Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of Women With an Unexpected Low Ovarian Response to Gonadotropin

POSEIDON groups 1 and 2 patients respond poorly (<4 oocytes retrieved) or sub-optimally (4–9 oocytes retrieved) to gonadotropin stimulation despite the presence of adequate ovarian parameters, which negatively affect their cumulative chances of delivering a baby using Assisted Reproductive Technology. A polygenic trait involving gonadotropins and/or their receptors seems to be the primary pathophysiology mechanism explaining this phenomenon. The clinical management is mainly focused on maximizing oocyte yield as to increase the likelihood of having at least one euploid embryo for transfer. Indices such as FORT (follicle output rate) and FOI (follicle-to-oocyte index) may be used to determine if the ovarian reserve was properly explored during a previous ovarian stimulation. Testing for the presence of common polymorphisms affecting gonadotropins and/or their receptors can also be considered to identify patients at risk of hypo-response. An individualized estimation of the minimum number of oocytes needed to obtain at least one euploid embryo can assist counseling and treatment planning. Among currently existing pharmacological interventions, use of recombinant FSH in preference over urinary gonadotropin preparations, FSH dosage increase, and use of rLH supplementation may be considered -alone or combined- for optimally managing POSEIDON's groups 1 and 2 patients. However, given the recent introduction of the POSEIDON criteria, there is still a lack of studies examining the role of interventions specifically to patients classified as groups 1 and 2, thus making it an area for open research.
Changes in Membrane Lipid Metabolism Accompany Pitting in Blueberry During Refrigeration and Subsequent Storage at Room Temperature

Low-temperature storage is the primary postharvest method employed to maintain fruit quality and commercial value. However, pitting can develop during refrigeration, especially during the shelf life. In this study, a membrane lipidomic approach was employed to analyze the potential relationship between pitting and membrane lipid metabolism during post-cold-storage shelf life. We also determined the changes in ultrastructure and water distribution by low-field nuclear magnetic resonance (LF-NMR) and assessed the permeability of membrane, membrane lipid peroxidation, proline and malondialdehyde contents, and the activity and gene expression of phospholipase D and lipoxygenase, which are involved in membrane lipid metabolism. The results indicated that the changes in blueberry phospholipids during storage could be caused by cold stress. Furthermore, dehydration is a manifestation of chilling injury. Finally, the significant increase in electrolyte leakage, content of malondialdehyde and proline, and activity of phospholipase D and lipoxygenase in chilled blueberry also indicated that membrane lipid metabolism plays an important role in cold stress response.
The Effects of Different Stages of Mindfulness Meditation Training on Emotion Regulation

This study examined mood enhancement effects from 4-week focusing attention (FA) meditation and 4-week open monitoring (OM) meditation in an 8-week mindfulness training program designed for ordinary individuals. Forty participants were randomly assigned to a training group or a control group. All participants were asked to perform cognitive tasks and subjective scale tests at three time points (pre-, mid-, and post-tests). Compared with the participants in the control group, the participants in the meditation training group showed significantly decreased anxiety, depression, and rumination scores; significantly increased mindfulness scores; and significantly reduced reaction times (RTs) in the incongruent condition for the Stroop task. The present study demonstrated that 8-week mindfulness meditation training could effectively enhance the level of mindfulness and improve emotional states. Moreover, FA meditation could partially improve individual levels of mindfulness and effectively improve mood, while OM meditation could further improve individual levels of mindfulness and maintain a positive mood.
Peripheral PD-1+ T Cells Co-expressing Inhibitory Receptors Predict SVR With Ultra Short Duration DAA Therapy in HCV Infection

Direct acting antiviral (DAA) regimens of 12 weeks result in HCV clearance in vast majority of patients across genotypes. We previously demonstrated an ultra-short regimen of 4 weeks DAA cleared HCV in a subset of patients. Here, we hypothesized that individual level of antiviral immunity differentially influenced viral clearance and investigated biomarkers of a successful response. Cohorts of HCV patients treated for 4 weeks with DAA therapy who either achieved sustained virologic response (SVR) or relapsed were compared at baseline and at end of therapy (EOT) for immune cell phenotypes and HCV specific immunity. Higher levels of PD-1+ CD8+ and CD4+ T lymphocytes co-expressing inhibitory receptors (IR) were present at baseline and at EOT in HCV patients who eventually achieved SVR compared with those who relpased. HCV specific CD8+ T cells were predominantly contained within these IR expressing PD-1+ subsets. Patients in the SVR group had significantly higher CD8+ T cell degranulation in response to HCV peptides at baseline and higher levels of cytokine producing T cells at EOT time-point, relative to those who relapsed. In ex vivo cultures, PD-1+CD160+ CD8+ T cells had higher HCV specific degranulation and PD-1+2B4+ CD8+ T cells had higher cytokine expression (IFNγ+TNFα+ or IFNγ+CD107a+) compared with single or no IR expressing subsets, indicating higher virus specific functional capacity of these subsets. Receiver operating characteristics curve (ROC) for baseline circulating frequencies of PD-1+CD160+, PD-1+Tim-3+ CD8+ T cells and PD-1+CD160+, PD-1+Blimp-1+, PD-1−CTLA4+ CD4+ T cells respectively, had associated C-statistics of 0.8214 and 0.9451 for discriminatin of patients who successfully cleared HCV with 4 weeks treatment. Thus, PD-1+ virus-specific CD8+ T cell subsets with cytotoxic capacity are present in a subset of chronic HCV infected individuals that associate with ability to achieve SVR, indicating role of immunity in DAA mediated viral clearance with short duration therapy.
Assessing the usage of personalized web information systems

The focus of this thesis is to assess factors which have an influence on the acceptance of personalized Web information systems (WIS). This study is primarily based on the widely used and accepted Technology Acceptance Model (TAM). The qualitative part of the study presents an approach for developing dynamic Web information systems. The development process was based on the work done in the OWLA (Open Web/Wireless Adaptive Hypermedia Applications) research project in 2000–2002. The quantitative part of the study, which includes the main objective of the study, is related to assessment of user acceptance of the given system. The researcher performed a field study in 2003 in which data was collected using a web questionnaire. The field study resulted in a sample size of 209 responses. Moreover we carried out a comparison with the 2002/2003 field study aiming to study possible differences in respondents' attitude toward personalization. The previous field study was carried out during the OWLA research project. The responses were analyzed using SPSS and AMOS. Results of the quantitative part of the study show that the respondents accept personalization but they like to and want to adapt and personalize the content themselves. This was one of the most important findings of the study. Respondents do not want the system to provide content automatically; they want to control the content themselves. The results of the 2003 field study indicate that respondents do not consider layout adaptation, whether automatic or manual, to be important or desirable. The formulated model reveals that perceived relevance is one of the most important aspects that have an effect on attitude toward using and intention to use the given IS. Perceived expectancy and intention to use the given WIS are significant antecedents of actual use. Perceived behavioral control and normative beliefs also have a significant effect on intention to use portal. Enjoyment has a significant effect on attitude. Overall, we have identified in this study some significant factors which will provide useful information to IS practitioners studying the voluntary adoption of specific personalized systems. The results will also provide useful information for systems designers and will contribute towards assessing possible individual barriers to the use of personalized information systems. The study includes several proposals for future research including developing deeper understanding activities related to presented development approach, creating a more detailed view of factors which have an influence on the user's decision to use or not to use personalized information system, studying several systems.
Attenuated Joint Tissue Damage Associated With Improved Synovial Lymphatic Function Following Treatment With Bortezomib in a Mouse Model of Experimental Posttraumatic Osteoarthritis

To investigate the roles of the synovial lymphatic system in the severity and progression of joint tissue damage and functional responses of synovial lymphatic endothelial cells (LECs) to macrophage subsets, and to evaluate the therapeutic potential of the proteasome inhibitor bortezomib (BTZ) in a mouse model of experimental posttraumatic osteoarthritis (OA).
Information Theoretical Limit of Operation Forensics

While more and more forensic techniques have been proposed to detect the processing history of multimedia content, one starts to wonder if there exists a fundamental limit on the capability of forensics. In other words, besides keeping on searching what investigators can do, it is also important to find out the limit of their capability and what they cannot do. In this work, we explore the fundamental limit of operation forensics by proposing an information theoretical framework. Specifically, we consider a general forensic system of estimating operations’ hypotheses based on extracted features from the multimedia content. In this system, forensicability is defined as the maximum forensic information that features contain about operations. Then, due to its conceptual similarity with mutual information in information theory, forensicability is measured as the mutual information between features and operations’ hypotheses. Such a measurement gives the error probability lower bound of all practical estimators which use these features to detect the operations’ hypotheses. Furthermore, it can determine the maximum number of hypotheses that we can theoretically detect. To demonstrate the effectiveness of our proposed information theoretical framework, we apply this framework on a forensic example of detecting the number of JPEG compressions based on DCT coefficient histograms. We conclude that, under typical settings of forensic analysis, the maximum number of JPEG compressions that we can detect using DCT coefficient histogram features is 4. Furthermore, we obtain the optimal strategies for investigators and forgers based on the fundamental measurement of forensicability.
Hidden Markov model framework for industrial maintenance activities

This article deals with modelization of industrial process by using hidden Markov model. The process is seen as a discrete event system. We propose different structures based on Markov automata, called topologies. A synthetic hidden Markov model is designed in order to match to a real industrial process. The models are intended to decode industrial maintenance observations (also called “symbol”). Symbols are produced with a corresponding degradation level (also called “state”). These 2-tuple (symbol, state) are known as Markov chains, also called “a signature. ” Hence, these various 2-tuple are implemented in the proposed topologies by using the Baum–Welch learning algorithm (decoding by forward variable) and the segmental K-means learning (decoding by Viterbi). We assess different frameworks (topology, learning and decoding algorithm, distribution) by relevancy measurements on model outputs. Then, we determine the most relevant framework for use in maintenance activities. Afterward, we try to minimize the size of the learning data. Thus, we could evaluate the model by using “sliding windows” of data. Finally, an industrial application is developed and compared with this framework. Our goal is to improve worker safety, maintenance policy, process reliability and reduce CO2 emissions in the industrial sector.
3D shape analysis for quantification, classification, and retrieval

3D Shape Analysis for Quantification, Classification, and Retrieval Indriyati Atmosukarto Chair of the Supervisory Committee: Professor Linda G. Shapiro Computer Science and Engineering Three-dimensional objects are now commonly used in a large number of applications including games, mechanical engineering, archaeology, culture, and even medicine. As a result, researchers have started to investigate the use of 3D shape descriptors that aim to encapsulate the important shape properties of the 3D objects. This thesis presents new 3D shape representation methodologies for quantification, classification and retrieval tasks that are flexible enough to be used in general applications, yet detailed enough to be useful in medical craniofacial dysmorphology studies. The methodologies begin by computing low-level features at each point of the 3D mesh and aggregating the features into histograms over mesh neighborhoods. Two different methodologies are defined. The first methodology begins by learning the characteristics of salient point histograms for each particular application, and represents the points in a 2D spatial map based on longitude-latitude transformation. The second methodology represents the 3D objects by using the global 2D histogram of the azimuth-elevation angles of the surface normals of the points on the 3D objects. Four datasets, two craniofacial datasets and two general 3D object datasets, were obtained to develop and test the different shape analysis methods developed in this thesis. Each dataset has different shape characteristics that help explore the different properties of the methodologies. Experimental results on classifying the craniofacial datasets show that our methodologies achieve higher classification accuracy than medical experts and existing state-of-the-art 3D descriptors. Retrieval and classification results using the general 3D objects show that our methodologies are comparable to existing view-based and feature-based descriptors and outperform these descriptors in some cases. Our methodology can also be used to speed up the most powerful general 3D object descriptor to date.
Comparative simulation study of three control techniques applied to a biped robot

This paper provides a comparative study, through simulation, of the effectiveness of the local (decoupled) PD control, the computed torque control, and the sliding mode robust control applied to a 5-link biped robot model. The superiority of the sliding mode control in case of existence of large parametric uncertainty is verified. It is argued that sliding mode control appropriately smoothed can be used successfully in actual (experimental or not) bipeds to increase their performance capabilities. <<ETX>>
The Rest-frame H-band Luminosity Function of Red-sequence Galaxies in Clusters at 1.0 < z < 1.3

We present results on the rest-frame H-band luminosity functions (LFs) of red-sequence galaxies in seven clusters at 1.0 < z < 1.3 from the Gemini Observations of Galaxies in Rich Early Environments Survey. Using deep GMOS and IRAC 3.6 μm imaging, we identify red-sequence galaxies and measure their LFs down to . By stacking the entire sample, we derive a shallow faint-end slope of and , suggesting that there is a deficit of faint red-sequence galaxies in clusters at high redshift. By comparing the stacked red-sequence LF of our sample with a sample of clusters at z ∼ 0.6, we find an evolution of the faint end of the red sequence over the ∼2.6 Gyr between the two samples, with the mean faint-end red-sequence luminosity growing by more than a factor of 2. The faint-to-luminous ratio of our sample ( ) is consistent with the trend of decreasing ratio with increasing redshift proposed in previous studies. A comparison with the field shows that the faint-to-luminous ratios in clusters are consistent with those in the field at z ∼ 1.15 and exhibit a stronger redshift dependence. Our results support the picture that the buildup of faint red-sequence galaxies occurs gradually over time and suggest that faint cluster galaxies, similar to bright cluster galaxies, already experience the quenching effect induced by the environment at z ∼ 1.15.
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsic susceptibility to adversarial perturbations --- small but deliberate alterations to the input of a neural network, precipitating incorrect predictions. In this paper, we seek to investigate whether adversarial perturbations pose risks to ML-based CAD tools, and if so, how these risks can be mitigated. To this end, we use a motivating case study of lithographic hotspot detection, for which convolutional neural networks (CNN) have shown great promise. In this context, we show the first adversarial perturbation attacks on state-of-the-art CNN-based hotspot detectors; specifically, we show that small (on average 0.5% modified area), functionality preserving and design-constraint satisfying changes to a layout can nonetheless trick a CNN-based hotspot detector into predicting the modified layout as hotspot free (with up to 99.7% success). We propose an adversarial retraining strategy to improve the robustness of CNN-based hotspot detection and show that this strategy significantly improves robustness (by a factor of ~3) against adversarial attacks without compromising classification accuracy.
Witnessing incompatibility of quantum channels

We introduce the notion of incompatibility witness for quantum channels, defined as an affine functional that is non-negative on all pairs of compatible channels and strictly negative on some incompatible pair. This notion extends the recent definition of incompatibility witnesses for quantum measurements. We utilize the general framework of channels acting on arbitrary finite dimensional von Neumann algebras, thus allowing us to investigate incompatibility witnesses on measurement-measurement, measurement-channel and channel-channel pairs. We prove that any incompatibility witness can be implemented as a state discrimination task in which some intermediate classical information is obtained before completing the task. This implies that any incompatible pair of channels gives an advantage over compatible pairs in some such state discrimination task.
Quantum critical scaling of the conductivity tensor at the metal-insulator transition in Nb$_{1-x}$Ti$_{x}$N

In contrast to the Landau paradigm, a metal-insulator transition (MIT), driven purely by competition between itinerance and localization and unaccompanied by any conventional (e.g, magnetic) order-disorder instabilities, admits no obvious local order parameter. Here, we present a detailed analysis of the quantum criticality in magneto-transport data on the alloy Nb$_{1-x}$Ti$_{x}$N across a Ti-doping-driven a MIT. We demonstrate, for the first time, clear and novel quantum criticality reflected in the full conductivity tensor across the MIT. Wide ranging, comprehensive accord with recent theoretical predictions strongly suggests that these unanticipated findings are representative of a continuous MIT of the band-splitting type, rather than a conventional Anderson disorder or a "pure" correlation-driven first-order Mott type.
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness

This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness). Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial training reduces the relative error by 20% for CIFAR10 without increasing the computational cost. This outperforms handcrafted networks that were explicitly designed to be spatial-equivariant. Furthermore, we observe for SVHN, known to have inherent variance in orientation, that robust training also improves standard accuracy on the test set. We prove that this no-trade-off phenomenon holds for adversarial examples from transformation groups in the infinite data limit.
Foundations of Statistical Mechanics and the Status of the Born Rule in de Broglie-Bohm Pilot-Wave Theory

We compare and contrast two distinct approaches to understanding the Born rule in de Broglie-Bohm pilot-wave theory, one based on dynamical relaxation over time (advocated by this author and collaborators) and the other based on typicality of initial conditions (advocated by the 'Bohmian mechanics' school). It is argued that the latter approach is inherently circular and physically misguided. The typicality approach has engendered a deep-seated confusion between contingent and law-like features, leading to misleading claims not only about the Born rule but also about the nature of the wave function. By artificially restricting the theory to equilibrium, the typicality approach has led to further misunderstandings concerning the status of the uncertainty principle, the role of quantum measurement theory, and the kinematics of the theory (including the status of Galilean and Lorentz invariance). The restriction to equilibrium has also made an erroneously-constructed stochastic model of particle creation appear more plausible than it actually is. To avoid needless controversy, we advocate a modest 'empirical approach' to the foundations of statistical mechanics. We argue that the existence or otherwise of quantum nonequilibrium in our world is an empirical question to be settled by experiment.
Well-posedness and regularity for a fractional tumor growth model

In this paper, we study a system of three evolutionary operator equations involving fractional powers of selfadjoint, monotone, unbounded, linear operators having compact resolvents. This system constitutes a generalization of a phase field system of Cahn-Hilliard type modelling tumor growth that has been proposed in Hawkins-Daarud et al. (Int. J. Numer. Math. Biomed. Eng. 28 (2012), 3-24) and investigated in recent papers co-authored by the present authors and E. Rocca. The model consists of a Cahn-Hilliard equation for the tumor cell fraction, coupled to a reaction-diffusion equation for a function S representing the nutrient-rich extracellular water volume fraction. Effects due to fluid motion are neglected. The generalization investigated in this paper is motivated by the possibility that the diffusional regimes governing the evolution of the different constituents of the model may be of different (e.g., fractional) type. Under rather general assumptions, well-posedness and regularity results are shown. In particular, by writing the equation governing the evolution of the chemical potential in the form of a general variational inequality, also singular or nonsmooth contributions of logarithmic or of double obstacle type to the energy density can be admitted.