Miracle-dz/UniFAR-FTUs huggingface dataset:
Hepato-renal protective effects of gallic acid and p-coumaric acid in nicotinamide/streptozotocin-induced diabetic rats

The goal of diabetes treatment is primarily to save life and alleviate symptoms and secondary to prevent long-term diabetic complications resulting from hyperglycemia. Thus, our present investigation was designed to evaluate the hepato-renal protective effects of gallic acid and p -coumaric acid in nicotinamide / streptozotocin (NA / STZ)-induced diabetic rats. Experimental type 2 diabetes was induced by a single intraperitoneal (i.p.) injection of STZ (65 mg/kg b.wt.), after 15 min of i.p. injection of NA (120 mg/kg b.wt.). Gallic acid and p -coumaric acid were orally administered to diabetic rats at a dose of 20, 40 mg/kg b.wt. / day, respectively, for 6 weeks. Body weight, serum glucose, protein profile, liver function enzymes and kidney function indicators was assayed. Treatment with either gallic acid or p- coumaric acid significantly ameliorated the elevated levels of glucose, alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea and uric acid. Both compounds were also found to restore total protein, albumin, and globulin as well as body weight of diabetic rats to near normal values. It can conclude that both gallic acid and p -coumaric acid have potent hypoglycemic and hepato-renal protective effects in diabetic rats. Therefore, our results suggest promising hypoglycemic agents that can attenuate the progression of diabetic hepatopathy and nephropathy.
Exploratory Study of Anger and Behavior in an Incarcerated Offe nder Population Utilizing the Mahan and DiTomasso Anger Scale

The present study proposed to advance the treatment and assessment of anger disorders by exploring the properties of the Mahan and DiTomasso Anger Scale (MAD-AS) Previous research of the reliability, validity, and factor structure of the MAD-AS was conducted with inpatient (Mahan, 2001) and outpatient participants (Beardmore, 2003) . In the present study a psychometric investigation was undertaken utilizing 300 male incarcerated offenders. The MAD-AS correlated positively with the presence of antisocial and borderline personality disorders and with violent offenses. Those inmates with a history of violent offenses scored significantly higher than those convicted of nonviolent offenses on the MAD-AS. The MAD-AS possessed sound psychometric properties in terms of reliability and validity. Results indicated the MAD-AS reflects the multidimensional quality of anger including its cognitive, physiological, and behavioral components. In clinical forensic work the MAD-AS may assist in identifying dynamic criminogenic needs, selecting interventions to address those needs, monitoring treatment outcomes, and assessing risk factors for recidivism.
Target setting for indirect processes: a new hybrid method for the continuous improvement management of indirect processes

Abstract Indirect processes are increasingly contributing to the total cost of production in highly competitive and technology-intensive industries. Unfortunately, they are less assessable than direct processes due to the complex organisational management structure. Therefore, companies seeking to make improvements in indirect areas need decision support methods to indicate which indirect process needs to be improved and to what extent. To facilitate this task, the target setting for indirect processes (TSIP) method has been developed following the constructive research approach. TSIP is a combination of process modelling, the analytic network process, activity-based management from managerial accounting research and the value control chart from target costing research in a kaizen budgeting framework. This new hybrid method is developed and validated in cooperation with a global first-tier automotive supplier.
SNF-REPORT NO . 37 / 01 Effects of Regional Agglomeration of Salmon Aquaculture on Production Costs by Ragnar Tveterås

During the last decade empirical evidence of regional agglomeration economies has emerged for some industries. This report argues that externalities from agglomeration are not only present in some manufacturing and service sectors, but can also occur in primary industries such as aquaculture. Econometric analyses in this literature have primarily estimated production functions on aggregated industry data. Here, cost functions are estimated on firm level observations of Norwegian salmon aquaculture farms. This approach provides us with measures of the cost savings due to agglomeration externalities. Furthermore, we avoid aggregation biases and can test a richer set of hypotheses on how these externalities affect the structure of costs. According to the econometric estimates there are significant cost savings associated with localization in regions with a large salmon aquaculture industry, suggesting the presence of positive agglomeration externalities. The econometric results imply that there are significant welfare gains to be made from changes in the government regulation of the industry.
Heterotrophic bacterial flora associated with European eel Anguilla anguilla reared in freshwater

The heterotrophic bacterial microflora of European eel reared in freshwater and its surround-ing aqueous environment is reported. Quality of eel culture water is also discussed with respect to its physicochemical and bacteriological characteristics. The presence of bacterial species pathogenic for eel and its serological classification was investigated in fishery water and eels. Overall 675 isolates from fishery water and healthy or diseased were eel identified at genus level. Pseudo-monas was the most frequently encountered genus. Moraxella sp. and Acinetobacter sp. were abundant in fishery water, and Aeromonas sp. in elver and internal organs of silver eel. Plesiomonas sp. , Escherichia sp. , Enterobacter sp. , Serratia sp. , Citrobacter sp. , and Streptococcus sp. were minority genera that were only recovered from eel. The species Aeromonas hydrophila, A. sobria, and Alteromonas putrefaciens were mostly isolated from diseased eel whereas other potential fish pathogens such as P. fluorescens, V. anguillarum, and Plesiomonas shigelloides were generally observed in fishery water or healthy eel. Following the typing scheme of Guinee and Jensen, most of the isolates of A. hydrophila from diseased European eels, as well as the strain A. hydrophila Y-62 pathogenic for Japanese eel, belonged to O19 serogroup. A relationship between origin and serogroup for A. sobria was not observed. None Vibrio anguillarum isolates agglutinated with O1 and O2 antisera of Sorensen and Larsen.
A Note on Lazy Training in Supervised Differentiable Programming

In a series of recent theoretical works, it has been shown that strongly over-parameterized neural networks trained with gradient-based methods could converge linearly to zero training loss, with their parameters hardly varying. In this note, our goal is to exhibit the simple structure that is behind these results. In a simplified setting, we prove that "lazy training" essentially solves a kernel regression. We also show that this behavior is not so much due to over-parameterization than to a choice of scaling, often implicit, that allows to linearize the model around its initialization. These theoretical results complemented with simple numerical experiments make it seem unlikely that "lazy training" is behind the many successes of neural networks in high dimensional tasks.
Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time. Given that it is impractical to train separate policies to accommodate all situations the agent may see in the real world, this work proposes to learn how to quickly and effectively adapt online to new tasks. To enable sample-efficient learning, we consider learning online adaptation in the context of model-based reinforcement learning. Our approach uses meta-learning to train a dynamics model prior such that, when combined with recent data, this prior can be rapidly adapted to the local context. Our experiments demonstrate online adaptation for continuous control tasks on both simulated and real-world agents. We first show simulated agents adapting their behavior online to novel terrains, crippled body parts, and highly-dynamic environments. We also illustrate the importance of incorporating online adaptation into autonomous agents that operate in the real world by applying our method to a real dynamic legged millirobot. We demonstrate the agent's learned ability to quickly adapt online to a missing leg, adjust to novel terrains and slopes, account for miscalibration or errors in pose estimation, and compensate for pulling payloads.
Eigenspace conditions for homomorphic sensing

Given two endomorphisms τ1,τ2 of ℂm, we provide eigenspace conditions under which τ1(v1)=τ2(v2) for v1,v2∈ can only be true if v1=v2, where  is a general n-dimensional subspace of ℂm for some n≤m/2. As a special case, we show that these eigenspace conditions are true when the endomorphisms are permutations composed with coordinate projections, leading to an abstract proof of the recent unlabeled sensing theorem of Unnikrishnan et al.
SDN - Architectural Enabler for Reliable Communication Over Millimeter-Wave 5G Networks

Millimeter-wave (mmWave) frequency bands offer a new frontier for next-generation wireless networks, popularly known as 5G, to enable multi-gigabit communication; however, the availability and reliability of mmWave signals are significantly limited due to its unfavorable propagation characteristics. Thus, mmWave networks rely on directional narrow-beam transmissions to overcome severe path-loss. To mitigate the impact of transmission-reception directionality and provide uninterrupted network services, ensuring the availability of mmWave transmission links is important. In this paper, we proposed a new flexible network architecture to provide efficient resource coordination among serving basestations during user mobility. The key idea of this holistic architecture is to exploit the software-defined networking (SDN) technology with mmWave communication to provide a flexible and resilient network architecture. Besides, this paper presents an efficient and seamless uncoordinated network operation to support reliable communication in highly-dynamic environments characterized by high density and mobility of wireless devices. To warrant high-reliability and guard against the potential radio link failure, we introduce a new transmission framework to ensure that there is at least one basestation is connected to the UE at all times. We validate the proposed transmission scheme through simulations.
Training on Art Composition Attributes to Influence CycleGAN Art Generation

I consider how to influence CycleGAN, image-to-image translation, by using additional constraints from a neural network trained on art composition attributes. I show how I trained the the Art Composition Attributes Network (ACAN) by incorporating domain knowledge based on the rules of art evaluation and the result of applying each art composition attribute to apple2orange image translation.
Wav2Letter++: A Fast Open-source Speech Recognition System

This paper introduces wav2letter++, a fast open-source deep learning speech recognition framework. wav2letter++ is written entirely in C++, and uses the ArrayFire tensor library for maximum efficiency. We explain the architecture and design of the wav2letter++ system and compare it to other major open-source speech recognition systems. In some cases wav2letter++ is more than 2× faster than other optimized frameworks for training end-to-end neural networks for speech recognition. We also show that wav2letter++ training times scale linearly to 64 GPUs, the most we tested, for models with 100 million parameters. High-performance frameworks enable fast iteration, which is often a crucial factor in successful research and model tuning on new datasets and tasks.
Angle-Encoded Swarm Optimization for UAV Formation Path Planning

This paper presents a novel and feasible path planning technique for a group of unmanned aerial vehicles (DAVs) conducting surface inspection of infrastructure. The ultimate goal is to minimise the travel distance of DAVs while simultaneously avoid obstacles, and maintain altitude constraints as well as the shape of the UAV formation. A multiple-objective optimisation algorithm, called the Angle-encoded Particle Swarm Optimization (θ- PSO) algorithm, is proposed to accelerate the swarm convergence with angular velocity and position being used for the location of particles. The whole formation is modelled as a virtual rigid body and controlled to maintain a desired geometric shape among the paths created while the centroid of the group follows a pre-determined trajectory. Based on the testbed of 3DR Solo drones equipped with a proprietary Mission Planner, and the Internet-of- Things (loT) for multi-directional transmission and reception of data between the DAV s, extensive experiments have been conducted for triangular formation maintenance along a monorail bridge. The results obtained confirm the feasibility and effectiveness of the proposed approach.
Detecting Standard Violation Errors in Smart Contracts

We present SOLAR, a new analysis tool for automatically detecting standard violation errors in Ethereum smart contracts.Given the Ethereum Virtual Machine (EVM) bytecode of a smart contract and a user specified constraint or invariant derived from a technical standard such as ERC-20,SOLAR symbolically executes the contract, explores all possible execution paths, and checks whether it is possible to initiate a sequence of malicious transactions to violate the specified constraint or invariant. Our experimental results highlight the effectiveness of SOLAR in finding new errors in smart con-tracts. Out of the evaluated 779 ERC-20 and 310 ERC-721smart contracts, SOLAR found 255 standard violation errors in 197 vulnerable contracts with only three false positives.237 out of the 255 errors are zero-day errors that are not re-ported before. Our results sound the alarm on the prevalence of standard violation errors in critical smart contracts that manipulate publicly traded digital assets
Very Power Efficient Neural Time-of-Flight

Time-of-Flight (ToF) cameras require active illumination to obtain depth information thus the power of illumination directly affects the performance of ToF cameras. Traditional ToF imaging algorithms are very sensitive to illumination and the depth accuracy degenerates rapidly with the power of it. Therefore, the design of a power efficient ToF camera always creates a painful dilemma for the illumination and the performance trade-off. In this paper, we show that despite the weak signals in many areas under extreme short exposure setting, these signals as a whole can be well utilized through a learning process which directly translates the weak and noisy ToF camera raw to depth map. This creates an opportunity to tackle the aforementioned dilemma and make a very power efficient ToF camera possible. To enable the learning, we collect a comprehensive dataset under a variety of scenes and photographic conditions by a specialized ToF camera. Experiments show that our method is able to robustly process ToF camera raw with the exposure time of one order of magnitude shorter than that used in conventional ToF cameras. In addition to evaluating our approach both quantitatively and qualitatively, we also discuss its implication to designing the next generation power efficient ToF cameras.
AME Blockchain: An Architecture Design for Closed-Loop Fluid Economy Token System

In this white paper, we propose a blockchain-based system, named AME, which is a decentralized infrastructure and application platform with enhanced security and self-management properties. The AME blockchain technology aims to increase the transaction throughput by adopting various optimizations in network transport and storage layers, and to enhance smart contracts with AI algorithm support. We introduce all major technologies adopted in our system, including blockchain, distributed storage, P2P network, service application framework, and data encryption. To properly provide a cohesive, concise, yet comprehensive introduction to the AME system, we mainly focus on describing the unique definitions and features that guide the system implementation.
Lexical analysis of automated accounts on Twitter

In recent years, social bots have been using increasingly more sophisticated, challenging detection strategies. While many approaches and features have been proposed, social bots evade detection and interact much like humans making it difficult to distinguish real human accounts from bot accounts. For detection systems, various features under the broader categories of account profile, tweet content, network and temporal pattern have been utilised. The use of tweet content features is limited to analysis of basic terms such as URLs, hashtags, name entities and sentiment. Given a set of tweet contents with no obvious pattern can we distinguish contents produced by social bots from that of humans? We aim to answer this question by analysing the lexical richness of tweets produced by the respective accounts using large collections of different datasets. Our results show a clear margin between the two classes in lexical diversity, lexical sophistication and distribution of emoticons. We found that the proposed lexical features significantly improve the performance of classifying both account types. These features are useful for training a standard machine learning classifier for effective detection of social bot accounts. A new dataset is made freely available for further exploration.
Rigid Body Structure and Motion From Two-Frame Point-Correspondences Under Perspective Projection

This paper is concerned with possibility of recovery of motion and structure parameters from multiframes under perspective projection when only points on a rigid body are traced. Free (unrestricted and uncontrolled) pattern of motion between frames is assumed. The major question is how many points and/or how many frames are necessary for the task. It has been shown in an earlier paper {Klopotek:95b} that for orthogonal projection two frames are insufficient for the task. The paper demonstrates that, under perspective projection, that total uncertainty about relative position of focal point versus projection plane makes the recovery of structure and motion from two frames impossible.
Explanatory Graphs for CNNs

This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN. Each filter in a conv-layer of a CNN for object classification usually represents a mixture of object parts. We develop a simple yet effective method to disentangle object-part pattern components from each filter. We construct an explanatory graph to organize the mined part patterns, where a node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns. More crucially, given a pre-trained CNN, the explanatory graph is learned without a need of annotating object parts. Experiments show that each graph node consistently represented the same object part through different images, which boosted the transferability of CNN features. We transferred part patterns in the explanatory graph to the task of part localization, and our method significantly outperformed other approaches.
Birth mode and infectious morbidity risks in Qom children of Argentina

Cesarean delivery may increase childhood infectious morbidity risks via altered birth exposures and subsequent immune, microbial, and epigenetic development. Many Latin American indigenous populations experience dual burdens of infectious and chronic diseases, and are particularly vulnerable to rising rates of cesarean delivery and associated adverse outcomes. The Qom/Toba are an indigenous population in Argentina experiencing rapid lifestyle transitions. We hypothesized that cesarean delivery would be associated with increased risk of infectious symptoms in Qom children after adjusting for gestational and nutritional factors.
Synthesis, characterization and use of iron oxide nano particles for antibacterial activity

In this decade, the use of nano particles (NPs) against bacterial growth is increasing day by day due to remarkable alternative properties compared to molecular antibiotics. Thus, the use of iron oxide nanoparticles (IONPs) has proven one of the most important transition metals oxide‐based remedy in nanotechnological advances and biological applications due to enriched biocompatibility of iron. In this study synthesis of IONPs was carried out via co‐precipitation method. The crystallographic morphology of the synthesized particles was studied via X‐ray diffraction which revealed cubic structure of the particles, whereas, the spinal shaped morphology of the prepared NPs was confirmed from scanning electron microscopy. Likewise, the presence of the major elements in the sample was determined through energy dispersive X‐ray analysis characterization. Bactericidal effect of the NPs was assessed at pre‐defined concentrations (50 and 100 μg/ml) against Gram +ve bacteria Staphylococcus aureus, Gram −ve bacteria Shigella dysentry and Escherichia coli. Bacterial strains, which demonstrate the potential of NPs. The purpose of this study was assessing the structure of the synthesized NPs for protective effect against harmful bacterial activity.
The Prevalence of Dementia and Cognitive Impairment in Hospitals.

BACKGROUND The care of elderly patients with comorbid dementia poses an increasing challenge in the acute inpatient setting, yet there remains a lack of representative studies on the prevalence and distribution of dementia in general hospitals. METHODS We conducted a cross-sectional study of patients aged 65 and older in randomly selected general hospitals in southern Germany. Patients were excluded if they were in an intensive care unit or isolation unit or if they were on specialized wards for psychiatry, neurology, or geriatric medicine. The findings are derived from patient interviews, neuropsychological testing, standardized rating scales, questioning of nursing staff, and the patients' medical records. RESULTS 1469 patients on 172 inpatient wards of 33 hospitals were studied. 40.0% of them (95% confidence interval, [36.2; 43.7]) had at least mild cognitive impairment. The point-prevalence of dementing illnesses was 18.4% [16.3; 20.7]. Delirium, most often on the basis of dementia, was present in 5.1% [3.9; 6.7]. 60.0% had no cognitive impairment. Dementia was more common among patients of very advanced age, those who were dependent on nursing care, those who lived in old-age or nursing homes, and those with a low level of education. Among patients with dementia, only 36.7% had a documented diagnosis of dementia in the medical record. Patients with dementia were treated more often for dehydration, electrolyte disturbances, urinary tract infections, contusions, and bone fractures, as well as for symptoms and findings of an unknown nature, and much less often for cancer or musculoskeletal diseases. CONCLUSION Two out of five elderly patients in general hospitals suffer from a cognitive disturbance. Patients with severe impairments such as dementia or delirium often need special care. Guidelines and model projects offer approaches by which the inpatient care of patients with comorbid dementia can be improved.