Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the dynamic model used by the KUKA LWR: A reverse engineering approach

An approach is presented for the model identification of the so-called link dynamics used by the KUKA LWR-IV, a lightweight manipulator with elastic joints that is very popular in robotics research but for which a complete and reliable dynamic model is not yet publicly available. The control software interface of this robot provides numerical values of the link inertia matrix and the gravity vector at each configuration, together with link position and joint torque sensor data. Taking advantage of this information, a general procedure is set up for determining the structure and identifying the value of the relevant dynamic coefficients used by the manufacturer in the evaluation of these robot model terms. We call this a reverse engineering approach, because our main goal is to match the numerical data provided by the software interface, using a suitable symbolic model of the robot dynamics and the inertial and gravity coefficients that are being estimated. Only configuration-dependent terms are involved in this process, and thus static experiments are sufficient for this task. The main issues of dynamic model identification for robots with elastic joints are discussed in general, highlighting the pros and cons of the approach taken for this class of KUKA lightweight manipulators. The main identification results, including training and validation tests, are reported together with additional dynamic validation experiments that use the complete identified model and joint torque sensor data.
Stochastic analysis of image acquisition, interpolation and scale-space smoothing

In the high-level operations of computer vision it is taken for granted that image features have been reliably detected. This paper addresses the problem of feature extraction by scale-space methods. There has been a strong development in scale-space theory and its applications to low-level vision in the last couple of years. Scale-space theory for continuous signals is on a firm theoretical basis. However, discrete scale-space theory is known to be quite tricky, particularly for low levels of scale-space smoothing. The paper is based on two key ideas: to investigate the stochastic properties of scale-space representations and to investigate the interplay between discrete and continuous images. These investigations are then used to predict the stochastic properties of sub-pixel feature detectors. The modeling of image acquisition, image interpolation and scale-space smoothing is discussed, with particular emphasis on the influence of random errors and the interplay between the discrete and continuous representations. In doing so, new results are given on the stochastic properties of discrete and continuous random fields. A new discrete scale-space theory is also developed. In practice this approach differs little from the traditional approach at coarser scales, but the new formulation is better suited for the stochastic analysis of sub-pixel feature detectors. The interpolated images can then be analysed independently of the position and spacing of the underlying discretisation grid. This leads to simpler analysis of sub-pixel feature detectors. The analysis is illustrated for edge detection and correlation. The stochastic model is validated both by simulations and by the analysis of real images.
TierML : Using tiers of reliability for agile elasticity in machine learning

The TierMLparameter server system for machine learning (ML) enables aggressive exploitation of transient revocable resources to complete model training cheaper and/or faster. Many shared computing clusters allow users to utilize excess idle resources at lower cost or priority, with the proviso that some or all may be taken away at any time (e.g., the Amazon EC2 spot market often provides such resources at a 90% discount). Unlike other parameter server systems, TierMLexploits such transient resources, using minimal non-transient resources to efficiently adapt to bulk additions and revocations of transient machines. Our evaluations show that TierMLreduces cost by ≈75% relative to non-transient pricing and by 46%-50% relative to using transient resources with checkpointing to address bulk changes, while nearly matching or decreasing running times. Acknowledgements: We thank the members and companies of the PDL Consortium: Broadcom, Citadel, EMC, Facebook, Google, Hewlett-Packard Labs, Hitachi, Intel, Microsoft Research, MongoDB, NetApp, Oracle, Samsung, Seagate Technology, Two Sigma, Uber and Western Digital for their interest, insights, feedback, and support. This research is supported in part by Intel as part of the Intel Science and Technology Center for Cloud Computing (ISTC-CC), National Science Foundation under awards CNS-1042537, CCF-1533858 and CNS-1042543 (PRObE [14]), and DARPA Grant FA87501220324.
Mediators of interparental conflict and adolescent internalizing/externalizing behaviors

Interparental conflict has been shown to be associated with child psychopathology (internalizing and externalizing behaviors). Adolescents are at risk for developing internalizing and externalizing behaviors because they are aware of the implications of the interparental conflict, they can attempt to mediate the conflict, and because of agerelated responsibilities, they often experience new and unfamiliar stressors. A comprehensive review of the literature revealed four mediational models with substantial empirical support that explain the relation between interparental conflict and adolescent psychopathology: the cognitive-contextual model, the triangulation model, the spillover model, and the interparental conflict-parental psychopathology model. Typically, the mediators of these models (self-blame/perceived threat; triangulation; negative parenting behaviors; parental psychopathology, respectively) have been examined individually. The aim of this study was threefold: 1) examine the specificity of adolescent psychopathology (dimension versus diagnosis), 2) test each theoretical model, and 3) develop and test an integrative model that included the mediational mechanisms from the individual models. A community sample of 152 families (mother, father, adolescent) was recruited from the contiguous United States. Considering specific psychiatric diagnoses did not improve the fit of models that included the respective adolescent dimensional internalizing or externalizing behaviors. The hypotheses of the cognitive-contextual model (mediator: perceived threat), spillover model (mediators: maternal/paternal parenting), and the interparental conflict-parental psychopathology model (mediators: maternal/paternal internalizing) were supported in this study, but mediation was not supported for the triangulation model. Considering the mediators together, adolescent
A Novel Animal Model of Hippocampal Cognitive Deficits, Slow Neurodegeneration, and Neuroregeneration

Long-term adrenalectomy (ADX) results in an extensive and specific loss of dentate gyrus granule cells in the hippocampus of adult rats. This loss of granule cells extends over a period of weeks to months and ultimately results in cognitive deficits revealed in a number of tasks that depend on intact hippocampal function. The gradual nature of ADX-induced cell death and the ensuing deficits in cognition resemble in some important respects a variety of pathological conditions in humans. Here, we characterize behavioural and cellular processes, including adult neurogenesis, in the rat ADX model. We also provide experimental evidence for a neurogenic treatment strategy by which the lost hippocampal cells may be replaced, with the goal of functional recovery in mind.
Training Effects on Older Adults in Information and Communication Technologies Considering Psychosocial Variables

The main aim of this study is to contribute knowledge about the impact of the use of information and communication technologies (ICT) on the self-concept, mood, and quality of life of institutionalized older adults in retirement homes and day care centers (Portuguese institutions). It also studies the influence of independent variables such as age; education; referral to the institution (own initiative, indicated by friends, indicated by family, indicated by social security); attendance schedule; and visits from family and friends. The study comprised 41 participants organized into two groups: 22 older adults in the ICT Group and 19 older adults in the Passive Control Group. There were three evaluation periods: before the onset of intervention and 11 and 22 months after intervention began. The results demonstrate that involvement with information and communication technologies produced positive effects on older adults’ social behavior and their self-perception of physical and environment facets of quality of life. Moreover, they suggest that there is a relationship between psychosocial variables and independent variables.
performance of students from the former USSR in Greek schools

In the 1990s, Greece received, among other immigrants from different regions of the world, repatriates from the former USSR as well as refugees from the same region.1 This article outlines part of a research study2 focusing on the children of both Greek and other origins, who emigrated to Greece from the former USSR and who attended primary schools in Thessaloniki between the academic years 1989—90 to 1999—2000. The aim of this article is to compare the performance in language and mathematics of immigrant and repatriate children, who started school in Greece, with children of the same group, who joined school at a later grade, and also to compare the first group of children with their native peers.
Increasing the Effectiveness of Homework for All Learners in the Inclusive Classroom.

This article discusses how teachers can increase the effectiveness of homework assignments for all learners. Homework, when designed and implemented properly, is a valuable tool for reinforcing learning. This essay provides a summary of educational research on homework, discusses the elements of effective homework, and suggests practical classroom applications for teachers. The synthesis of these three areas is intended to supplement the literature on homework in order to help preprofessional and current teachers increase the effectiveness of homework and employ best practices in inclusive classroom settings. With the increasing number of students with special needs included in general education settings and the increasing pressure placed on students to make academic gains on standardized tests, it is more important than ever that teachers are equipped with the tools necessary to effectively use homework as a learning tool for all students regardless of their ability levels.
A view of margin losses as regularizers of probability estimates

Regularization is commonly used in classifier design, to assure good generalization. Classical regularization enforces a cost on classifier complexity, by constraining parameters. This is usually combined with a margin loss, which favors large-margin decision rules. A novel and unified view of this architecture is proposed, by showing that margin losses act as regularizers of posterior class probabilities, in a way that amplifies classical parameter regularization. The problem of controlling the regularization strength of a margin loss is considered, using a decomposition of the loss in terms of a link and a binding function. The link function is shown to be responsible for the regularization strength of the loss, while the binding function determines its outlier robustness. A large class of losses is then categorized into equivalence classes of identical regularization strength or outlier robustness. It is shown that losses in the same regularization class can be parameterized so as to have tunable regularization strength. This parameterization is finally used to derive boosting algorithms with loss regularization (BoostLR). Three classes of tunable regularization losses are considered in detail. Canonical losses can implement all regularization behaviors but have no flexibility in terms of outlier modeling. Shrinkage losses support equally parameterized link and binding functions, leading to boosting algorithms that implement the popular shrinkage procedure. This offers a new explanation for shrinkage as a special case of loss-based regularization. Finally, α-tunable losses enable the independent parameterization of link and binding functions, leading to boosting algorithms of great exibility. This is illustrated by the derivation of an algorithm that generalizes both AdaBoost and LogitBoost, behaving as either one when that best suits the data to classify. Various experiments provide evidence of the benefits of probability regularization for both classification and estimation of posterior class probabilities.
Comparison of functional connectivity between empirical and randomized structural brain networks

This study combines experimental and modeling approaches in order to investigate the temporal dynamics of the human brain at rest. The dynamics of the neuronal activity is modeled with FitzHugh-Nagumo oscillators and the blood-oxygen-level-dependent (BOLD) time series is inferred via the Balloon-Windkessel hemodynamic model. The simulations are based on structural connections that are derived from diffusionweighted magnetic resonance imaging measurements yielding anatomical probabilities between the considered brain regions of interest. In addition, the length of the fiber tracks allows for inference of coupling delays due to finite signal propagation velocities. We aim (i) to investigate the network topology of our neuroimaging data and (ii) how randomization of structural connections influence dynamics on top of it. The network characteristics of the structural connectivity data are compared to density-matched Erdős-Rényi random graphs. Furthermore, the neuronal and BOLD activity are modeled on both real and random (Erdős-Rényi type) graphs. The simulated temporal dynamics on both graphs are compared statistically to capture whether the spatial organization of these network affects the modeled time series. Results supported that key topological network properties such as small-worldness of our neuroimaging data are distinguishable from random networks. Moreover, the simulated BOLD activity on real and random graphs are observed to be dissimilar. The difference of the modeled temporal dynamics on the brain and random graphs suggests that anatomical connections in the human brain together with dynamical self-organization are crucial for the temporal evolution of the resting-state activity.
Automatic allocation of identifiers in linear wireless sensor networks using link-level processes

Wireless sensor networks, which are used for monitoring large-scale linear structures, are having a significant importance due to its implementation and low cost maintenance. The main challenges that this technology faces are the location and automatic identification of thousands of nodes that are part of a linear large-scale infrastructure, with minimum processing and low power consumption of the nodes. This paper proposes an automatic identification and location mechanism of the nodes that are part of a linear structure using link-level processes and the IEEE 802.15. 4 protocol. This mechanism improves the reliability in the allocation process of identifiers without using routing protocols to minimize the number of sent messages, also the computation process is done at the nodes, and therefore it reduces energy consumption. The proposed algorithm is evaluated using a prototype, for which we present initial performance analysis results focusing on delay. The implementation results confirm that with the proposed algorithm we obtain automatic allocations of identifiers.
John Dewey's Conundrum: Can Democratic Schools Empower?

Developed at the end of the 1900s, largely in his short-lived Laboratory School at the University of Chicago, John Dewey's vision of democratic education has remained influential for over a century. Yet, as he grew older Dewey himself increasingly lost faith in the ability of schools, alone, to create a more democratic society. Drawing on data available from the Laboratory School, this paper expands upon Dewey's concerns. Ultimately, I argue that Dewey's educational approach failed to equip students to act effectively in the world as it was (and still is), and, further, that Dewey's model of democracy, while extremely useful, is nonetheless inadequate to serve the varied needs of a diverse and contentious society.
Probabilistic tree transducers for grammatical error correction

We investigate the application of weighted tree transducers to correcting grammatical errors in natural language. Weighted finite-state transducers (FST) have been used successfully in a wide range of natural language processing (NLP) tasks, even though the expressiveness of the linguistic transformations they perform is limited. Recently, there has been an increase in the use of weighted tree transducers and related formalisms that can express syntax-based natural language transformations in a probabilistic setting. The NLP task that we investigate is the automatic correction of grammar errors made by English language learners. In contrast to spelling correction, which can be performed with a very high accuracy, the performance of grammar correction systems is still low for most error types. Commercial grammar correction systems mostly use rule-based methods. The most common approach in recent grammatical error correction research is to use statistical classifiers that make local decisions about the occurrence of specific error types. The approach that we investigate is related to a number of other approaches inspired by statistical machine translation (SMT) or based on language modelling. Corpora of language learner writing annotated with error corrections are used as training data. Our baseline model is a noisy-channel FST model consisting of an n-gram language model and a FST error model, which performs word insertion, deletion and replacement operations. The tree transducer model we use to perform error correction is a weighted top-down tree-to-string transducer, formulated to perform transformations between parse trees of correct sentences and incorrect sentences. Using an algorithm developed for syntax-based SMT, transducer rules are extracted from training data of which the correct version of sentences have been parsed. Rule weights are also estimated from the training data. Hypothesis sentences generated by the tree transducer are reranked using an n-gram language model. We perform experiments to evaluate the performance of different configurations of the proposed models. In our implementation an existing tree transducer toolkit is used. To make decoding time feasible sentences are split into clauses and heuristic pruning is performed during decoding. We consider different modelling choices in the construction of transducer rules. The evaluation of our models is based on precision and recall. Experiments are performed to correct various error types on two learner corpora. The results show that our system is competitive with existing approaches on several error types. ii Stellenbosch University http://scholar.sun.ac.za
Successful Treatment of Elderly Diffuse Large B-Cell Lymphoma with Central Nervous System Recurrence by Rituximab, Ranimusutine, Ifosfamide, Procarbazine, Dexamethasone, and Etoposide Therapy

The prognosis of CD20-positive (CD20+) diffuse large B-cell lymphoma (DLBCL) with central nervous system (CNS) recurrence is still poor. A standard treatment for CD20+ DLBCL with CNS recurrence in elderly patients has not been established mainly due to adverse effects. We previously reported the efficacy and safety of MIND-E (ranimustine, ifosfamide, procarbazine, dexamethasone, and etoposide) therapy for elderly CD20+ DLBCL patients with CNS recurrence. Here, we report the use of R-MIND-E therapy (rituximab, ranimustine, ifosfamide, procarbazine, dexamethasone and etoposide) in an elderly CD20+ DLBCL patient with CNS recurrence. The patient achieved a complete response according to Revised Response Criteria for Malignant Lymphoma, and treatment-related toxicity was tolerable. R-MIND-E therapy may be a feasible and useful treatment option for elderly CD20+ DLBCL patients with CNS recurrence.
Time to Pay Up

The effectiveness of educator incentive programs rests on the assumption that the potential rewards for participants will motivate them to behave in certain ways (e.g., choose certain jobs, expend greater effort, engage in capacity-building professional development). Some researchers have examined the impact of financial incentives on teacher behaviors and work conditions, but few look inside schools to examine how current teachers interpret their rewards, how payouts affect teachers’ willingness to participate in these programs, and how incentives might be structured to motivate teachers. This mixed-methods study of one Teacher Incentive Fund–supported program addresses that gap. We draw on expectancy and goal setting theories to analyze teachers’ reactions to financial rewards and how their reactions may shape the motivational potency of the incentives.
Pathological and biochemical effects of therapeutic and supratherapeutic doses of celecoxib in Wistar albino male rats

Celecoxib is intended for acute pain, menstrual cramps, pain, and inflammation of osteoarthritis and rheumatoid arthritis. The aim of this study was to evaluate the effects of celecoxib (10 and 50 mg/kg/day) treatment on rats orally for 28 days. We examined effects on some biochemical parameters and kidney and liver tissues of celecoxib-treated Wistar albino male rats. At the end of the study, hepatic and renal function tests were performed and liver and kidney of rats were microscopically examined to detect systemic toxicity of celecoxib. Celecoxib-treated rats had statistically significant decreases of cholesterol, total bilirubin, total protein, urea, globulin, blood urea nitrogen, phosphorus, and calcium. Serum gamma glutamyl transferase levels increased in 10- and 50-mg/kg/day celecoxib-treated rats. Histological examinations showed mononuclear cell infiltration, hyperplasia, and cellular degeneration in liver and tubular damage and mononuclear cell infiltration in kidney. We suggest that high doses of celecoxib may cause changes in liver and kidney histopathology, liver function, and in some biochemical parameters.
Improvement of prognosis in breast cancer in Denmark 1977–2006, based on the nationwide reporting to the DBCG Registry

Introduction. Since 30 years DBCG (Danish Breast Cancer Coperative Group) has maintained, on a nation-wide basis, a clinical database of diagnostic procedures, therapeutic interventions, and clinical outcome in patients with primary breast cancer. The present analysis was undertaken to evaluate the development of the prognosis since 1977, and to analyse factors potentially contributing to the change of the prognosis. Material and Methods. All cases of invasive breast cancer reported to DBCG during the period 1977–2006 were included in the present analysis. Results. A total of close to 80 000 patients were registered in the DBCG Database. Since 1977 the prognosis has improved significantly, thus 5 year survival for the total population of patients with primary breast cancer has increased from 65 to 81%. Discussion. According to the present analysis diagnosis at an earlier stage in the natural course of the disease and especially the development of more active systemic treatment modalities have contributed to the improved prognosis.