Miracle-dz/UniFAR-FTUs huggingface dataset:
Melanin Concentrating Hormone Is a Novel Regulator of Islet Function and Growth

Melanin concentrating hormone (MCH) is a hypothalamic neuropeptide known to play a critical role in energy balance. We have previously reported that overexpression of MCH is associated with mild obesity. In addition, mice have substantial hyperinsulinemia and islet hyperplasia that is out of proportion with their degree of obesity. In this study, we further explored the role of MCH in the endocrine pancreas. Both MCH and MCHR1 are expressed in mouse and human islets and in clonal β-cell lines as assessed using quantitative real-time PCR and immunohistochemistry. Mice lacking MCH (MCH-KO) on either a C57Bl/6 or 129Sv genetic background showed a significant reduction in β-cell mass and complemented our earlier observation of increased β-cell mass in MCH-overexpressing mice. Furthermore, the compensatory islet hyperplasia secondary to a high-fat diet, which was evident in wild-type controls, was attenuated in MCH-KO. Interestingly, MCH enhanced insulin secretion in human and mouse islets and rodent β-cell lines in a dose-dependent manner. Real-time PCR analyses of islet RNA derived from MCH-KO revealed altered expression of islet-enriched genes such as glucagon, forkhead homeobox A2, hepatocyte nuclear factor (HNF)4α, and HNF1α. Together, these data provide novel evidence for an autocrine role for MCH in the regulation of β-cell mass dynamics and in islet secretory function and suggest that MCH is part of a hypothalamic-islet (pancreatic) axis.
Point X-ray Sources in Elliptical Galaxies

We analyze the upper-end X-ray luminosity function (XLF) observed in elliptical galaxies for point sources. We propose that the observed XLF is dominated by transient BH systems in outburst and the XLF shape reflects the black hole (BH) mass spectrum among old X-ray transients. The BH mass spectrum – XLF connection depends on a weighting factor that is related to the transient duty cycle and depends on the host-galaxy age, the BH mass and the donor type (main sequence, red giant, or white dwarf). We argue that the assumption of a constant duty cycle for all systems leads to results inconsistent with current observations. The type of dominant donors in the upper-end XLF depends on what type of magnetic braking operates: in the case of “standard” magnetic braking, BH X-ray binaries with red-giant donors dominate, and in the case of weaker magnetic braking prescriptions main sequence donors dominate.
TCADS: Trustworthy, Context-Related Anomaly Detection for Smartphones

In this paper we propose a novel, network-based, distributed anomaly detection framework for smart phones. Our approach is based upon the distributed collection of arbitrary, static and dynamic smart phone features. Our approach is not limited to features that can be obtained directly on a device, but also includes features that are provided by other services running in the respective, corporate IT infrastructure (e.g. an IDS). Furthermore, we tag each collected feature with two kinds of information: (1) context-information and (2) trust-information. This way, anomaly detection methods are not limited to work on the actual feature values, but can also consider the context and the trustworthiness of collected features.
Glucosamine Interferes With Myelopoiesis and Enhances the Immunosuppressive Activity of Myeloid-Derived Suppressor Cells

Glucosamine (GlcN) is the most widely consumed dietary supplement and exhibits anti-inflammatory effects. However, the influence of GlcN on immune cell generation and function is largely unclear. In this study, GlcN was delivered into mice to examine its biological function in hematopoiesis. We found that GlcN promoted the production of immature myeloid cells, known as myeloid-derived suppressor cells (MDSCs), both in vivo and in vitro. Additionally, GlcN upregulated the expression of glucose transporter 1 in hematopoietic stem and progenitor cells (HSPCs), influenced HSPC functions, and downregulated key genes involved in myelopoiesis. Furthermore, GlcN increased the expression of arginase 1 and inducible nitric oxide synthase to produce high levels of reactive oxygen species, which was regulated by the STAT3 and ERK1/2 pathways, to increase the immunosuppressive ability of MDSCs. We revealed a novel role for GlcN in myelopoiesis and MDSC activity involving a potential link between GlcN and immune system, as well as the new therapeutic benefit.
Acceleration of accurate floating point operations using SIMD

Several computing systems that use decimal number calculations suffer from the accumulation and propagation of errors. Decimal numbers are represented using specific length floating point formats and hence there will always be a truncation of extra fraction bits causing errors. Several solutions had been proposed for such a problem. Among those accurate calculation systems was the usage of vectors of floating point numbers to represent decimal values with very large accuracy, known as Multi-Number System (MN). Unfortunately, MN calculations are time consuming and are not suitable for real time applications. Several attempts for special architectures had been proposed to speed up those calculations. In this work, the Single Instruction Multiple Data (SIMD) paradigm found in modern CPUs is exploited to accelerate the MN calculations. The basic arithmetic operation algorithms had been modified to utilize the SIMD architecture and a new Square representation of operands had been proposed, this representation was introduced because the MN operations are sequential and iterative, and thus we can't apply the SIMD parallel instructions directly. The proposed architecture has an execution time that is 35% of the original MN execution time for the division, which is the most time consuming operation while preserving the same accuracy.
Optimal Software Pipelining In Presence Of Resource Constraints

This paper presents a new class of algorithms for loop software pipelining in presence of resource constraints. This new approach allows to generate optimal code with respect to throughput even for processors with complex resource constraints which make them hard to program even by hand. It consists of two steps: rst, we build the reservation table for the body of the software pipelined loop by packing the elementary reservation tables of the diierent tasks as if the tasks were independent, the length of the global reservation tables then determines the throughput of the loop; second we determine the schedule of each iteration of the loop so that all data dependencies are satissed and the resource usage conforms with the previously computed reservation table. We show that, in order to reach the optimal throughput (induced by the highest used resource or critical resource), the loop may need to be unrolled. In some important and general cases, we provide results on the unwinding degree necessary to achieve the optimal throughput. We also show how this class of software pipelining algorithms can be used to generate code for the explicitly advanced pipelines found in the Intel i860 processor.
Adaptive Behavior and Development of Infants and Toddlers with Williams Syndrome

Williams syndrome (WS) is a neurodevelopmental disorder that causes deficits in adaptive behavior, difficulties eating and sleeping, cognitive delays, and delayed development. Although researchers have conducted characterizations of children and adults with WS, less is known about young children with this disorder. This study characterizes the developmental and adaptive behavior features of 16 infants and toddlers with WS aged 3 months – 5 years. Data for this project was obtained from 2007 to 2014, and includes parent report data and standardized developmental testing. Thirty-one percent (31.3%) of parents reported that their infant/toddler with WS had sleeping problems and 58.3% reported feeding difficulties. Levels of adaptive behavior were in the Mildly Delayed range as measured by the Adaptive Behavior Assessment System, Second Edition. Self-care skills such as feeding or dressing oneself were significantly weaker than skills needed to function in the community, such as recognizing his/her home or throwing away trash. The difficulty with self-care skills is hypothesized to be related to the reported difficulties with eating and sleeping. Motor skills were significantly lower than both cognitive and language skills on the Bayley Scales of Infant and Toddler Development, Third Edition. The current study highlights the need for early intervention in these young children across all areas of development, particularly in self-care skills.
Unifying Justifications and Debugging for Answer-Set Programs

Recently, (Viegas Damasio et al. 2013) introduced a way to construct propositional formulae encoding provenance information for logic programs. From these formulae, justifications for a given interpretation are extracted but it does not explain why such interpretation is not an answer-set (debugging). Resorting to a meta-programming transformation for debugging logic programs, (Gebser et al. 2008) does the converse. Here we unify these complementary approaches using meta-programming transformations. First, an answer-set program is constructed in order to generate every provenance propositional models for a program, both for well-founded and answer-set semantics, suggesting alternative repairs to bring about (or not) a given interpretation. In particular, we identify what changes must be made to a program in order for an interpretation to be an answer-set, thus providing the basis to relate provenance with debugging. With this meta-programming method, one does not have the need to generate the provenance propositional formulas and thus obtain debugging and justification models directly from the transformed program. This enables computing provenance answer-sets in an easy way by using AS solvers. We show that the provenance approach generalizes the debugging one, since any error has a counterpart provenance but not the other way around. Because the method we propose is based on meta-programming, we extended an existing tool and developed a proof-of-concept built to help computing our models.
ISIS: interface for a semantic information system

ISIS IS an experimental system for graphically manlpulatmg a database The system 1s based on a simply specified high-level semantic data model It demonstrates the capablbtles of a workstation environment by mtegratmg three aspects of database programming m one graphical setting Namely, it permits database constructlon and modification, it allows browsing at the schema and data levels, and provides a graphical query language In all of these activities it maintains uniform graphlcal representations and consistent user mteractlon techniques
QoS performance of OBS in WDM/TDM networks

We discuss the requirement of basic quality of service (QoS) in optical burst switched WDM networks considering their delay and loss probability. Low delay and reducing burst overlap in the electronic domain of an ingress node reduces burst loss, and hence improves (QoS). A simplified mathematical model for delay versus traffic loads considering both low and heavy traffic conditions is developed. In optical domain we have studied the delay characteristics for the offset-time-based scheme with fiber delay lines (FDLs). The minimum delay zone is shown considering the variation of channel capacity C, burst size B, offset time depending on degree of isolation, burst overlapping parameters, and token round trip time R in a metropolitan area network (MAN). Finally, numerical calculation and simulations in priority queue management for different class of service (CoS) show the results are very close, thus validating our approximate delay model. The results show the reduction for higher priority packet latency and the conditions so that low priority packets are not severely dropped.
Rapid Prototyping of Multimedia Analysis Systems: A Networked Hardware/Software Solution

This paper describes a hardware/software framework and approach for fast integration and testing of complex real-time multimedia analysis algorithms. It enables the rapid assessment of combinations of multimedia analysis algorithms, in order to determine their usefulness in future consumer storage products. The framework described here consists of a set of networked personal computers, running a variety of multimedia analysis algorithms and a multi-media database. The database stores both multimedia content and metadata – as generated by multimedia content analysis algorithms – and maintains links between the two. The full hardware/software solution functions as a test-bed for new, advanced content analysis algorithms; new algorithms are easily plugged-in into any of the networked PCs, while outdated algorithms are simply removed. Once a selected consumer system configuration has passed important user-tests, a more dedicated embedded consumer product implementation is derived in a straightforward way from the framework.
A Windowed Impulse Rejection filter for HRV artifact detection

Heart Rate Variability (HRV) has been garnering a lot of attention from medical researchers and biomedical engineers due to its ability to expose crucial information about the status of the nervous system and the health of the human heart. Although time domain analysis of a HRV signals can yield a wealth of information, frequency domain analysis has been gaining in popularity. This is mainly due to the identification of distinct frequency bands that reflect specific components of the nervous system. Nonetheless, signal artifact can severely distort the extracted time and frequency domain parameters alike and thus rendering the information obtained from the signal unusable. In this paper, we propose the use of a Windowed Impulse Rejection (WIR) based artifact detection algorithm. Our performance evaluation demonstrated that our method performs with a higher level of accuracy than its competitors. Also, in terms of complexity, it outperformed the Moving Average algorithm.
Optimal auto-tuning of fractional order PIλ Dμ controller for a DC motor speed using Extremum seeking

Fractional calculus provides novel and higher performance extensions for fractional order proportional integral and derivative (FOPID) controller. In this paper, the parameters of the FO PIλ Dμ controller are optimally learned by using Extremum Seeking (ES) approach, and the optimization performance target is chosen as the integral of square error (ISE). ES is a non-model based method which searches on-line for the parameters that minimize a cost function; in this case the cost function is representative of the controller's performance. Furthermore, this method has the advantage that it can be applied to a plant in which there is no knowledge of the model. The FOPID parameters are tuned based on the results of step response simulations to produce a response with minimal settling time and overshoot. Comparative simulation results on a DC Motor speed control show the effectiveness of the proposed strategy for fractional order PIλ Dμ tuning.
Towards relation discovery for diagnostics

It is difficult to implement predictive maintenance in the automotive industry as it looks today, since the sensor capabilities and engineering effort available for diagnostic purposes is limited. It is, in practice, impossible to develop diagnostic algorithms capable of detecting many different kinds of faults that would be applicable to a wide range of vehicle configurations and usage patterns. However, it is now becoming feasible to obtain and analyse on-board data on vehicles as they are being used. It makes automatic data-mining methods an attractive alternative, since they are capable of adapting themselves to specific vehicle configurations and usage. In order to be useful, though, such methods need to be able to detect interesting relations between large number of available signal. This paper presents an unsupervised method for discovering useful relations between measured signals in a Volvo truck, both during normal operations and when a fault has occurred. The interesting relationships are found in a two-step procedure. In the first step, we identify a set of "good" models, by establishing an MSE threshold over the complete data set. In the second step, we estimate model parameters over time, in order to capture the dynamic behaviour of the system. We use two different approaches here, the LASSO method and the Recursive Least Squares filter. The usefulness of obtained relations is then evaluated using supervised learning to separate different classes of faults.
Hybrid Transfer Learning for Efficient Learning in Object Detection

In the detection of human from image using statistical learning methods, the labor cost of collecting training samples and the time cost for retraining to match the target scene are major issues. One method to reduce the work involved in sample collection is transfer learning based on boosting. However, if there is a large change between the auxiliary scene and target scene, it is difficult to apply the transfer learning. We therefore propose a hybrid transfer learning method in which two feature spaces are prepared, one of feature obtained by transfer and another of full feature search that is the same as retraining. The feature space is selectively switched on the basis of the defined training efficiency. The proposed method improving accuracy up to 8.35% compared to conventional transfer learning while accelerating training time by 3.2 times faster compared to retraining.
Global Workspace Theory (GWT) and Prefrontal Cortex: Recent Developments

In this work, we provide a brief overview of Global Workspace Theory (GWT), along with recent developments and clarifications of modern neuroscientific evidence. GWT started in the 1980s as a purely psychological theory of conscious cognition, and has become a prominent approach in scientific studies of consciousness (Mashour et al., 2020). Based on today’s far more detailed understanding of the brain, GWT has adapted to new waves of evidence. The brain-based version of GWT is called Global Workspace Dynamics (GWD) (Baars et al., 2013; Baars and Geld, 2019) precisely because the cortex is viewed as a “unified oscillatory machine” (Steriade, 1999). GWT therefore joins other theories in viewing consciousness as the product of highly integrated and widespread cortico-thalamic (C-T) activity, following a long trail of evidence (Dehaene et al., 1998). Here we aim to clarify some empirical questions that have been raised, and review evidence that the prefrontal and posterior regions support dynamic global workspace functions, in agreement with several other authors. Static, gross anatomical divisions are superseded by the dynamical connectome of cortex. We aim to correct the following misunderstandings. In a recent paper, Raccah et al. (2021) claimed that the prefrontal cortex (PfC) is not causally involved in enabling consciousness, based on a review of intracranial electrical stimulation (iES) experiments.We will show that Raccah et al.’s claim that the prefrontal cortex (PfC) does not support consciousness is incorrect. The brain evidence is now compelling that PfC indeed participates in the visual conscious stream, for example, and excellent evidence for that has emerged in recent years. We discuss the additional evidence and how that has a direct bearing on the PfC. We also respond to Raccah et al.’s (2021) mistaken claim about the role of the prefrontal cortex and GWT. GWT does not assert that the prefrontal cortex (PfC) is essential for conscious vision, nor does it deny a role for the prefrontal lobe. The 1988 version of GWT made no assertions about the role of cortex in consciousness. These claims are mistaken, and indeed, self contradictory. In addition, this integrated conception of cortex also answers counterclaims about consciousness and metacognition; therefore we address some misunderstandings about metacognition in the Global Workspace “family” of theories (Shea and Frith, 2019). Shea and Frith (2019) proposed that “The Global Workspace Needs Metacognition. ” However, in 1988 Baars already described two varieties of metacognition that are implied by GWT (Baars, 1988; Baars and Geld, 2019). Here we have three objectives.
System Development Service Quality: A Comparison of the In-House Development and the Application Outsourcing Environment

Very few studies have investigated the service quality of the system development process. In this study, an instrument based on SERVPERF's service quality dimensions, i.e., tangibles, reliability, empathy, responsiveness, and assurance, was developed to study the service quality for in-house and outsourced system development process. Analysis of the data collected from 152 system users who had also involved in the development of those systems supported the validity and reliability of the newly developed measures. The relative contribution of the individual service quality dimensions to the overall system development service quality was found to vary between the outsourcing and in-house group, so as the impact of the overall system development service quality on the perceived usefulness and perceived ease of use. These results suggest that users may evaluate the services provided by outsourcing vendors and in-house IT personnel differently.
Industrial Location: Principles, Practice and Policy

Location is vital to the efficiency and profitability of industrial activity. Industrial Location presents a comprehensive introduction to and critical review of this field of growing academic and business interest. In business, the right choices have to be made to produce profit. Industrial location is a fixed investment, crucial to the strategy and capital investment of any organization. Location also impacts upon non-investors, directly affecting employment, the environment, and economic activity in the locale. Focusing chiefly on the United States, but drawing on an international range of cases, the authors explain the economic, social and political forces which have shaped comtemporary patterns of industrialization and examines the changing nature of production and systems.