When the first CubeSats were launched nearly two decades ago, few people believed that the miniature satellites would likely prove to be a useful scientific tool. Skeptics abounded. However, the last decade has seen the highly successful implementation of space missions that make creative and innovative use of fast‐advancing CubeSat and small satellite technology to carry out important science experiments and missions. Several projects now have used CubeSats to obtain first‐of‐their‐kind observations and findings that have formed the basis for high‐profile engineering and science publications, thereby establishing without doubt the scientific value and broad utility of CubeSats. In this paper, we describe recent achievements and lessons learned from a representative selection of successful CubeSat missions with a space weather focus. We conclude that these missions were successful in part because their limited resources promoted not only mission focus but also appropriate risk‐taking for comparatively high science return. Quantitative analysis of refereed publications from these CubeSat missions and several larger missions reveals that mission outcome metrics compare favorably when publication number is normalized by mission cost or if expressed as a weighted net scientific impact of all mission publications.
The Different Faces of AI Ethics Across the World: A Principle-Implementation Gap Analysis

Artificial Intelligence (AI) is transforming our daily life with several applications in healthcare, space exploration, banking and finance. These rapid progresses in AI have brought increasing attention to the potential impacts of AI technologies on society, with ethically questionable consequences. In recent years, several ethical principles have been released by governments, national and international organisations. These principles outline high-level precepts to guide the ethical development, deployment, and governance of AI. However, the abstract nature, diversity, and context-dependency of these principles make them difficult to implement and operationalize, resulting in gaps between principles and their execution. Most recent work analysed and summarized existing AI principles and guidelines but they did not provide findings on principle-implementation gaps and how to mitigate them. These findings are particularly important to ensure that AI implementations are aligned with ethical principles and values. In this paper, we provide a contextual and global evaluation of current ethical AI principles for all continents, with the aim to identify potential principle characteristics tailored to specific countries or applicable across countries. Next, we analyze the current level of AI readiness and current implementations of ethical AI principles in different countries, to identify gaps in the implementation of AI principles and their causes. Finally, we propose recommendations to mitigate the principle-implementation gaps.
A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk

We consider the classical multi-armed bandit problem and design simple-to-implement new policies that simultaneously enjoy two properties: worst-case optimality for the expected regret, and safety against heavy-tailed risk for the regret distribution. Recently, [10] showed that information-theoretic optimized bandit policies as well as standard UCB policies suffer from some serious heavy-tailed risk; that is, the probability of incurring a linear regret slowly decays at a polynomial rate of 1 /T , as T (the time horizon) increases. Inspired by their result, we further show that any policy that incurs an instance-dependent O (ln T ) regret must incur a linear regret with probability Ω(poly(1 /T )) and that the heavy-tailed risk actually exists for all “instance-dependent consistent" policies. Next, for the two-armed bandit setting, we provide a simple policy design that (i) has the worst-case optimality for the expected regret at order ˜ O ( √ T ) and (ii) has the worst-case tail probability of incurring a linear regret decay at an exponential rate exp( − Ω( √ T )) . We further prove that this exponential decaying rate of the tail probability is optimal across all policies that have worst-case optimality for the expected regret. Finally, we generalize the policy design and analysis to the general setting with an arbitrary K number of arms. We provide detailed characterization of the tail probability bound for any regret threshold under our policy design. Numerical experiments are conducted to illustrate the theoretical findings. Our results reveal insights on the incompatibility between consistency and light-tailed risk, whereas indicate that worst-case optimality on expected regret and light-tailed risk are compatible.
Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection

Dynamic time warping (DTW) is an effective dissimilarity measure in many time series applications. Despite its popularity, it is prone to noises and outliers, which leads to singularity problem and bias in the measurement. The time complexity of DTW is quadratic to the length of time series, making it inapplicable in real-time applications. In this paper, we propose a novel time series dissimilarity measure named RobustDTW to reduce the effects of noises and outliers. Specifically, the RobustDTW estimates the trend and optimizes the time warp in an alternating manner by utilizing our designed temporal graph trend filtering. To improve efficiency, we propose a multi-level framework that estimates the trend and the warp function at a lower resolution, and then repeatedly refines them at a higher resolution. Based on the proposed RobustDTW, we further extend it to periodicity detection and outlier time series detection. Experiments on real-world datasets demonstrate the superior performance of RobustDTW compared to DTW variants in both outlier time series detection and periodicity detection.
A Vector Representation of Fluid Construction Grammar Using Holographic Reduced Representations

The question of how symbol systems can be instantiated in neural network-like computation is still open. Many technical challenges remain and most proposals do not scale up to realistic examples of symbol processing, for example, language understanding or language production. Here we use a top-down approach. We start from Fluid Construction Grammar, a wellworked out framework for language processing that is compatible with recent insights into Construction Grammar and investigate how we could build a neural compiler that automatically translates grammatical constructions and grammatical processing into neural computations. We proceed in two steps. FCG is translated from symbolic processing to numeric processing using a vector symbolic architecture, and this numeric processing is then translated into neural network computation. Our experiments are still in an early stage but already show promise.
Searching Similarity Measure for Binarized Neural Networks

Being a promising model to be deployed in resource-limited devices, Binarized Neural Networks (BNNs) have drawn extensive attention from both academic and industry. However, comparing to the full-precision deep neural networks (DNNs), BNNs suffer from non-trivial accuracy degradation, limiting its applicability in various domains. This is partially because existing network components, such as the similarity measure, are specially designed for DNNs, and might be sub-optimal for BNNs. In this work, we focus on the key component of BNNs -- the similarity measure, which quantifies the distance between input feature maps and filters, and propose an automatic searching method, based on genetic algorithm, for BNN-tailored similarity measure. Evaluation results on Cifar10 and Cifar100 using ResNet, NIN and VGG show that most of the identified similarty measure can achieve considerable accuracy improvement (up to 3.39%) over the commonly-used cross-correlation approach.
Effects of laser-annealing on fixed-frequency superconducting qubits

As superconducting quantum processors increase in complexity, techniques to overcome constraints on frequency crowding are needed. The recently developed method of laser-annealing provides an effective post-fabrication method to adjust the frequency of superconducting qubits. Here, we present an automated laser-annealing apparatus based on conventional microscopy components and demonstrate preservation of highly coherent transmons. In addition, we perform noise spectroscopy to investigate the change in defect features, in particular, two-level system defects, after laser-annealing. Finally, we present a local heating model as well as demonstrate aging stability for laser-annealing on the wafer scale. Our work constitutes an important step toward both understanding the underlying physical mechanism and scaling up laser-annealing of superconducting qubits.