Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing neural networks through growth

In the paper a new method to develop neural networks is proposed. The method is based on the idea of growth. Usually neural networks have been designed to be a large number of neurons with regular interconnections and adaptation to a speci c problem has been implemented through sophisticated learning algorithms. In the proposed approach the adaptation to a speci c problem has been implemented partly during the development of the structure of neural network. The growth starts from an initial value and the development of neural network is controlled through a set of production rules. The mechanism has a strong similarity to the nature growing process. The used control mechanism is based on Lindenmayer systems, that have been used successfully for modeling life of plants and bacteria. The growth control of neural networks is only one type of adaptation in a larger project to simulate more generally adaptive behavior.
Adaptive P2PTV with scalable video coding

In this paper, we propose a novel quality adaptation mechanisms using Scalable Video Coding (SVC) over Peer-to-Peer Television (P2PTV) architecture in order to provide maximum quality level. Our algorithms are based on the idea of dividing quality adaptation into two stages. The first stage, Layer Level Initialization (LLI), allows adapting to static resources at the peers. After streaming starts, another set of algorithms called the Layer Level Adjustment (LLA) is used to adapt the H.264/SVC streams with available real time resources of P2PTV. LLA is evaluated and compared with Progressive Quality Adaptation (PQA) on Quality of PSNR metric using PSIM simulator. Performance evaluation demonstrates that LLA outperforms PQA and provides a significant improvement in the PSNR value in term of adapting the Quantization Parameter (QP) with instantaneous throughput available at the peer.
Assessing the „humorous temperament“: Construction of the facet and standard trait forms of the State-Trait-Cheerfulness-Inventory — STCI

The present paper outlines the relevance of cheerfulness, seriousness, and bad mood for humor research. A state-trait model of exhtiaratability is presented which incorporates the three concepts as both states and traits. Definitions of the concepts are undertaken utilizing a facet approach and the relationships among the three concepts are outlined. The construction strategy for the various forms of the German Version of the State-Trait- Cheerfulness-Inventory (STCI) is outlined and the following versions of the trait form will be elaborated: (a) the pilot form with 122 items (STCI-T ); (b) a component (or long) form with 106 items (STCI-T ); (c) the Standard form with 60 items (STCI-T ) and (d) the international form with 106 items (STCI-T ). The development ofthe twoforms, the replication ofthepsychometric character- istics, and the evaluation of the facet model utilized samples of German and American adults comprising more that 1,300 subjects altogether. The hypothesized facet structure emerged and appeared to be highly generaliz- able across the samples. The psychometric characteristics ofthe facets and scales appeared to be satisfactory. While there were no sex differences in any of the scales, seriousness increased steadily ofter age 40. Correspondence between seif· and peer-evaluation was examinedand turned out to be sufficiently high. The construction seemed to have been successful in promding a reliable Instrument for the assessment ofthe temperamental basis ofthe sense of humor.
Automated Template Discovery for Information Extraction from Biomedical Literature

We propose a method to automatically extract templates from biomedical literature without background knowledge. The proposed method automatically extracts verbs and templates indicating interactions between biomolecules with a large dictionary called an extensional ontology. We applied our method to two datasets: one comprised 299 full texts from Cell (1998– 2002) and 13,818 entries from OMIM (Online Mendelian Inheritance in Man); the other included 33,622 abstracts from Medline (2002). Experimental results showed that our method could extract verbs and templates that had been manually collected in related works. For extracting templates, our method only needs to prepare ontology (or dictionary) and a large body of texts. Consequently, it can be applied to those of other fields as well as the biomedical literature.
Selective Hyaluronan–CD44 Signaling Promotes miRNA-21 Expression and Interacts with Vitamin D Function during Cutaneous Squamous Cell Carcinomas Progression Following UV Irradiation

Hyaluronan (HA), the major extracellular matrix component, is often anchored to CD44, a family of structurally/functionally important cell surface receptors. Recent results indicate that UV irradiation (UVR)-induced cutaneous squamous cell carcinomas (SCC) overexpress a variety of CD44 variant isoforms (CD44v), with different CD44v isoforms appear to confer malignant SCC properties. UVR also stimulates HA degradation in epidermal keratinocytes. Both large HA polymers and their UVR-induced catabolic products (small HA) selectively activate CD44-mediated cellular signaling in normal keratinocytes and SCC cells, with all of the downstream processes being mediated by RhoGTPases (e.g., Rac1 and Rho). Importantly, we found that the hormonally active form of vitamin D 1,25(OH)2D3 not only prevents the UVR-induced small HA activation of abnormal keratinocyte behavior and SCC progression, but also enhances large HA stimulation of normal keratinocyte activities and epidermal function(s). The aim of this hypothesis and theory article is to question whether matrix HA and its UVR-induced catabolic products (e.g., large and small HA) can selectively activate CD44-mediated cellular signaling such as GTPase (Rac and RhA) activation. We suggested that large HA–CD44 interaction promotes Rac-signaling and normal keratinocyte differentiation (lipid synthesis), DNA repair, and keratinocyte survival function. Conversely, small HA–CD44 interaction stimulates RhoA activation, NFκB/Stat-3 signaling, and miR-21 production, resulting in inflammation and proliferation as well as SCC progression. We also question whether vitamin D treatment displays any effect on small HA–CD44v-mediated RhoA signaling, inflammation, and SCC progression, as well as large HA–CD44-mediated differentiation, DNA repair, keratinocyte survival, and normal keratinocyte function. In addition, we discussed that the topical application of signaling perturbation agents (e.g., Y27623, a ROK inhibitor) may be used to treat certain skin diseases displaying upregulation of keratinocyte proliferation such as psoriasis and actinic keratoses in order to correct the imbalance between Rac and RhoA signaling during various UV irradiation-induced skin diseases in patients. Finally, we proposed that matrix HA/CD44-signaling strategies and matrix HA (HAS vs. HAL or HAS → HAL)-based therapeutic approaches (together with vitamin D) may be used for the treatment of patients suffering a number of UV irradiation-induced skin diseases (e.g., inflammation, skin cancer, and chronic non-healing wounds).
Ycf93 (Orf105), a Small Apicoplast-Encoded Membrane Protein in the Relict Plastid of the Malaria Parasite Plasmodium falciparum That Is Conserved in Apicomplexa

Malaria parasites retain a relict plastid (apicoplast) from a photosynthetic ancestor shared with dinoflagellate algae. The apicoplast is a useful drug target; blocking housekeeping pathways such as genome replication and translation in the organelle kills parasites and protects against malaria. The apicoplast of Plasmodium falciparum encodes 30 proteins and a suite of rRNAs and tRNAs that facilitate their expression. orf105 is a hypothetical apicoplast gene that would encode a small protein (PfOrf105) with a predicted C-terminal transmembrane domain. We produced antisera to a predicted peptide within PfOrf105. Western blot analysis confirmed expression of orf105 and immunofluorescence localised the gene product to the apicoplast. Pforf105 encodes a membrane protein that has an apparent mass of 17.5 kDa and undergoes substantial turnover during the 48-hour asexual life cycle of the parasite in blood stages. The effect of actinonin, an antimalarial with a putative impact on post-translational modification of apicoplast proteins like PfOrf105, was examined. Unlike other drugs perturbing apicoplast housekeeping that induce delayed death, actinonin kills parasites immediately and has an identical drug exposure phenotype to the isopentenyl diphosphate synthesis blocker fosmidomycin. Open reading frames of similar size to PfOrf105, which also have predicted C-terminal trans membrane domains, occur in syntenic positions in all sequenced apicoplast genomes from Phylum Apicomplexa. We therefore propose to name these genes ycf93 (hypothetical chloroplast reading frame 93) according to plastid gene nomenclature convention for conserved proteins of unknown function.
Interval Type-2 Recurrent Fuzzy Neural System for Nonlinear Systems Control Using Stable Simultaneous Perturbation Stochastic Approximation Algorithm

This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function), for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function (AFMF) and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information of the IT2RFNS-A is not easy to obtain due to the asymmetric membership functions and interval valued sets. The corresponding stable learning is derived by simultaneous perturbation stochastic approximation (SPSA) algorithm which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results for the chaotic system identification and the control of Chua's chaotic circuit are shown to illustrate the feasibility and effectiveness of the proposed method.
Efficient Fully-Parallel LDPC Decoder Design with Improved Simplified Min-Sum Algorithms

In this paper we introduce an area and power efficient fully-parallel LDPC decoder design, which keeps the BER performance while consuming less hardware resources and lower power compared with conventional decoders. For this decoder, we firstly propose two improved simplified min-sum algorithms, which enable the decoder to reduce the hardware implementation complexity and area: hardware consumption of check operation module is reduced by 40%, while achieving a negligible performance loss compared with the general min-sum algorithm. To reduce the power dissipation of the decoder, we also proposed a power-saved strategy, according to which the message evolution halts as the parity-check condition is satisfied. This strategy reduces more than 50% power under good channel condition. The synthesis result in 0.18 μm CMOS technology shows our decoder based on (648,540) irregular LDPC code of WLAN (802.1 1n) protocol achieves 810 [Mbps] throughput with 283 [mW] power consumption.
Higher Coordination With Less Control—A Result of Information Maximization in the Sensorimotor Loop

This work presents a novel learning method in the context of embodied artificial intelligence and self-organization, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive information in the sensorimotor loop. It is evaluated on robot chains of varying length with individually controlled, noncommunicating segments. The comparison of the results shows that maximizing the predictive information per wheel leads to a higher coordinated behavior of the physically connected robots compared with a maximization per robot. Another focus of this article is the analysis of the effect of the robot chain length on the overall behavior of the robots. It will be shown that longer chains with less capable controllers outperform those of shorter length and more complex controllers. The reason is found and discussed in the information-geometric interpretation of the learning process.
Error-Resilient Machine Learning in Near Threshold Voltage via Classifier Ensemble

In this paper, we present the design of error-resilient machine learning architectures by employing a distributed machine learning framework referred to as classifier ensemble (CE). CE combines several simple classifiers to obtain a strong one. In contrast, centralized machine learning employs a single complex block. We compare the random forest (RF) and the support vector machine (SVM), which are representative techniques from the CE and centralized frameworks, respectively. Employing the dataset from UCI machine learning repository and architectural-level error models in a commercial 45 nm CMOS process, it is demonstrated that RF-based architectures are significantly more robust than SVM architectures in presence of timing errors due to process variations in near-threshold voltage (NTV) regions (0.3 V - 0.7 V). In particular, the RF architecture exhibits a detection accuracy (P_{det}) that varies by 3.2% while maintaining a median P_{det} > 0.9 at a gate level delay variation of 28.9% . In comparison, SVM exhibits a P_{det} that varies by 16.8%. Additionally, we propose an error weighted voting technique that incorporates the timing error statistics of the NTV circuit fabric to further enhance robustness. Simulation results confirm that the error weighted voting achieves a P_{det} that varies by only 1.4%, which is 12X lower compared to SVM.
AVQ Overview The AVQ represents a series of tuple values in a relational database

Most data-intensive applications are confronted with the problems of I/O bottleneck, poor query processing times and space requirements. Database compression has been discovered to alleviate the I/O bottleneck, reduce disk space, improve disk access speed, speed up query, reduce overall retrieval time and increase the effective I/O bandwidth. However, random access to individual tuples in a compressed database is very difficult to achieve with most available compression techniques. We propose a lossless compression technique called non-differential augmented vector quantization, a close variant of the novel augmented vector quantization. The technique is applicable to a collection of tuples and especially effective for tuples with many low to medium cardinality fields. In addition, the technique supports standard database operations, permits very fast random access and atomic decompression of tuples in large collections. The technique maps a database relation into a static bitmap index cached access structure. Consequently, we were able to achieve substantial savings in space by storing each database tuple as a bit value in the computer memory. Important distinguishing characteristics of our technique is that individual tuples can be compressed and decompressed, rather than a full page or entire relation at a time, (b) the information needed for tuple compression and decompression can reside in the memory or at worst in a single page. Promising application domains include decision support systems, statistical databases and life databases with low cardinality fields and possibly no text fields.
The disease burden of chronic obstructive pulmonary disease in Greece

Background The objective of the study was to estimate the self-reported prevalence of chronic obstructive pulmonary disease (COPD) in Greece and to quantify its burden on patients’ daily activities, productivity, and psychological status. Methods A population-based, random digit-dialed telephone nationwide survey was conducted between July 10, 2015 and July 31, 2015 in order to recruit patients with COPD in Greece. Among the 11,471 persons contacted, 3,414 met the inclusion criterion of age ≥40 years and completed the screening questions regarding COPD. Of the 362 subjects who reported that they had been diagnosed with COPD, 351 completed the survey. Data on demographic and lifestyle characteristics, comorbidities, disease history, perceived disease severity, breathlessness severity, symptoms severity, limitations in daily activities, psychological distress, and productivity were collected. All data were collected through the telephone interview method using a structured questionnaire. Results The overall self-reported COPD prevalence was 10.6%. Among 351 participants, only 9% reported that they suffered from severe breathlessness. The mean COPD assessment test score was 19.0, with 84% of participants having a COPD assessment test score ≥10. As for the perceived severity of COPD, the majority of subjects considered that their respiratory condition was of moderate (34.2%) or mild severity (33.9%). Overall, the participants reported a significant impact of COPD on their daily life. For instance, 61.5% of them reported that their respiratory condition has affected their sports activities. Moreover, 73% of subjects considered that the health care system could do more for them than it actually does. Almost one-fourth of the participants reported that they had missed work during the past 12 months due to their respiratory symptoms, with the mean number of days lost being 10. Conclusion This survey provides insightful data regarding the impact of COPD on Greek patients’ everyday life, psychology, and productivity, revealing the increased individual morbidity and the significant burden of this condition on society.
Notice of Violation of IEEE Publication PrinciplesLocation Based Hybrid Indexing Structure - R k-d Tree

Location based spatial object selection and searching is emerging as an important search paradigm in indexing of multimedia database systems. The technique used is to map the objects as points into a two dimensional space which is indexed using a multidimensional data structure. Although several data structures have been proposed for location and spatial indexing, none of them is known to index points in both overlapping and non-overlapping regions in an optimum query retrieval time. This paper introduces the hybrid tree - a multidimensional data structure for indexing two dimensional location spaces. Unlike other multidimensional data structures, the hybrid tree cannot be classified as either a pure data partitioning (DP) index structure (e.g., R-tree, SS-tree, SR tree) or a pure space partitioning (SP) one (e.g., KD tree, hB tree);rather, it “combines” positive aspects of the two types of index structures a single data structure to achieve search performance is increased by a time complexity of O (nlog(log n)) than either of the above techniques (hence, the name “hybrid”). Our experiments on “real” distributed large size spatial databases demonstrate that the fan out is independent of dimensionality and enables fast intra node search. It significantly outperforms purely k-d tree space partitioning and R-tree data partitioning-based index mechanisms as well as linear scan at all dimensionalities for large sized databases.
The role of bridge jobs in the retirement patterns of older Americans in the 1990s

A method and apparatus for making a multi-ply paper sheet wherein a first web is formed by wet-laying fibres and a second web is formed by dry-laying fibres, the two webs being combined to form a multi-ply sheet and wherein the dry-laid web is produced by depositing dry fibres on a supporting band and consolidating the web by hot moist pressing before transferring to the first wet-laid web.
Data Retrieval Mechanisms in Mobile Environments by Using Spatial Queries

------------------------------------------------------------------------ABSTRACT--------------------------------------------------------This work discusses a novel hybrid replacement policy to adopt in the design of our cache. According to our hybrid caching strategy, the results of the most frequently accessed queries are maintained in a static cache of fixed size, which is completely rebuilt at fixed time intervals. Only the queries that cannot be satisfied by the static cache compete for the use of a dynamic cache. Our hybrid cache represents an effective and fast way to address both recency, and frequency of occurrences criteria. While the static cache maintains results of queries that are globally frequent, a simple and fast policy like LRU, which only takes into account query reference recency,could be adopted for the dynamic cache. This novel representation and the associated creation algorithm result in more effective EVRs of window queries. In addition, due to the distinct characteristics, a separate index structures, namely EVR-tree and grid index, for NN queries and window queries, respectively are used. To further increase efficiency, an algorithms to exploit the results of NN queries to aid grid index growth, benefiting EWV creation of window queries is developed. Similarly, the grid index is utilized to support NN query answering and EVR updating. Several experiments for performance evaluation are performed. The experimental results show that the proposed approach significantly outperforms the existing proxy-based approaches.
Dependence of thermal resistance on ambient and actual temperature

We investigate the temperature dependence of thermal resistance. We extract the thermal resistance as a function of ambient temperature. The increase of thermal resistance due to self-heating leads to a non-linear relation between temperature and power dissipation. We show how to implement this in a compact model and what its effect is on simulations at high power dissipation.
Motivation, Social Identity and Ideology Conviction in OSS Communities: The Mediating Role of Effort Intensity and Goal Commitment

Research has found salient individual and social motivating factors that influence participants’ involvement and contribution to OSS projects. Yet, these factors were examined independently from each other, and the mechanisms of their impacts were unclear. This paper is a first attempt to have a better and complete understanding of the phenomenon. We theorize an integrated model on the effects of personal motivation, social identity and ideology conviction on individuals’ task performance in and satisfaction with open source software projects. In particular, we posit that these motivating factors are translated into performance and satisfaction through effort intensity and goal commitment. Also, we contend that task performance positively affects an individual’s satisfaction with a specific OSS project.
The Association between Body Mass Index and Mortality in Incident Dialysis Patients

Objectives To study the body mass index (BMI) trajectory in patients with incident end-stage kidney disease and its association with all-cause mortality. Methods This longitudinal cohort study included 17022 adult patients commencing hemodialysis [HD] (n = 10860) or peritoneal dialysis [PD] (n = 6162) between 2001 and 2008 and had ≥6-month follow-up and ≥2 weight measurements, using the Australia and New Zealand Dialysis and Transplant Registry data. The association of time-varying BMI with all-cause mortality was explored using multivariate Cox regression models. Results The median follow-up was 2.3 years. There was a non-linear change in the mean BMI (kg/m2) over time, with an initial decrease from 27.6 (95% confidence interval [CI]: 27.5, 27.7) to 26.7 (95% CI: 26.6, 26.9) at 3-month, followed by increments to 27.1 (95% CI: 27, 27.2) at 1-year and 27.2 (95% CI: 26.8, 27.1) at 3-year, and a gradual decrease subsequently. The BMI trajectory was significantly lower in HD patients who died than those who survived, although this pattern was not observed in PD patients. Compared to the reference time-varying BMI category of 25.1–28 kg/m2, the mortality risks of both HD and PD patients were greater in all categories of time-varying BMI <25 kg/m2. The mortality risks were significantly lower in all categories of time-varying BMI >28.1 kg/m2 among HD patients, but only in the category 28.1–31 kg/m2 among PD patients. Conclusions BMI changed over time in a non-linear fashion in incident dialysis patients. Time-varying measures of BMI were significantly associated with mortality risk in both HD and PD patients.