Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of chronic stress and L-carnitine on rat stomach.

BACKGROUND AND AIM L-Carnitine is an essential cofactor in the mitochondrial transfer of fatty acids, and it is also a scavenger of free radicals in mammalian tissues. The aim of the study was to determine the effect of L-carnitine on chronic restraint stress-induced gastric mucosal injury. METHODS Wistar rats were applied restraint stress (1 h/day) and L-carnitine (50 mg/kg) for 21 days. The lesion index, prostaglandin E(2) and mucus content, lipid peroxidation, superoxide dismutase, and catalase activity in gastric mucosa were evaluated. RESULTS Chronic restraint stress increased the lesion index, lipid peroxidation, and superoxide dismutase activity in gastric mucosa, and it decreased prostaglandin E(2) and mucus content. L-Carnitine treatment prevented the stress-induced increase in lesion index, lipid peroxidation and a stress-induced decline in prostaglandin E(2), and mucus content in gastric mucosa, but it increased catalase activity. CONCLUSIONS L-Carnitine prevents the occurrence of lesion by strengthening the gastric mucosal barrier and by reducing lipid peroxidation against the harmful effects of chronic restraint stress.
Euclidean vacuum mode functions for a scalar field on open de Sitter space.

Motivated by recent studies of the one-bubble inflationary universe scenario that predicts a low density, negative curvature universe, we investigate the Euclidean vacuum mode functions of a scalar field in a spatially open chart of de Sitter space which is foliated by hyperbolic time slices. When we consider the possibility of an open inflationary universe, we are faced with the problem of the initial condition for the quantum fluctuations of the inflaton field, because the inflationary era should not last too long to lose all information of the initial condition. In the one-bubble scenario, in which an open universe is created in an exponentially expanding false vacuum universe triggered by quantum decay of a false vacuum, it seems natural that the initial state is the de Sitter\char21{}invariant Euclidean vacuum. Here we present explicit expressions for the Euclidean vacuum mode functions in the open chart for a scalar field with arbitrary mass and curvature coupling.
Maximin-Aware Allocations of Indivisible Goods

We study envy-free allocations of indivisible goods to agents in settings where each agent is unaware of the goods allocated to other agents. In particular, we propose the maximin aware (MMA) fairness measure, which guarantees that every agent, given the bundle allocated to her, is aware that she does not envy at least one other agent, even if she does not know how the other goods are distributed among other agents. We also introduce two of its relaxations, and discuss their egalitarian guarantee and existence. Finally, we present a polynomial-time algorithm, which computes an allocation that approximately satisfies MMA or its relaxations. Interestingly, the returned allocation is also 1/2-approximate EFX when all agents have sub- additive valuations, which improves the algorithm in [Plaut and Roughgarden, 2018].
Statistical embedding for directed graphs

We propose a novel statistical node embedding of directed graphs, which is based on a global minimization of pairwise relative entropy and graph geodesics in a non-linear way. Each node is encoded with a probability density function over a measurable real n-dimensional space. Furthermore, we analyze the connection to the geometrical properties of such embedding and characterize the curvature of the statistical manifolds. Extensive experiments show that our proposed embedding is better preserving the global geodesic information of graphs, as well as outperforming existing embedding models on directed graphs in a variety of evaluation metrics, in an unsupervised setting.
Saliency detection based on structural dissimilarity induced by image quality assessment model

Abstract. The distinctiveness of image regions is widely used as the cue of saliency. Generally, the distinctiveness is computed according to the absolute difference of features. However, according to the image quality assessment (IQA) studies, the human visual system is highly sensitive to structural changes rather than absolute difference. Accordingly, we propose the computation of the structural dissimilarity between image patches as the distinctiveness measure for saliency detection. Similar to IQA models, the structural dissimilarity is computed based on the correlation of the structural features. The global structural dissimilarity of a patch to all the other patches represents saliency of the patch. We adopt two widely used structural features, namely the local contrast and gradient magnitude, into the structural dissimilarity computation in the proposed model. Without any postprocessing, the proposed model based on the correlation of either of the two structural features outperforms 11 state-of-the-art saliency models on three saliency databases.
EARNING A LGORITHMS FOR A CTIVE L EARNING

We present a model that learns active learning algorithms via metalearning. For each metatask, our model jointly learns: a data representation, an item selection heuristic, and a one-shot classifier. Our model uses the item selection heuristic to construct a labeled support set for the one-shot classifier. Using metatasks based on the Omniglot and MovieLens datasets, we show that our model performs well in synthetic and practical settings.
Affirmative action policies for top-k candidates selection: with an application to the design of policies for university admissions

We consider the problem of designing affirmative action policies for selecting the top-k candidates from a pool of applicants. We assume that for each candidate we have socio-demographic attributes and a series of variables that serve as indicators of future performance (e.g., results on standardized tests) - as well as historical data including the actual performance of previously selected candidates. We consider the case where an organization wishes to increase the selection of people from disadvantaged socio-demographic groups. Hence, we seek to design an affirmative action policy to select candidates who are more likely to perform well, but in a way that increases the representation of disadvantaged groups. Our motivating application is the design of university admission policies to bachelor's degrees. We use a causal framework to describe several families of policies (changing component weights, giving bonuses, and enacting quotas), and compare them both theoretically and through extensive experimentation on a real-world dataset containing thousands of university applicants. Our empirical results indicate that simple policies could favor the admission of disadvantaged groups without significantly compromising on the quality of accepted candidates.
Sliced Gromov-Wasserstein

Recently used in various machine learning contexts, the Gromov-Wasserstein distance (GW) allows for comparing distributions whose supports do not necessarily lie in the same metric space. However, this Optimal Transport (OT) distance requires solving a complex non convex quadratic program which is most of the time very costly both in time and memory. Contrary to GW, the Wasserstein distance (W) enjoys several properties ({\em e.g.} duality) that permit large scale optimization. Among those, the solution of W on the real line, that only requires sorting discrete samples in 1D, allows defining the Sliced Wasserstein (SW) distance. This paper proposes a new divergence based on GW akin to SW. We first derive a closed form for GW when dealing with 1D distributions, based on a new result for the related quadratic assignment problem. We then define a novel OT discrepancy that can deal with large scale distributions via a slicing approach and we show how it relates to the GW distance while being $O(n\log(n))$ to compute. We illustrate the behavior of this so called Sliced Gromov-Wasserstein (SGW) discrepancy in experiments where we demonstrate its ability to tackle similar problems as GW while being several order of magnitudes faster to compute.
An Integrated Model for User Innovation Knowledge Based on Super-Network

Purpose: This article aims to provide an integrated model and methodology for discovering valuable innovation knowledge and creative users in online innovative communities. Design/methodology/approach: The super-network integration approach is applied in constructing the user's innovation knowledge super-network (UIKSN) model based on knowledge fragments discovered from user-generated content (UGC) data using text mining methods. The social network analysis (SNA) methodology is then used to analyze the KSN model. An empirical research is conducted with China's Xiaomi online community to illustrate the UIKSN model and the SNA methodology. Findings: Compared with current methods, the proposed KSN model and SNA methodology are demonstrated to be more effective and valid in discovering valuable innovation knowledge including valuable innovations, innovation trends, creative users and their knowledge background, etc. Research limitations/implications: The results of KSN modeling and analysis are inevitably influenced by the performance of text mining methods. Practical implications: More and more companies start to adopt online community user's innovations in their new product developments. However, it is difficult to discover users’ opinions and innovations from the UGC due to its tremendous volume. Therefore, the KSN model and SNA methodology presented in this article are helpful for companies to manage online communities and to utilize users’ innovations effectively and efficiently. Originality/value: The article provides two main contributions in the study of online communities: 1) a well-justified model (users' innovation KSN, UIKSN) to explore online users’ contributions; 2) a new and more effective methodology to discover and analyze valuable innovations and innovative users.
Implications of a transition in the dark energy equation of state for the H0 and σ8 tensions

We explore the implications of a rapid appearance of dark energy between the redshifts (z) of one and two on the expansion rate and growth of perturbations. Using both Gaussian process regression and a parametric model, we show that this is the preferred solution to the current set of low-redshift (z<3) distance measurements if H0=73 km s−1 Mpc−1 to within 1% and the high-redshift expansion history is unchanged from the ΛCDM inference by the Planck satellite. Dark energy was effectively non-existent around z=2, but its density is close to the ΛCDM model value today, with an equation of state greater than −1 at z<0.5. If sources of clustering other than matter are negligible, we show that this expansion history leads to slower growth of perturbations at z<1, compared to ΛCDM, that is measurable by upcoming surveys and can alleviate the σ8 tension between the Planck CMB temperature and low-redshift probes of the large-scale structure.
Universal bounds and monotonicity properties of ratios of Hermite and parabolic cylinder functions

We obtain so far unproved properties of a ratio involving a class of Hermite and parabolic cylinder functions. Those ratios are shown to be strictly decreasing and bounded by universal constants. Differently from the usual analytic approaches, we employ simple purely probabilistic arguments to derive our results. In particular, we exploit the relation between Hermite and parabolic cylinder functions and the eigenfunctions of the infinitesimal generator of the Ornstein-Uhlenbeck process. As a byproduct, we obtain Turán-type inequalities.
In-DRAM Bulk Bitwise Execution Engine

Many applications heavily use bitwise operations on large bitvectors as part of their computation. In existing systems, performing such bulk bitwise operations requires the processor to transfer a large amount of data on the memory channel, thereby consuming high latency, memory bandwidth, and energy. In this paper, we describe Ambit, a recently-proposed mechanism to perform bulk bitwise operations completely inside main memory. Ambit exploits the internal organization and analog operation of DRAM-based memory to achieve low cost, high performance, and low energy. Ambit exposes a new bulk bitwise execution model to the host processor. Evaluations show that Ambit significantly improves the performance of several applications that use bulk bitwise operations, including databases.
Delay Management with Rerouting of Passengers

The question of delay management (DM) is whether trains should wait for a delayed feeder train or should depart on time. In classical DM models, passengers are assumed to take their originally planned routes. After the wait-depart decisions are made, passengers will certainly change to the best-possible route according to these decisions. In this paper, we propose a model where such a rerouting of passengers is incorporated in the DM process. To describe the problem, we represent it as an event-activity network similar to the one used in classical DM, with some additional events to incorporate origin and destination of the passengers. We present an integer programming formulation of this problem. Furthermore, we discuss the variant in which we assume fixed costs for maintaining connections, and we present a polynomial algorithm for the special case of only one origin-destination pair that we later use to derive a strong lower bound for the integer program. Finally, computational experiments based on real-world data from Netherlands Railways show that significant improvements with respect to the passengers' traveling times can be obtained by taking the rerouting of passengers into account in the model.
Measurement of carnitine in hemodialysis patients – adaptation of an enzymatic photometric method for an automatic analyzer

Abstract Background: The main goal of this work was to describe the analytical characteristics of an enzymatic photometric test for carnitine determination and its automation using an Olympus analyzer. Methods: We used a test from Roche intended for manual processing and tried to apply it for use on an Olympus AU 400 analyzer. The analytical parameters of our modified technique were determined using external quality controls and kit controls, and by measurements in venous blood samples from 85 chronically hemodialyzed patients (before and after hemodialysis) and from 68 healthy blood donors serving as controls. Results: A reference value for free carnitine was estimated parametrically as 40.1±17. 8μmol/L. The mean bias for eight control measurements was 5.1%. Sensitivity was calculated as the limit of quantification at 2. 6μmol/L. The intra-assay coefficient of variation was 2.4%. The inter-assay coefficient of variation was 8.3%. Analytical recovery was 101.8%, 99.5% and 95.4%. Conclusions: The main advantages of our automated method in comparison to the original manual method are the smaller amounts of samples, reagents and diluents required and the shorter analysis time. As hemodialysis patients often suffer from carnitine deficiency, we conclude that its determination may be helpful for diagnostic verification. Clin Chem Lab Med 2006;44:983–6.
Adaptive Symmetric Reward Noising for Reinforcement Learning

Recent reinforcement learning algorithms, though achieving impressive results in various fields, suffer from brittle training effects such as regression in results and high sensitivity to initialization and parameters. We claim that some of the brittleness stems from variance differences, i.e. when different environment areas - states and/or actions - have different rewards variance. This causes two problems: First, the "Boring Areas Trap" in algorithms such as Q-learning, where moving between areas depends on the current area variance, and getting out of a boring area is hard due to its low variance. Second, the "Manipulative Consultant" problem, when value-estimation functions used in DQN and Actor-Critic algorithms influence the agent to prefer boring areas, regardless of the mean rewards return, as they maximize estimation precision rather than rewards. This sheds a new light on how exploration contribute to training, as it helps with both challenges. Cognitive experiments in humans showed that noised reward signals may paradoxically improve performance. We explain this using the two mentioned problems, claiming that both humans and algorithms may share similar challenges. Inspired by this result, we propose the Adaptive Symmetric Reward Noising (ASRN), by which we mean adding Gaussian noise to rewards according to their states' estimated variance, thus avoiding the two problems while not affecting the environment's mean rewards behavior. We conduct our experiments in a Multi Armed Bandit problem with variance differences. We demonstrate that a Q-learning algorithm shows the brittleness effect in this problem, and that the ASRN scheme can dramatically improve the results. We show that ASRN helps a DQN algorithm training process reach better results in an end to end autonomous driving task using the AirSim driving simulator.
Ectopic expression of AP-2alpha transcription factor in the lens disrupts fiber cell differentiation.

AP-2alpha is a developmentally important transcription factor which has been implicated in the regulation of cell growth, programmed cell death, and differentiation. To investigate the specific function of AP-2alpha in differentiation of the lens, AP-2alpha was expressed in the differentiating lens fiber cells under control of the alphaA-crystallin promoter. Normally, AP-2alpha is selectively expressed in lens epithelial cells and expression terminates at the lens equator, where epithelial cells terminally differentiate into fiber cells. Ectopic expression of the AP-2alpha gene in the fiber cell compartment resulted in bilateral cataracts and microphthalmia in mice by 2 weeks of age. Histological evaluation of embryonic and adult transgenic lenses revealed a significant reduction in lens size and anterior shifting of the transitional zone. Two aspects of fiber cell differentiation were also blocked, including the migration of newly formed fiber cells and an inhibition in fiber cell denucleation. Correlated with these defects were expanded expression of E-cadherin in the lens transitional zone and reduced expression of the fiber cell-specific protein MIP (major intrinsic protein). Together, these data demonstrate that AP-2alpha acts as a negative regulator of terminal fiber cell differentiation through the regulation of genes involved in cell adhesion and migration.
Learning Choice Functions: Concepts and Architectures

We study the problem of learning choice functions, which play an important role in various domains of application, most notably in the field of economics. Formally, a choice function is a mapping from sets to sets: Given a set of choice alternatives as input, a choice function identifies a subset of most preferred elements. Learning choice functions from suitable training data comes with a number of challenges. For example, the sets provided as input and the subsets produced as output can be of any size. Moreover, since the order in which alternatives are presented is irrelevant, a choice function should be symmetric. Perhaps most importantly, choice functions are naturally context-dependent, in the sense that the preference in favor of an alternative may depend on what other options are available. We formalize the problem of learning choice functions and present two general approaches based on two representations of context-dependent utility functions. Both approaches are instantiated by means of appropriate neural network architectures, and their performance is demonstrated on suitable benchmark tasks.
Hypothesis Testing Interpretations and Renyi Differential Privacy

Differential privacy is a de facto standard in data privacy, with applications in the public and private sectors. A way to explain differential privacy, which is particularly appealing to statistician and social scientists is by means of its statistical hypothesis testing interpretation. Informally, one cannot effectively test whether a specific individual has contributed her data by observing the output of a private mechanism---any test cannot have both high significance and high power. In this paper, we identify some conditions under which a privacy definition given in terms of a statistical divergence satisfies a similar interpretation. These conditions are useful to analyze the distinguishability power of divergences and we use them to study the hypothesis testing interpretation of some relaxations of differential privacy based on Renyi divergence. This analysis also results in an improved conversion rule between these definitions and differential privacy.
Dirac Delta Regression: Conditional Density Estimation with Clinical Trials

Personalized medicine seeks to identify the causal effect of treatment for a particular patient as opposed to a clinical population at large. Most investigators estimate such personalized treatment effects by regressing the outcome of a randomized clinical trial (RCT) on patient covariates. The realized value of the outcome may however lie far from the conditional expectation. We therefore introduce a method called Dirac Delta Regression (DDR) that estimates the entire conditional density from RCT data in order to visualize the probabilities across all possible treatment outcomes. DDR transforms the outcome into a set of asymptotically Dirac delta distributions and then estimates the density using non-linear regression. The algorithm can identify significant patient-specific treatment effects even when no population level effect exists. Moreover, DDR outperforms state-of-the-art algorithms in conditional density estimation on average regardless of the need for causal inference.
Increase in overall mortality risk in patients with type 2 diabetes receiving glipizide, glyburide or glimepiride monotherapy versus metformin: a retrospective analysis

Aims: It remains uncertain if differences in mortality risk exist among the sulfonylureas, especially in patients with documented coronary artery disease (CAD). The purpose of this study was to assess the overall mortality risk of the individual sulfonylureas versus metformin in a large cohort of patients with type 2 diabetes.
Tissue segmentation with deep 3D networks and spatial priors

Conventional automated segmentation of the human head distinguishes different tissues based on image intensities in an MRI volume and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in the presence of unexpected lesions. Deep convolutional neural networks leverage instead volumetric spatial patterns and can be trained to segment lesions, but have thus far not integrated prior probabilities. Here we add to a three-dimensional convolutional network spatial priors with a TPM, morphological priors with conditional random fields, and context with a wider field-of-view at lower resolution. The new architecture, which we call MultiPrior, was designed to be a fully-trainable, three-dimensional convolutional network. Thus, the resulting architecture represents a neural network with learnable spatial memories. When trained on a set of stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation. The approach is further demonstrated on patients with disorders of consciousness, where we find that cognitive state correlates positively with gray-matter volumes and negatively with the extent of ventricles. We make the code and trained networks freely available to support future clinical research projects.