Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Cascade Learning

In this paper, we propose a novel approach for efficient training of deep neural networks in a bottom-up fashion using a layered structure. Our algorithm, which we refer to as deep cascade learning, is motivated by the cascade correlation approach of Fahlman and Lebiere, who introduced it in the context of perceptrons. We demonstrate our algorithm on networks of convolutional layers, though its applicability is more general. Such training of deep networks in a cascade directly circumvents the well-known vanishing gradient problem by ensuring that the output is always adjacent to the layer being trained. We present empirical evaluations comparing our deep cascade training with standard end–end training using back propagation of two convolutional neural network architectures on benchmark image classification tasks (CIFAR-10 and CIFAR-100). We then investigate the features learned by the approach and find that better, domain-specific, representations are learned in early layers when compared to what is learned in end–end training. This is partially attributable to the vanishing gradient problem that inhibits early layer filters to change significantly from their initial settings. While both networks perform similarly overall, recognition accuracy increases progressively with each added layer, with discriminative features learned in every stage of the network, whereas in end–end training, no such systematic feature representation was observed. We also show that such cascade training has significant computational and memory advantages over end–end training, and can be used as a pretraining algorithm to obtain a better performance.
Pareto Optimal Solutions for Network Defense Strategy Selection Simulator in Multi-Objective Reinforcement Learning

Using Pareto optimization in Multi-Objective Reinforcement Learning (MORL) leads to better learning results for network defense games. This is particularly useful for network security agents, who must often balance several goals when choosing what action to take in defense of a network. If the defender knows his preferred reward distribution, the advantages of Pareto optimization can be retained by using a scalarization algorithm prior to the implementation of the MORL. In this paper, we simulate a network defense scenario by creating a multi-objective zero-sum game and using Pareto optimization and MORL to determine optimal solutions and compare those solutions to different scalarization approaches. We build a Pareto Defense Strategy Selection Simulator (PDSSS) system for assisting network administrators on decision-making, specifically, on defense strategy selection, and the experiment results show that the Satisficing Trade-Off Method (STOM) scalarization approach performs better than linear scalarization or GUESS method. The results of this paper can aid network security agents attempting to find an optimal defense policy for network security games.
On the Expressive Completeness of Bernays-Schönfinkel-Ramsey Separation Logic

This paper investigates the satisfiability problem for Separation Logic, with unrestricted nesting of separating conjunctions and implications, for prenex formulae with quantifier prefix in the language $\exists^*\forall^*$, in the cases where the universe of possible locations is either countably infinite or finite. In analogy with first-order logic with uninterpreted predicates and equality, we call this fragment Bernays-Sch\"onfinkel-Ramsey Separation Logic [BSR(SLk)]. We show that, unlike in first-order logic, the (in)finite satisfiability problem is undecidable for BSR(SLk) and we define two non-trivial subsets thereof, that are decidable for finite and infinite satisfiability, respectively, by controlling the occurrences of universally quantified variables within the scope of separating implications, as well as the polarity of the occurrences of the latter. The decidability results are obtained by a controlled elimination of separating connectives, described as (i) an effective translation of a prenex form Separation Logic formula into a combination of a small number of \emph{test formulae}, using only first-order connectives, followed by (ii) a translation of the latter into an equisatisfiable first-order formula.
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation

Mild cognitive impairment (MCI) is a prodromal phase in the progression from normal aging to dementia, especially Alzheimers disease. Even though there is mild cognitive decline in MCI patients, they have normal overall cognition and thus is challenging to distinguish from normal aging. Using transcribed data obtained from recorded conversational interactions between participants and trained interviewers, and applying supervised learning models to these data, a recent clinical trial has shown a promising result in differentiating MCI from normal aging. However, the substantial amount of interactions with medical staff can still incur significant medical care expenses in practice. In this paper, we propose a novel reinforcement learning (RL) framework to train an efficient dialogue agent on existing transcripts from clinical trials. Specifically, the agent is trained to sketch disease-specific lexical probability distribution, and thus to converse in a way that maximizes the diagnosis accuracy and minimizes the number of conversation turns. We evaluate the performance of the proposed reinforcement learning framework on the MCI diagnosis from a real clinical trial. The results show that while using only a few turns of conversation, our framework can significantly outperform state-of-the-art supervised learning approaches.
Preventing crime at places

Places have received relatively little attention in crime policy so it is important to define "place. " A place is a very small area reserved for a narrow range of functions, often controlled by a single owner, and separated from the surrounding area. By small we mean that a location is smaller than a neighborhood or community. Examples of places include stores, homes, apartment buildings, street corners, subway stations, and airports. We will also include mobile places, such as buses, in our discussions.
Is Scrum Fit for Global Software Engineering?

Distributed software engineering and agility are strongly pushing on today's software industry. Due to inherent incompatibilities, for years, studying Scrum and its application in distributed setups has been subject to theoretical and applied research, and an increasing body of knowledge reports insights into this combination. Through a systematic literature review, this paper contributes a collection of experiences on the application of Scrum to global software engineering (GSE). In total, we identified 40 challenges in 19 categories practitioners face when using Scrum in GSE. Among the challenges, scaling Scrum to GSE and adopting practices accordingly are the most frequently named. Our findings also show that most solution proposals aim at modifying elements of the Scrum core processes. We thus conclude that, even though Scrum allows for extensive modification, Scrum itself represents a barrier for global software engineering, and development teams have to customize Scrum properly to benefit from agile software development in GSE.
Biomedical Data Analysis by Supervised Manifold Learning

Biomedical data analysis is usually carried out by assuming that the information structure embedded into the biomedical recordings is linear, but that statement actually does not corresponds to the real behavior of the extracted features. In order to improve the accuracy of an automatic system to diagnostic support, and to reduce the computational complexity of the employed classifiers, we propose a nonlinear dimensionality reduction methodology based on manifold learning with multiple kernel representations, which learns the underlying data structure of biomedical information. Moreover, our approach can be used as a tool that allows the specialist to do a visual analysis and interpretation about the studied variables describing the health condition. Obtained results show how our approach maps the original high dimensional features into an embedding space where simple and straightforward classification strategies achieve a suitable system performance.
Carbon nanotubes: a multi-functional material for organic optoelectronics

In recent years carbon nanotubes have emerged as a versatile material suitable for hybrid macro-electronic devices employing organic semiconductors, with the potential to address many of the barriers to the widespread exploitation of organic semiconductors as the basis for low cost, large area, flexible optoelectronics. This paper focuses on the applications of carbon nanotubes in organic light-emitting diodes and photovoltaics. These related devices are currently being pursued with much anticipation as energy efficient solid state lighting and a scalable means of harvesting electrical energy from sunlight. In this paper particular emphasis is placed on the utility of multi-wall carbon nanotubes which have received much less attention than single wall carbon nanotubes for these applications despite offering a number of key advantages.
Light-responsive spiropyran based polymers: Synthesis and photochromic behaviour.

Photo-responsive polymers have been investigated intensively as important elements for the development of smart materials and devices. Our interest is in polymers functionalized with spiropyran (SP), a well-known photocromic molecule that has the ability to reversibly switch from an uncharged, colorless, benzospiropyran form, to a zwitterionic, planar, highly colored merocyanine (MC) form upon exposure to ultraviolet (UV) or visible light. Controlled radical polymerization (CRP) is a particularly attractive method to produce spiropyran- derivitized polymers as the synthesis of polymers with photochromic units present in defined positions, either along the polymer chain or as the polymer end-group, is readily achievable. This thesis initially documents firstly the synthesis of polymers that possess a single SP terminal unit, produced by atom transfer radical polymerization (ATRP), and their light responsiveness. The second part of this work reports the synthesis and the characterization of a SP derivative with a propyne functional group and the study of its photochromism. The alkyne functionality of the BSP compound was exploited to demonstrate a BSP-based polymeric system that utilizes commercially available polymers, through copper(I) catalyzed azide/alkyne reaction (CuAAC). The third part of this work consists of synthesizing well-defined polymeric systems with complex architectures, such as star or graft copolymer, that bear a high density of SP units in the side chain. The strategy employed consists of combining ATRP and the CuAAC.
Prescribing menopausal hormone therapy: an evidence-based approach

The constantly changing landscape regarding menopausal hormone therapy (MHT) has been challenging for providers caring for menopausal women. After a decade of fear and uncertainty regarding MHT, reanalysis of the Women’s Health Initiative data and the results of recent studies have provided some clarity regarding the balance of risks and benefits of systemic MHT. Age and years since menopause are now known to be important variables affecting the benefit-risk profile. For symptomatic menopausal women who are under 60 years of age or within 10 years of menopause, the benefits of MHT generally outweigh the risks. Systemic MHT initiated early in menopause appears to slow the progression of atherosclerotic disease, thereby reducing the risk of cardiovascular disease and mortality. During this window of opportunity, MHT might also provide protection against cognitive decline. In older women and women more than 10 years past menopause, the risk-benefit balance of MHT is less favorable, particularly with regard to cardiovascular risk and cognitive impairment. For women entering menopause prematurely (<40 years), MHT ameliorates the risk of cardiovascular disease, osteoporosis, and cognitive decline. Nonoral administration of estrogen offers advantages due to the lack of first-pass hepatic metabolism, which in turn avoids the increased hepatic synthesis of clotting proteins, C-reactive protein, triglycerides, and sex hormone-binding globulin. The duration of combined MHT use is ideally limited to less than 5 years because of the known increase in breast cancer risk after 3–5 years of use. Limitations to use of estrogen only MHT are less clear, since breast cancer risk does not appear to increase with use of estrogen alone. For women under the age of 60 years, or within 10 years of onset of natural menopause, MHT for the treatment of bothersome menopausal symptoms poses low risk and is an acceptable option, particularly when nonhormonal management approaches fail.
Histogram Equalization Tool: Brightness Preservation and Contrast Enhancement using Segmentation with Opening-by-Reconstruction

Sundry improvement plans are used for improving a picture which incorporates ash scale control, sifting and Histogram Equalization (HE). The issue with pictures is that, their quality depends upon a number of different variables like lighting in the picture catching area, commotion and capability of the administrator. The writing addresses the verbalized issue widely and presents answers for them. Contrast improvement systems are used for correcting visual nature of low difference pictures. Histogram Equalization (HE) is one such procedure used for difference upgrade. The proposed illustrations have a few shared traits in their procedures. Approximately every one of them is at fluctuation either in histogram leveling strategies or in picture quality estimation instruments. An instrument is lost in the writing that is proficient to improve the picture and even perform the examination. In this paper, a GUI apparatus is composed which is coupled with different procedures of picture improvement through histogram balance. Opening-byrecreation is a standout amongst the most effective picture division strategy is used to attain to the craved results. To assess the adequacy of the delineated systems; PSNR, Tenengrad, and Absolute Mean Brightness mistake (AMBE) are used as parameters. The results are decently backed by the parameter estimations toward the end. General Terms Digital Image Processing, Image Enhancement and Histogram Equalization.
Partial Stator Overlap in a Linear Generator for Wave Power: An Experimental Study

This paper presents a study on how the power absorption and damping in a linear generator for wave energy conversion are affected by partial overlap between stator and translator. The theoretical study shows that the electrical power as well as the damping coefficient change quadratically with partial stator overlap, if inductance, friction and iron losses are assumed independent of partial stator overlap or can be neglected. Results from onshore experiments on a linear generator for wave energy conversion cannot reject the quadratic relationship. Measurements were done on the inductance of the linear generator and no dependence on partial stator overlap could be found. Simulations of the wave energy converter’s operation in high waves show that entirely neglecting partial stator overlap will overestimate the energy yield and underestimate the peak forces in the line between the buoy and the generator. The difference between assuming a linear relationship instead of a quadratic relationship is visible but small in the energy yield in the simulation. Since the theoretical deduction suggests a quadratic relationship, this is advisable to use during modeling. However, a linear assumption could be seen as an acceptable simplification when modeling since other relationships can be computationally costly.
Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint

Abstract Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.
ADAPTATION OF GROWTH FORM IN ECHIUM LEUCOPHAEUM (BORAGINACEAE)

Ecotype formation in eight populations of Echium leucophaeum was studied on La Palma (Canary Islands) at 100-m intervals from sea level to 600 m elevation. Climate varied with altitude, the lower elevation having higher temperatures, evaporation, and wind velocity and lower rainfall. A method for recording growth form of individual plants was developed whereby lateral branch production and branching patterns of woody plants could readily be determined. Height growth, lateral branch production, periodicity, and number of branching cycles of Echium were all found to be significantly different in the field, plants from higher elevations having longer branches, more laterals per cycle, fewer cycles, and retarded flowering and fruiting compared with plants near sea level. Two generations of seedlings of the extreme types grown in transplant gardens showed significant differences in inherent rate of growth, indicating genetic adaptation of high and low altitude ecotypes. No significant differences could be shown in the rate of lateral branch formation and periodicity; differences observed in the field with respect to these characteristics may be physiological responses.
Taekwondo Fighting in Training Does Not Simulate the Affective and Cognitive Demands of Competition: Implications for Behavior and Transfer

Enhancing practice design is critical to facilitate transfer of learning. Considerable research has focused on the role of perceptual information in practice simulation, yet has neglected how affect and cognition are shaped by practice environments and whether this influences the fidelity of behavior (Headrick et al., 2015). This study filled this gap by examining the fidelity of individual (cognition, affect, and actions) and interpersonal behavior of 10 highly skilled Australian Taekwondo athletes fighting in training compared to competition. Interpersonal behavior was assessed by tracking location coordinates to analyze distance-time coordination tendencies of the fighter–fighter system. Individual actions were assessed through notational analysis and approximate entropy calculations of coordinate data to quantify the (un)predictability of movement displacement. Affect and cognition were assessed with mixed-methods that included perceptual scales measuring anxiety, arousal, and mental effort, and post-fight video-facilitated confrontational interviews to explore how affect and cognitions might differ. Quantitative differences were assessed with mixed models and dependent t-tests. Results reveal that individual and interpersonal behavior differed between training and competition. In training, individuals attacked less (d = 0.81, p < 0.05), initiated attacks from further away (d = -0.20, p < 0.05) and displayed more predictable movement trajectories (d = 0.84, p < 0.05). In training, fighters had lower anxiety (d = -1.26, p < 0.05), arousal (d = -1.07, p < 0.05), and mental effort (d = -0.77, p < 0.05). These results were accompanied by changes in interpersonal behavior, with larger interpersonal distances generated by the fighter–fighter system in training (d = 0.80, p < 0.05). Qualitative data revealed the emergence of cognitions and affect specific to the training environment, such as reductions in pressure, arousal, and mental challenge. Findings highlight the specificity of performer–environment interactions. Fighting in training affords reduced affective and cognitive demands and a decrease in action fidelity compared to competition. In addition to sampling information, representative practice needs to consider modeling the cognitions and affect of competition to enhance transfer.
Graphical model for joint segmentation and tracking of multiple dividing cells

MOTIVATION To gain fundamental insight into the development of embryos, biologists seek to understand the fate of each and every embryonic cell. For the generation of cell tracks in embryogenesis, so-called tracking-by-assignment methods are flexible approaches. However, as every two-stage approach, they suffer from irrevocable errors propagated from the first stage to the second stage, here from segmentation to tracking. It is therefore desirable to model segmentation and tracking in a joint holistic assignment framework allowing the two stages to maximally benefit from each other. RESULTS We propose a probabilistic graphical model, which both automatically selects the best segments from a time series of oversegmented images/volumes and links them across time. This is realized by introducing intra-frame and inter-frame constraints between conflicting segmentation and tracking hypotheses while at the same time allowing for cell division. We show the efficiency of our algorithm on a challenging 3D+t cell tracking dataset from Drosophila embryogenesis and on a 2D+t dataset of proliferating cells in a dense population with frequent overlaps. On the latter, we achieve results significantly better than state-of-the-art tracking methods. AVAILABILITY AND IMPLEMENTATION Source code and the 3D+t Drosophila dataset along with our manual annotations will be freely available on http://hci.iwr. uni-heidelberg.de/MIP/Research/tracking/
Distinct roles of class I and class III phosphatidylinositol 3-kinases in phagosome formation and maturation

Phagosomes acquire their microbicidal properties by fusion with lysosomes. Products of phosphatidylinositol 3-kinase (PI 3-kinase) are required for phagosome formation, but their role in maturation is unknown. Using chimeric fluorescent proteins encoding tandem FYVE domains, we found that phosphatidylinositol 3-phosphate (PI[3]P) accumulates greatly but transiently on the phagosomal membrane. Unlike the 3′-phosphoinositides generated by class I PI 3-kinases which are evident in the nascent phagosomal cup, PI(3)P is only detectable after the phagosome has sealed. The class III PI 3-kinase VPS34 was found to be responsible for PI(3)P synthesis and essential for phagolysosome formation. In contrast, selective ablation of class I PI 3-kinase revealed that optimal phagocytosis, but not maturation, requires this type of enzyme. These results highlight the differential functional role of the two families of kinases, and raise the possibility that PI(3)P production by VPS34 may be targeted during the maturation arrest induced by some intracellular parasites.