Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimum walking techniques for quadrupeds and bipeds

A new theory is presented which describes quadrupedal as well as bipedal walking. It avoids errors which occurred in previous theories by evaluating separately the work done by each leg instead of deriving net work from mechanical energy fluctuations. It takes particular account of two parameters, the duty factor β (the fraction of the stride for which each foot is on the ground) and a parameter q which defines the time course of the force on each foot. It shows that for any given speed there is an optimum (β,q) which minimizes the energy cost of locomotion. These (β,q) are only a little different for bipeds and quadrupeds except near the critical speed at which the optimum moves abruptly from walking (high β) to running (low β). Walking men use (β, q) close to the theoretical optima, but with slightly higher q. Walking dogs and sheep use q which are lower than the optimum values except at very low speeds. Some of the energy which would otherwise be required for locomotion may be saved by storage of elastic strain energy in tendons etc. This mechanism is more effective in running than in fast walking, which may be why men change from walking to running at lower speeds than the inelastic theory suggests.
A Novel Modified Deep Belief Network for Gastric Cancer Prediction

: Gastric cancer, one of the most fatal cancers in the world is characterized by a growth of cancerous cells within the lining of the stomach. A novel method is proposed for accurate segmentation and classification of gastric cancer. The pathological images are acquired from the Pathology Outline in which the images are divided into three classes namely Adenocarcinoma, GIST and Neuroendocrine-Carcinoid. The image pre-processing step primarily focuses on image re-sizing, noise removal and image enhancement. The processing stage includes image segmentation. The two main approaches used for image segmentation are thresholding and edge detection. The feature extraction step consists of the binarization and masking approach. The masking approach is used for red-spot detection. The feature vectors extracted are normalized to get a uniform value in intensity. In order to achieve an efficient feature set, the extracted original set of features is processed and pre-trained by Deep Boltzmann Machine (DBM). Before feeding the KNN classifier with the selected features, the pre-trained images are divided into two sets, training set (70%) and testing set (30 %). Therefore, the KNN classifier is trained by using the training set to predict and classify the type of cancer.
A Unified Topological-Physical Model for Adaptive Refinement

In Computer Graphics, physically-based simulation of deformable objects is a current challenge, and many effi-cient models have been developed to reach real-time performance. However, these models are often limited when complex interactions involving topological modifications are required. To overcome this, the key issue is to manage concurrently, and at minimal cost, both the topology and physical properties. Thus, this paper presents a unified topological-physical model for soft body simulation. The complete embedding of physical and topological models will facilitate operations like piercing, fracture or cutting, as well as adap-tive refinement. Indeed, the difficulty is to treat topological changes during the simulation, requiring combined geometric and physics considerations. Rigorous topological operations guarantee the validity of the mesh, while direct access to the adjacent and incident relations will ease the update of physical properties of new elements created during these operations. These features are illustrated on an embedded mass-spring system undergoing topological modifications per-formed during simulation. Different levels of subdivision are also presented.
Cryptic diversity among Yazoo Darters (Percidae: Etheostoma raneyi) in disjunct watersheds of northern Mississippi

The Yazoo Darter, Etheostoma raneyi (Percidae), is an imperiled freshwater fish species endemic to tributaries of the Yocona and Little Tallahatchie rivers of the upper Yazoo River basin, in northern Mississippi, USA. The two populations are allopatric, isolated by unsuitable lowland habitat between the two river drainages. Relevant literature suggests that populations in the Yocona River represent an undescribed species, but a lack of data prevents a thorough evaluation of possible diversity throughout the range of the species. Our goals were to estimate phylogenetic relationships of the Yazoo Darter across its distribution and identify cryptic diversity for conservation management purposes. Maximum likelihood (ML) phylogenetic analyses of the mitochondrial cytochrome b (cytb) gene returned two reciprocally monophyletic clades representing the two river drainages with high support. Bayesian analysis of cytb was consistent with the ML analysis but with low support for the Yocona River clade. Analyses of the nuclear S7 gene yielded unresolved relationships among individuals in the Little Tallahatchie River drainage with mostly low support, but returned a monophyletic clade for individuals from the Yocona River drainage with high support. No haplotypes were shared between the drainages for either gene. Additional cryptic diversity within the two drainages was not indicated. Estimated divergence between Yazoo Darters in the two drainages occurred during the Pleistocene (<1 million years ago) and was likely linked to repeated spatial shifts in suitable habitat and changes in watershed configurations during glacial cycles. Individuals from the Yocona River drainage had lower genetic diversity consistent with the literature. Our results indicate that Yazoo Darters in the Yocona River drainage are genetically distinct and that there is support for recognizing Yazoo Darter populations in the Yocona River drainage as a new species under the unified species concept.
Order restricted inference for sequential k-out-of-n systems

Sequential order statistics have been introduced to model sequential k-out-of-n systems which, as an extension of k-out-of-n systems, allow the failure of some components of the system to influence the remaining ones. Based on an independent sample of vectors of sequential order statistics, the maximum likelihood estimators of the model parameters of a sequential k-out-of-n system are derived under order restrictions. Special attention is paid to the simultaneous maximum likelihood estimation of the model parameters and the distribution parameters for a flexible location-scale family. Furthermore, order restricted hypothesis tests are considered for making the decision whether the usual k-out-of-n model or the general sequential k-out-of-n model is appropriate for a given data. c © 2008 Elsevier Inc. All rights reserved. AMS 2000 subject classifications: primary 62F30; 62G30; secondary 62H12; 62H15
Fast Colorimetric Method for the Detection of Captagon Based on a General Sensor Design Involving Aptamers and Gold Nanoparticles

Illicit drug use represents a worldwide health problem involving about 5% of world’s adult population and contributing to crime misery and insecurity. A widely used illicit drug in Iraq in recent years is Captagon, which is second only to methamphetamine. It is also popular in Middle East especially in Syria, Saudi Arabia and Kuwait. It is the brand name of chemical compound Fenethylline, and it is a derivative substantial of amphetamines. It is amphetamine coupled with theophylline via an alkyl chain. We used a rapid, low-cost colorimetric assay for sensitive and visual detection of Captagon in real human samples using a Captagon-specific aptamer as the recognition element and original gold nanoparticles as indicators. The method indicated that the presence of Captagon resulted in gold nanoparticles (Au NPs) solution’s color change from purple to blue. The method was rapid and also worked well in human urine samples, blood and hair. Colorimtric detection of Captagon could be measured either visually or by measurement of the absorbance intensity ratios at 650 and 520 nm, respectively. It worked in the 2 μM to 50 μM concentration range. Selectivity of captgon detection method was also investigated with illicit and licit drugs, which revealed that an obvious change both in absorption spectra and in visual color was observed upon the addition of Captagon, whereas slight and negligible change occurred in the presence of any examined drugs with the similar concentration as Captagon. Our findings presented that hair was a good example for detection of drug history of Captagon and other illicit drugs compared with urine and blood, which is believed to represent a widely applicable aptamer-based detection system.
Increased oxygen consumption in blennies (Blennim pholis L.) exposed to their mirror images

The sight of its mirrored image increases the oxygen consumption of Blennius pholis. This is due to an increased metabolic rate and an increased activity. For schooling species, the opposite effect has been reported in the literature and has been termed the ‘group effect’. The B. Pholis response is discussed in relation to species specific preferences of the social environment. The hypothesis is put forward that deviations form the preferred social environment cause an activation of the individual.
Machine Learning Methods for Computer Assisted Diagnosis and Medical Image Registration

In the last years, machine learning based approaches have seen tremendous success at solving challenging tasks in a plethora of disciplines due to their potential to generate accurate prediction models, leveraging on empirical information extracted from large amounts of data. Medical image analysis has not been the exception, and machine learning based approaches now dominate a variety of tasks including computer assisted diagnosis, image segmentation, image registration and computer assisted interventions. The success of machine learning paired with an increase on the availability of digital image records and computational power calls for an exploration of how novel machine learning approaches can be developed to address key questions in the medical image analysis field. In this thesis, we present a number of novel machine learning based methods for diverse medical image analysis tasks. Our first contribution is a novel framework based on an age estimation model, used to detect brain abnormalities caused by neuropathologies. We propose a model which measure deviations from the mean healthy aging trajectory using uncertainty based metrics and we showcase its ability to measure brain abnormality caused by autism, mild cognitive impairment and Alzheimer’s disease. In our second contribution, we tackle the problem of training machine learning models using limited amounts of labeled data. We formulate the efficient selection of a training dataset from big repositories of medical data as a multi-armed bandit problem. Our method is able to select relevant samples, based solely on meta information associated to the images leading to accurate models trained with only a fraction of the available data. Our third and final contribution is a machine learning based framework for the registration of multimodal images. Our approach models the problem of multimodal image registration not as that of learning a similarity metric between images from different modalities, but it rather aims at learning directly the transformation parameters bringing the images into spatial alignment. This approach leads to a fast and accurate multimodal registration metric, which can be easily optimized using simple gradient descent optimization. This dissertation aims to generate discussion about the potential of machine learning techniques to solve a variety of medical image analysis tasks, and also to highlight and address some of the critical challenges which have so far limited the success of machine learning in medical image analysis when compared to other disciplines.
Pension incentives and labor supply: Evidence from the introduction of universal old-age assistance in the UK

In dieser Studie untersuchen wir wie das Arbeitsangebot und die gesellschaftliche Wohlfahrt auf die Einfuhrung einer umfassenden Altersabsicherung reagiert. Dafur greifen wir auf eine einzigartige Reform zuruck: Die Einfuhrung offentlicher Renten im Vereinigten Konigreich durch den Old-Age Pension Act (OPA) im Jahr 1908. Fur die Analyse verwenden wir erst seit kurzem verfugbare Volkszahlungsdaten, die die gesamte Bevolkerung abdecken. Wir identifizieren den Arbeitsangebotseffekt durch ein Regression Discontinuity Design am neu geschaffenen Rentenanspruchsalter. Unsere Ergebnisse zeigen einen betrachtlichen und abrupten Ruckgang der Erwerbsbeteiligung um 6,0 Prozentpunkte (13%), wenn Personen das Anspruchsalter von 70 Jahren erreichen. Dieser plotzliche Ruckgang tritt erst nach Erreichen der Altersgrenze und erst nach der Umsetzung des OPA auf. Trotz des betrachtlichen Ruckganges des Arbeitskrafteangebots war der gesamte Effizienzverlust durch den OPA gering. Vergleicht man den Effizienzverlust mit dem Wohlfahrtsgewinn durch die Umverteilung von oben nach unten in einer Zeit von hoher Ungleichheit und geringer offentlicher Umverteilung zeigt sich, dass die Renteneinfuhrung die gesellschaftliche Wohlfahrt erhoht haben durfte.
We Need Fairness and Explainability in Algorithmic Hiring

Algorithms and machine learning models, including the decisions made by these models, are becoming ubiquitous in our daily life, including hiring. We make no value judgment regarding this development; rather, we simply acknowledge that it is quickly becoming reality that automation plays a role in hiring. Increasingly, these technologies are used in all of the small decisions that make up the modern hiring pipeline: from which resumes get selected for a first screen to who gets an on site interview. Thus, these algorithms and models may potentially amplify bias and (un)fairness issues for many historically marginalized groups. While there is a rapidly expanding literature on algorithmic decision making and fairness, there has been limited work on fairness specifically for online, multi-stakeholder decision making processes such as those found in hiring. We outline broad challenges including formulating definitions for fair treatment and fair outcomes in hiring, and incorporating these definitions into the algorithms and processes that constitute the modern hiring pipeline. We see the AAMAS community as uniquely positioned to address these challenges.
Imitation Learning via Bootstrapped Demonstrations in an Open-World Video Game

Training an artiﬁcial agent in a modern open-world video game can be time-consuming and computationally expensive. Considering the game as a completely observable MDP may not be the best solution due to the run-away observational complexity, data volume problem, and high costs of obtaining observations. We show that the treatment of the game as a POMDP with low-dimensional observations provides some key advantages in training simpler Markov models. We combine these models with an automated bootstrapping from human demonstrations via automatic interaction of an ensemble of Markov models with the game to train a DNN. The proposed approach promises a quick, inexpensive turn-around on efﬁciently training agents that approximate individual human players or segments of the players in a complex open-world game.
Felt Sound: A Shared Musical Experience for the Deaf and Hard of Hearing

We present a musical interface speciﬁcally designed for inclusive performance that oﬀers a shared experience for both individuals who are hard of hearing as well as those who are not. This interface borrows gestures (with or without their overt meaning) from American Sign Language (ASL), rendered using low-frequency sounds that can be felt by every-one in the performance. The Hard of Hearing cannot experience the sound in the same way. Instead, they are able to physically experience the vibrations, nuances, contours, as well as their correspondences with the hand gestures. Those who are not hard of hearing can experience the sound, but also feel it just the same, with the knowledge that the same physical vibrations are shared by everyone. The employment of sign language adds another aesthetic dimension to the instrument –a nuanced borrowing of a functional communication medium for an artistic end.
Germ cell cluster formation and cell death are alternatives in caste-specific differentiation of the larval honey bee ovary

Summary Caste-specific differentiation of the female honey bee gonad takes place in the fifth larval instar. In queen larvae most ovarioles exhibit almost simultaneous formation of numerous germ cell clusters within the first 20 h after the last larval molt. Ultrastructurally distinctive fusomal cytoplasm connects these cystocytes. Germ cell differentiation is accompanied by morphological changes in somatic components of the ovarioles, the follicle and the terminal filament cells. Subsequently, queen ovarioles elongate and differentiate basal stalks that coalesce in a basal calyx. A second round of mitotic activity was found to occur in the late prepupal and early pupal queen ovary. This round may elevate germ cell numbers composing each cluster to levels observed in follicles of adult honey bee queens. In contrast, germ cell cluster formation does not occur in most of the 120–160 ovarioles of the larval worker ovary, but instead many cells in such ovarioles show signs of impending degeneration, such as l.
Design & Analysis of Optimal Coin-tossing: New Techniques

Collective coin-tossing allows n processors with private randomness sources to agree on a common public coin. Without loss of generality, one can assume that the output is in the set { 0 , 1 } , and the expected output of a coin-tossing protocol is X . The objective of a coin-tossing protocol is to be robust to adversarial interventions. In this paper, we study Byzantine adversaries who can arbitrarily set the messages of the corrupted processors. Historically, the study of coin-tossing protocols, with the introduction of even the mildest of variations in its setting, tends to yield surprising and exciting outcomes. We know several optimal or asymptotically optimal protocols like tribes, baton passing, and threshold protocols. Incidentally, there are several variants of coin-tossing where the majority protocol (or, more generally, the threshold protocols) turn out to be asymptotically optimal. In this work, we consider coin-tossing protocols in two security models and study the susceptibility of the optimal coin-tossing protocols in those settings to adversarial attacks. In the ﬁrst model, there are n processors and processor i broadcasts her uniformly and independently random message x i ∈ { 0 , 1 } . The processors apply a function f n : { 0 , 1 } n → { 0 , 1 } to the broadcast messages and agree on their common output f n ( x 1 , . . . , x n ). After all the processors broadcast their messages, the adversary may corrupt at most t processors and change their messages arbitrarily. The optimal protocol minimizes the change in the expected output that this adversary causes. We reduce this problem to an isoperimetric inequality over the boolean hypercube and demonstrate that the threshold protocols are the optimal protocols. In the second model, at time i , processor i broadcasts her message x i , and her message distribution possibly depends on the previously broadcast messages. We consider an adversary who can take control of one processor and change her message arbitrarily. In this case, we prove that the threshold protocols are asymptotically optimal.
University of Groningen Polymorphisms of Catechol-O-Methyl Transferase (COMT) Gene in Vulnerability to Levodopa-Induced Dyskinesia Ivanova,

Purpose. Parkinson's disease (PD), a common neurodegenerative disorder, is usually treated with Levodopa (L-DOPA). The use of this drug, however, is severely limited by the development of side effects of the motor system: Levodopa-induced dyskinesia (LID). The aim of this study is to investigate the association between seven COMT gene single-nucleotide polymorphisms (SNPs) and the development of LID in patients with PD. Methods. 232 Caucasian patients with PD were investigated. 212 patients with PD received Levodopa therapy. Dyskinesia was assessed with the use of the Abnormal Involuntary Movement Scale (AIMS). Genotyping was carried out on seven SNPs of the COMT gene (rs4680, rs6269, rs4633, rs4818, rs769224, rs165774, rs174696) using a real-time PCR method, and blind to the clinical status of the subjects. Results. We found association between four SNPs, rs165774, rs4818, rs4633, rs4680, and LID. When the duration of disease was added as a covariate in regression analysis, however, the results did not reach statistical significance. Only the additive model for rs165774 was found to be close to be statistical significance (OR = 1.627 [0.976–2.741], permutation p = 0.057). Conclusions. The results failed to clearly support a contribution of the studied polymorphisms; this may be related to a dominant relationship with the disease duration confounding the effect on the prevalence of LID.
Power transformer differential protection with integral approach

This paper presents a new approach to differential protection of power transformers with use of the integral principle. The required criteria signals are calculated directly from the operational and restraining currents in particular phases. Additional support of the second harmonic is also used. The proposed integral principle is simple and exhibits superior performance against other commonly applied algorithms. The theoretical investigations are followed by testing with simulation runs as well as recorded signals.
A new era for biosphere reservesMexico's Sian Ka'an shows that it's hard to be everything a biosphere reserve should be

n a typical hot, sunny afternoon last summer, Mexican fisherman Armando Lopez anchored his boat to the pier near his home and began sorting the day's catch of lobster. A few moments later, on the other side of the pier, US biologist David Miller pulled up in another boat filled with research equipment and four US high school teachers and students working as volunteers on his lobster habitat study. As the volunteers snapped photographs, Lopez and Miller swapped stories on their morning's activities. After unloading the boats, all six headed across a dirt road to the Cuzan Guest House, run by Lopez and his partner Sonja Lillvik, for a lunch of fresh lobster ceviche. That all these activities took place within a biosphere reserve known as Sian Ka'an might be a visitor's first clue that these reserves have little in