Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrative analysis of gene expression and phenotype data

The linking genotype to phenotype is the fundamental aim of modern genetics. We focus on study of links between gene expression data and phenotype data through integrative analysis. We propose three approaches. 1) The inherent complexity of phenotypes makes high-throughput phenotype profiling a very difficult and laborious process. We propose a method of automated multi-dimensional profiling which uses gene expression similarity. Large-scale analysis show that our method can provide robust profiling that reveals different phenotypic aspects of samples. This profiling technique is also capable of interpolation and extrapolation beyond the phenotype information given in training data. It can be used in many applications, including facilitating experimental design and detecting confounding factors. 2) Phenotype association analysis problems are complicated by small sample size and high dimensionality. Consequently, phenotype-associated gene subsets obtained from training data are very sensitive to selection of training samples, and the constructed sample phenotype classifiers tend to have poor generalization properties. To eliminate these obstacles, we propose a novel approach that generates sequences of increasingly discriminative gene cluster combinations. Our experiments on both simulated and real datasets show robust and accurate classification performance. 3) Many complex phenotypes, such as cancer, are the product of not only gene expression, but also gene interaction. We propose an integrative approach to find gene network modules that activate under different phenotype conditions. Using our method, we discovered cancer subtype-specific network modules, as well as the ways in which these modules coordinate. In particular, we detected a breast-cancer specific tumor suppressor network module with a hub gene, PDGFRL, which may play an important role in this module.
Side chain Backbone projections in aromatic and ASX residues from NMR cross-correlated relaxation

The measurements of cross-correlated relaxation rates between H N –N and C b –C c intraresidual and sequential dipolar interactions is demonstrated in ASN, ASP and aromatic residues. The experiment can be used for deuterated samples and no additional knowledge such as Karplus parametrizations is required for the analysis. The data constitutes a new type of information since no other method relates the C b –C c bond to H N –N. Using this method the dominant populations of rotamer states of v 1 can be readily cross checked provided that u or w are known. In addition, dynamics on all timescales can be probed. As opposed to standard dynamics analysis of isolated bonds, the presented observables depend on relative dynamics with an interesting prospect to analyze correlated ﬂuctuations of the two torsion angles u or w with v 1. Experimental rates are compared to single conformer and ensemble representations of GB3 and ubiquitin. In particular, it is found that the recently published ubiquitin ensemble 2k39 improves the agreement obtained for 1UBQ. In general, however, input data restricting ASX and aromatic side chains in structure calculation is sparse highlighting the need for new NMR observables.
5 Climate of the Mediterranean: Synoptic Patterns, Temperature, Precipitation, Winds, and Their Extremes

aIns titu t für Meteorologie, Freie Universität Berlin, Germany, bUniversity of Salento, Lecce, Ita ly and CMCC, Italy, cSchool of Mathematical Sciences, Monash University, Victoria, Australia, d Institute of Geography, University of Augsburg, Augsburg, Germany, In s titu te for Atmospheric Physics, Johannes Gutenberg University, Mainz, Germany, in s titu te of Geography and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland, 9School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UK, hDepartment o f Geography, JustusLiebig University o f Giessen, Giessen, Germany, ’Dipartimento di Fisica, Université degli studi di Milano, Milan, Italy, ^Department of Meteorology and Climatology, Aristotle University of Thessaloniki, Thessaloniki, Greece, kARPA-SIMC, Bologna, Italy, ‘institute for Geophysics and Meteorology, University o f Cologne, Cologne, Germany, m Department of Geography and the Human Environment, Tel Aviv University, Tel Aviv, Israel, "Department of Natural Sciences, The Open University o f Israel, Raanana, Israel
Swarm-Based Multi-Objective Design Optimization of Single-Plate Condenser MEMS Microphone

This work presents a multi-objective optimization of MEMS microphone using the PolyMUMPS fabrication technology. The proposed method handles simultaneous optimization of performance parameters such as sensitivity, lower cut-off frequency, resonance frequency, linearity and noise level. An agglomerative method is being used for multi-objective optimization due to its simplicity. While for efficient exploration of complex search space of MEMS microphone, the traditional particle swarm optimizer has been amended by adaptively adjusting its social and personal best coefficients.
A Novel Multi-swarm Algorithm for Optimization in Dynamic Environments Based on Particle Swarm Optimization

Optimization in dynamic environment is considered amongst prominent optimization problems. There are particular challenges for optimization in dynamic environments, so that the designed algorithms must conquer the challenges in order to perform an efficient optimization. In this paper, a novel optimization algorithm in dynamic environments was proposed based on particle swarm optimization approach, in which several mechanisms were employed to face the challenges in this domain. In this algorithm, an improved multi-swarm approach has been used for finding peaks in the problem space and tracking them after an environment change in an appropriate time. Moreover, a novel method based on change in velocity vector and particle positions was proposed to increase the diversity of swarms. For improving the efficiency of the algorithm, a local search based on adaptive exploiter particle around the best found position as well as a novel AwakeningSleeping mechanism were utilized. The experiments were conducted on Moving Peak Benchmark which is the most well-known benchmark in this domain and results have been compared with those of the state-of-the art methods. The results show the superiority of the
Why Uncertainty Matters - Discounting under Intertemporal Risk Aversion and Ambiguity

eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide. Abstract: Uncertainty has an almost negligible impact on project value in the standardeconomic model. I show that a comprehensive evaluation of uncertainty and uncertainty attitude changes this picture fundamentally. The illustration of this result relies on the discount rate, which is the crucial determinant in balancing immediate costs againstfuture benefits, and the single most important determinant of optimal mitigation policies in the integrated assessment of climate change. First, the paper removes an implicit assumption of (intertemporal or intrinsic) risk neutrality from the standard economic model. Second, the paper introduces aversion to non-risk uncertainty (ambiguity). Ishow a close formal similarity between the model of intertemporal risk aversion, which is a reformulation of the widespread Epstein-Zin-Weil model, and a recent model of smooth ambiguity aversion. I merge the models, achieving a threefold disentanglement between, risk aversion, ambiguity aversion, and the propensity to smooth consumptionover time. Abstract: Uncertainty has an almost negligible impact on project value in the standard economic model. I show that a comprehensive evaluation of uncertainty and uncertainty attitude changes this picture fundamentally. The illustration of this result relies on the discount rate, which is the crucial determinant in balancing immediate costs against future beneﬁts, and the single most important determinant of optimal mitigation policies in the integrated assessment of climate change. First, the paper removes an implicit assumption of (intertemporal or intrinsic) risk neutrality from the standard economic model. Second, the paper introduces aversion to non-risk uncertainty (ambiguity). I show a close formal similarity between the model of intertemporal risk aversion, which is a reformulation of the widespread Epstein-Zin-Weil model, and a recent model of smooth ambiguity aversion. I merge the models, achieving a threefold disentanglement between, risk aversion, ambiguity aversion, and the propensity to smooth consumption over time.
Dynamique sédimentaire et mécanismes de transferts des métaux/métalloïdes dans un écosystème contaminé : la Rade de Toulon

Les ecosystemes cotiers mediterraneens sont soumis a de multiples sources de contamination (activites agricoles, industrielles et touristiques) impactant a des degres divers les compartiments environnementaux (colonne d'eau, sediments, biota, .). En particulier, des etudes precedentes ont demontre que les sediments de la Rade de Toulon presentent un niveau de multi-contamination tres important, qui pourrait avoir des consequences non-negligeables sur l'ecosysteme cotier, ce qui necessite la realisation de differentes etudes complementaires visant a mieux comprendre les processus regissant le transfert potentiel des polluants vers le milieu environnant. Dans ce contexte, les objectifs de cette these consistent a etudier les processus controlant la dynamique des polluants inorganiques dans la colonne sedimentaire et a l'interface eau/sediment et a evaluer leur remobilisation potentielle vers la colonne d'eau. Trois modes principaux de dispersion peuvent conduire a une remobilisation des contaminants: (1) la diagenese precoce mobilisant des elements nutritifs et des elements traces metalliques et metalloides, (2) la remobilisation des contaminants lors de remises en suspension sedimentaire, et (3) le flux diffusif a l'interface eau/sediment en lien avec la mobilite des contaminants dans la colonne sedimentaire. Pour atteindre ses objectifs, ce projet de these combine des approches in-situ (carottage d'interface, extraction de l'eau interstitielle, traitement/analyse des eaux interstitielles et des fractions solides, …), des experiences en laboratoire (suivi cinetique de la remobilisation des contaminants lors des melanges eau/sediment, extractions sequentielles…), des outils de modelisation (calcul de la speciation chimique, simulation de la partition dissous/particulaire, simulation des profils sedimentaires, reactivite de la matiere organique, …) ainsi que le tracage des sources de contamination et de leur transfert entre differents compartiments environnements a l'aide des isotopes stables du Pb. Ces travaux ont ete realises en etroite collaboration avec le LASEM et avec la contribution de differents partenaires (EPOC/Universite de Bordeaux, Ruđer Boskovic Institut/Croatie, CAM/Universite de Lausanne/Suisse, IFREMER, IRSN, …).
Towards Measuring Fairness in Grid Layout in Recommender Systems

There has been significant research in the last five years on ensuring the providers of items in a recommender system are treated fairly, particularly in terms of the exposure the system provides to their work through its results. However, the metrics developed to date have all been designed and tested for linear ranked lists. It is unknown whether and how existing fair ranking metrics for linear layouts can be applied to grid-based displays. Moreover, depending on the device (phone, tab, or laptop) users use to interact with systems, column size is adjusted using column reduction approaches in a grid-view. The visibility or exposure of recommended items in grid layouts varies based on column sizes and column reduction approaches as well. In this paper, we extend existing fair ranking concepts and metrics to study provider-side group fairness in grid layouts, present an analysis of the behavior of these grid adaptations of fair ranking metrics, and study how their behavior changes across different grid ranking layout designs and geometries. We examine how fairness scores change with different ranking layouts to yield insights into (1) the consistency of fair ranking measurements across layouts; (2) whether rankings optimized for fairness in a linear ranking remain fair when the results are displayed in a grid; and (3) the impact of column reduction approaches to support different device geometries on fairness measurement. This work highlights the need to use layout-specific user attention models when measuring fairness of rankings, and provide practitioners with a first set of insights on what to expect when translating existing fair ranking metrics to the grid layouts in wide use today.
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes

The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of machine learning (ML) in the last two decades because of their superior prediction abilities, especially in data-sparse scenarios, and their inherent ability to provide robust uncertainty estimates. Even so, their performance highly depends on intricate customizations of the core methodology, which often leads to dissatisfaction among practitioners when standard setups and off-the-shelf software tools are being deployed. Arguably, the most important building block of a GP is the kernel function, which assumes the role of a covariance operator. Stationary kernels of the Matérn class are used in the vast majority of applied studies; poor prediction performance and unrealistic uncertainty quantification are often the consequences. Non-stationary kernels show improved performance but are rarely used due to their more complicated functional form and the associated effort and expertise needed to define and tune them optimally. In this perspective, we want to help ML practitioners make sense of some of the most common forms of non-stationarity for Gaussian processes. We show a variety of kernels in action using representative datasets, carefully study their properties, and compare their performances. Based on our findings, we propose a new kernel that combines some of the identified advantages of existing kernels.
Asynchronous Perception-Action-Communication with Graph Neural Networks

Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments due to limited sensing and communication capabilities. The robots must execute a Perception-Action-Communication (PAC) loop -- they perceive their local environment, communicate with other robots, and take actions in real time. A fundamental challenge in decentralized PAC systems is to decide what information to communicate with the neighboring robots and how to take actions while utilizing the information shared by the neighbors. Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control. Although conceptually, GNN policies are fully decentralized, the evaluation and deployment of such policies have primarily remained centralized or restrictively decentralized. Furthermore, existing frameworks assume sequential execution of perception and action inference, which is very restrictive in real-world applications. This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication. In particular, we use aggregated GNNs, which enable the exchange of hidden layer information between robots for computational efficiency and decentralized inference of actions. Furthermore, the modules in the framework are asynchronous, allowing robots to perform sensing, extracting information, communication, action inference, and control execution at different frequencies. We demonstrate the effectiveness of GNNs executed in the proposed framework in navigating large robot swarms for collaborative coverage of large environments.
Quantum algorithms for state preparation and data classification based on stabilizer codes

Quantum error correction (QEC) is a way to protect quantum information against noise. It consists of encoding input information into entangled quantum states known as the code space. Furthermore, to classify if the encoded information is corrupted or intact, a step known as syndrome detection is performed. For stabilizer codes, this step consists of measuring a set of stabilizer operators. In this paper, inspired by the QEC approach, and specifically stabilizer codes, we propose a prototype quantum circuit model for classification of classical data. The core quantum circuit can be considered as a \emph{quantum perceptron} where the classification is based on syndrome detection. In this proposal, a quantum perceptron is realized by one stabilizer as part of a stabilizer code, while a quantum neural network (QNN) layer is realized by a stabilizer code which consists of many stabilizers. The concatenation of stabilizer codes results in complex QNNs. The QNN is trained by performing measurements and optimization of a set of parameterized stabilizers. We demonstrate the concept numerically. In this paper we also consider the first challenge to most applications of quantum computers, including data classification, which is to load data into the memory of the quantum computer. This loading amounts to representing the data as a quantum state, i.e., quantum state preparation. An exact amplitude encoding algorithm requires a circuit of exponential depth. We introduce an alternative recursive algorithm which approximates amplitude encoding with only a polynomial number of elementary gates. We name it recursive approximate-scheme algorithm (RASA).
Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies

This paper investigates the issue of an adequate loss function in the optimization of machine learning models used in the forecasting of financial time series for the purpose of algorithmic investment strategies (AIS) construction. We propose the Mean Absolute Directional Loss (MADL) function, solving important problems of classical forecast error functions in extracting information from forecasts to create efficient buy/sell signals in algorithmic investment strategies. Finally, based on the data from two different asset classes (cryptocurrencies: Bitcoin and commodities: Crude Oil), we show that the new loss function enables us to select better hyperparameters for the LSTM model and obtain more efficient investment strategies, with regard to risk-adjusted return metrics on the out-of-sample data.
The Hurst Exponent of Heart Rate Variability in Neonatal Stress, Based on a Mean-Reverting Fractional Lévy Stable Motion

We aim at detecting stress in newborns by observing heart rate variability (HRV). The HRV features nonlinearities. Fractal dynamics is a usual way to model them and the Hurst exponent summarizes the fractal information. In our framework, we have observations of short duration, for which usual estimators of the Hurst exponent, like detrended fluctuation analysis (DFA), are not adapted. Moreover, we observe that the Hurst exponent does not vary much between stress and rest phases, but its decomposition in memory and underlying properties of the probability distribution leads to satisfactory diagnostic tools. This decomposition of the Hurst exponent is in addition embedded in a mean-reverting model. The resulting model is a mean-reverting fractional Lévy stable motion (FLSM). We estimate it and use its parameters as diagnostic tools of neonatal stress. Indeed, the value of the speed of reversion parameter is a significant indicator of stress. The evolution of both parameters in which the Hurst exponent is decomposed provides us with significant indicators as well. On the contrary, the Hurst exponent itself does not bear useful information.
Numerical Study of the Role of Shallow Convection in Moisture Transport and Climate

The objective of this investigation was to study the role of shallow convection on the regional water cycle of the Mississippi and Little Washita Basins of the Southern Great Plains (SGP) using a 3-D mesoscale model, the PSU/NCAR MM5. The underlying premise of the project was that current modeling of regional-scale climate and moisture cycles over the continents is deficient without adequate treatment of shallow convection. At the beginning of the study, it was hypothesized that an improved treatment of the regional water cycle can be achieved by using a 3-D mesoscale numerical model having high-quality parameterizations for the key physical processes controlling the water cycle. These included a detailed land-surface parameterization (the Parameterization for Land-Atmosphere-Cloud Exchange (PLACE) sub-model of Wetzel and Boone), an advanced boundary-layer parameterization (the 1.5-order turbulent kinetic energy (TKE) predictive scheme of Shafran et al.), and a more complete shallow convection parameterization (the hybrid-closure scheme of Deng et al.) than are available in most current models. PLACE is a product of researchers working at NASA's Goddard Space Flight Center in Greenbelt, MD. The TKE and shallow-convection schemes are the result of model development at Penn State. The long-range goal is to develop an integrated suite of physical sub-models that can be used for regional and perhaps global climate studies of the water budget. Therefore, the work plan focused on integrating, improving, and testing these parameterizations in the MM5 and applying them to study water-cycle processes over the SGP. These schemes have been tested extensively through the course of this study and the latter two have been improved significantly as a consequence.
The Guaraní and Their Missions: A Socioeconomic History

The thirty Guarani missions of the Rio de la Plata were the largest and most prosperous of all the Catholic missions established throughout the frontier regions of the Americas to convert, acculturate, and incorporate indigenous peoples and their lands into the Spanish and Portuguese empires. But between 1768 and 1800, the mission population fell by almost half and the economy became insolvent. This unique socioeconomic history provides a coherent and comprehensive explanation for the missions' operation and decline, providing readers with an understanding of the material changes experienced by the Guarani in their day-to-day lives. Although the mission economy funded operations, sustained the population, and influenced daily routines, scholars have not focused on this important aspect of Guarani history, primarily producing studies of religious and cultural change. This book employs mission account books, letters, and other archival materials to trace the Guarani mission work regime and to examine how the Guarani shaped the mission economy. These materials enable the author to poke holes in longheld beliefs about Jesuit mission management and offer original arguments regarding the Bourbon reforms that ultimately made the missions unsustainable.