Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of 9T SRAM Cell at Various Process Corners at Deep Sub-micron Technology for Multimedia Applications

In the past decades CMOS IC technologies have been constantly scaled down and at present they aggressively entered in the nanometer regime. Amongst the wide-ranging variety of circuit applications, integrated memories especially the SRAM cell layout has been significantly reduced. As it is very well know the reduction of size of CMOS involves an increase in physical parameters variation, this is a factor which has a direct impact on SRAM cell stability. Polysilicon and diffusion critical dimensions (CD) together with implant variations are the main causes of mismatch in SRAM cells. SRAM memory cells have always been designed to occupy the minimum amount of silicon area consistent with the performance and reliability required. Today's system on Chip (SoC) trends result in a major percentage of the total die area being dedicated to memory blocks, consequently making SRAM parameter variations dominate the overall circuit parameter characteristics, including leakage, process variation effects, etc. The reliability is usually measured by static noise margin, SNM (1), and write trip point simulations and measurements. In this paper we have analyzed the stability of the 9T SRAM cell at SS, FF, TT, FS, SF corners. The simulations have been done at 45nm technology.
Expression Profiles of Fungal Cytochrome P450 Genes: A Mini Review

The authors describe the updates on expression of cytochrome P450 genes of fungi (mold and yeast) at mRNA level aiming to facilitate biotechnological application of these versatile biocatalysts in the industry for a series of applications like drug discovery, xenobiotic detoxification and/or bioremediation. This mini-review highlights how to solve the problems that may arise during studying expression profiles of P450s in liquid media, being the crucial step for stepping forward with the genes. Present knowledge on steps to be cared while conducting experiments for P450 clone library construction are summarized. Also, experimental evidences on expression profile of P450 genes in yeast using liquid media have been focused.
Testosterone, estradiol and their ratio in male patients with acute coronary syndrome

The role of androgens and estrogens in cardiovascular disease is a subject of ongoing studies. Therefore, we aim to explore the association of testosterone (T), estradiol and their ratio with cardiovascular risk factors and severity of cardiac ischemia in male patients with acute coronary syndrome. (ACS) n = 72 and controls (n = 35) Lower values of both total and free T were found in the ACS group compared to controls (8.97 vs. 10.98 nmol/l, p = 0.001 for total T and 0.189 vs. 0.223 nmol/l, p = 0.006 for free T). Patients with myocardial infarction with ST-elevation had significantly lower T fractions compared to ACS without ST-elevation. The T to estradiol ratio was significantly lower in the ACS group compared to controls and is lowest in those with STEMI. Also, all testosterone fractions – free, bioavailable, and free T, are lower in patients with ACS compared to controls. Based on these observations a conclusion can be drawn that amore estrogenic environment is associated with ST-elevation and correlates with the severity of ACS in male patients.
Serum antimüllerian hormone predicts ovarian reserve in a monkey model

Objective: A monkey model of the menopausal transition (perimenopause) would facilitate efforts to understand better the effect of hormonal fluctuations during this life phase on the initiation of chronic diseases associated with the postmenopausal years. Antimüllerian hormone (AMH) is a promising marker of ovarian reserve (primordial follicle number) in women. Here, we describe the relationship between AMH and ovarian reserve in cynomolgus monkeys (Macaca fascicularis) estimated to be 12 to 15 years of age (∼36-45 y in women). Methods and Results: The results of daily vaginal swabbing (to detect menses) and thrice weekly blood sampling for 12 weeks indicate that AMH is relatively stable across the menstrual cycle (intraclass correlation, ∼0.80), with a slight although significant (P < 0.02) reduction (∼1.4-fold) on days 2 to 5 postovulation. Substantial interindividual variation in AMH concentrations were observed between monkeys, with values ranging from 4.46 ± 0.17 to 18.80 ± 0.71 ng/mL (mean ± SE). Antimüllerian hormone concentrations were reduced by ∼63% after the removal of one ovary (7.6 ± 0.77 vs 2.75 ± 0.37 ng/mL; P < 0.001; n = 19) and were below the level of detection after the removal of both ovaries (5.8 ± 0.42 to <0.05 ng/mL; P < 0.001; n = 84), suggesting that the ovary is likely to be either the major or the sole source of AMH in the monkey. Finally, we examined the association between AMH and primordial, primary, and secondary follicles in 29 monkeys and found significant associations with all follicle types (r = 0.78, r = 0.66, and r = 0.80, respectively; P < 0.01). Conclusions: The relationship between AMH and ovarian reserve in the monkey is similar to that in women, suggesting that monkeys may be a useful model for studying hormonal fluctuations across the menopausal transition.
Robust face recognition by an albedo based 3D morphable model

Large pose and illumination variations are very challenging for face recognition. The 3D Morphable Model (3DMM) approach is one of the effective methods for pose and illumination invariant face recognition. However, it is very difficult for the 3DMM to recover the illumination of the 2D input image because the ratio of the albedo and illumination contributions in a pixel intensity is ambiguous. Unlike the traditional idea of separating the albedo and illumination contributions using a 3DMM, we propose a novel Albedo Based 3D Morphable Model (AB3DMM), which removes the illumination component from the images using illumination normalisation in a preprocessing step. A comparative study of different illumination normalisation methods for this step is conducted on PIE and Multi-PIE databases. The results show that overall performance of our method outperforms state-of-the-art methods.
Traditional vs Federated Learning with Deep Autoencoders: a Study in IoT Intrusion Detection

Security of Internet of Things (IoT) devices and networks is a primary concern. Many intrusion detection systems (IDS) proposals in the IoT leverage machine and deep learning algorithms to learn models that can be used to discriminate normal behaviors from intrusions. Due to the dynamicity and scale of modern IoT networks, it is hard to learn and maintain one separate IDS model per device; on the other hand, the Cloud-Edge-IoT architecture allows learning a single IDS model (instead of many separate models). This paper compares two paradigms, i.e., traditional and federated, to learn a single IDS model atop the traffic of different IoT devices. The former assumes the availability of an all-in-one training dataset at a unique learning node; the latter aggregates the outcomes of independent learning procedures executed on individual training datasets hosted by different nodes. The experiments are done with a well-established public benchmark of nine IoT devices and the use of deep autoencoders. In the experiment and dataset at hand, federated learning lead to an increase of the false positive rate of six devices compared to the traditional scenario. Such an increase was balanced by a narrower variability of the false positive rate across all the devices and a mitigation of potential overfitting.
Particle swarm optimization in stationary and dynamic environments

Inspired by social behavior of bird flocking or fish schooling, Eberhartand Kennedy first developed the particle swarm optimization (PSO) algorithm in 1995. PSO, as a branch of evolutionary computation, has been successfully applied in many research and application areas in the past several years, e.g., global optimization, artificial neural network training, and fuzzy system control, etc… Especially, for global optimization, PSO has shown its superior advantages and effectiveness. Although PSO is an effective tool for global optimization problems, it shows weakness while solving complex problems (e.g., shifted, rotated, and compositional problems) or dynamic problems (e.g., the moving peak problem and the DF1 function). This is especially true for the original PSO algorithm. In order to improve the performance of PSO to solve complex problems, we present a novel algorithm, called self-learning PSO (SLPSO). In SLPSO, each particle has four different learning strategies to deal with different situations in the search space. The cooperation of the four learning strategies is implemented by an adaptive framework at the individual level, which can enable each particle to choose the optimal learning strategy according to the properties of its own local fitness landscape. This flexible learning mechanism is able to automatically balance the behavior of exploration and exploitation for each particle in the entire search space during the whole running process. Another major contribution of this work is to adapt PSO to dynamic environments, we propose an idea that applies hierarchical clustering techniques to generate multiple populations. This idea is the first attempt to solve some open issues when using multiple population methods in dynamic environments, such as, how to define the size of search region of a sub-population, how many individuals are needed in each sub-population, and how many sub-populations are needed, etc. Experimental study has shown that this idea is effective to locate and track multiple peaks in dynamic environments.
Functional and Molecular Mitochondrial Abnormalities Associated with a C T Transition at Position 3256 of the Human Mitochondrial Genome

We have previously identified a mitochondrial DNA polymorphism (a C T transition at position 3256, within the mitochondrial tRNA gene) in a patient with a multisystem disorder. Although there were several indicators suggesting a pathogenetic role for this mtDNA polymorphism, its heteroplasmic nature made functional and molecular studies difficult to interpret. We have now fused enucleated fibroblasts from the patient with a mtDNA-less cell line to generate transmitochondrial cybrids harboring different proportions of mutated and wild-type mtDNA. Individual clones harboring essentially 100% wild-type or >99% mutated mtDNAs were characterized and studied for respiratory capacity, respiratory chain enzymes activity, mitochondrial protein synthesis, and RNA steady-state levels and processing. Our results showed that cell lines containing exclusively mutated mtDNAs respire poorly, overproduce lactic acid, and have significantly impaired activity of respiratory complexes I and IV. Molecular studies showed that mutant clones have a decrease in steady-state levels of mitochondrial tRNA, and a partial impairment of mitochondrial protein synthesis and steady-state levels, suggesting that these molecular abnormalities are involved in the pathogenetic mechanism of the mtDNA 3256 mutation.
Metabolic balance in the mixed layer of the oligotrophic North Pacific Ocean from diel changes in O2/Ar saturation ratios

In situ measurements were made to determine oxygen (O2) metabolic balance in the upper oligotrophic ocean from diel changes in O2 to argon (Ar) ratios. The study took place during 13–24 March 2014, at the Hawaii Ocean Time‐series Station ALOHA (A Long‐term Oligotrophic Habitat Assessment), in the North Pacific Subtropical Gyre. Microbial community respiration and gross O2 production, estimated from in situ diel changes in O2/Ar saturation, agreed well with those calculated using other independent methods. Net oxygen production (NOP), estimated from in situ diel changes in O2/Ar saturation, showed large day‐to‐day variability. However, when averaged over the entire observational period, mean diel NOP was in relatively good agreement with the estimated mean steady state NOP (9.2 ± 9.3 mmol O2 m−2 d−1 compared to 11.7 ± 1.1 mmol O2 m−2 d−1, respectively).
Attentional modulation of sensorimotor processes in the absence of perceptual awareness

Attention modulates visual perception and is generally considered inextricably linked with conscious awareness: we become aware of stimuli as we attend to them, and we attend to stimuli as we become aware of them. Recent evidence suggests that attention can also modulate the effects of stimuli that remain invisible, and a natural explanation is that attention enhances weak perceptual representations, bringing them closer to conscious threshold even if they do not reach that threshold. However, there is also the possibility that attention may modulate neural processes that are entirely separate from those supporting conscious perception: sensorimotor mechanisms that do not create awareness however much they are enhanced. Here we provide evidence in support of this second hypothesis by showing that attentional cueing can modulate the behavioral response to invisible stimuli in a way that is distinct from enhancing their visibility. We used a masked-prime paradigm that produces a negative or positive compatibility effect depending on the perceptual strength (duration or brightness) of the prime. We found that attention enhanced the effect of both visible and invisible primes and also increased the likelihood of detecting the prime (i.e., boosted perceptual strength). Crucially, the pattern of attentional influence on priming could not be explained by attentional modulation of the prime’s perceptual strength but was predicted by a direct attentional influence on the nonconscious priming process itself. Therefore, in addition to regulating what we perceive, attention seems to influence our behavior through sensorimotor processes that are not involved in conscious awareness.
Integration of scheduling analysis into UML based development processes through model transformation

The complexity of embedded systems and their safety requirements have risen significantly in recent years. Models and the model based development approach help to keep overview and control of the development. Nevertheless, a support for the analysis of non-functional requirements, e.g. the scheduling, based on development models and consequently the integration of these analysis technologies into a development process exists only sporadically. The problem is that the analysis tools use different metamodels than the development tools. Therefore, a remodeling of the system in the format of the analysis tool or a model transformation is necessary to be able to perform an analysis. Here, we introduce a scheduling analysis view as a part of the development model, which is a MARTE annotated UML model to describe a system from the scheduling behavior point of view. In addition, we present a transformation from this annotated UML model to the scheduling analysis tool SymTA/S and a treatment of the analysis results to integrate scheduling analysis into a development process. With our approach it is not necessary to remodel the system in an analysis tool to profit from the analysis and its results. Additionally, we illustrate our approach in a case study on a parallel robot controller.
Active Disk Architecture for Databases

Abstract : Today's commodity disk drives, the basic unit of storage for computer systems large and small, are actually small computers, with a processor, memory and a network connection, in addition to the spinning magnetic material that stores the data. Large collections of data are becoming larger, and people are beginning to analyze, rather than simply store and forget, these masses of data. At the same time, advances in I/O performance have lagged the rapid development of commodity processor and memory technology. This paper describes the use of Active Disks to take advantage of the processing power on individual disk drives to run a carefully chosen portion of a relational database system. Moving a portion of the database processing to execute directly at the disk drives improves performance by: (1) dramatically reducing data traffic; and (2) exploiting the parallelism in large storage systems. It provides a new point of leverage to overcome the I/O bottleneck. This paper discusses how to map all the basic database operations - select, project, and join - onto an Active Disk system. The changes required are small and the performance gains are dramatic. A prototype based on the Postgres database system demonstrates a factor of 2x performance improvement on a small system using a portion of the TPC-D decision support benchmark, with the promise of larger improvements in more realistically-sized system.
Patching: a multicast technique for true video-on-demand services

c:nt~ now, true videwon-dernaad can ody be atievsd hg a dedicated data flow for web service request. This brute force approach is probibitivdy &\Tensive. Using mtiticast w si@cantly reduce the system rest. This solution, however, mu~t dday services in order to serve many requ~s as a hztch. h this paper, we consider a third alternative ded Pat&ing. h our technique, an e*mg mtiticast m expand dynarnidy to serve new &ents. ~otig new &ents to join an existiig rutiticast improves the ficiency of the rntiti-. ~hermor~ since W requ~s can be served immediatdy, the &ents experience no service dday md true vide+on-dem~d ~ be achieve~ A si~cant contribution of tkis work, is making mdti- work for true vide~ on-demand ssrvicw. h fact, we are able to tiate the service latency and improve the efficiency of mtiticast at the same time To assms the ben~t of this sdetne, w perform simdations to compare its performance +th that of standard rntiti-. Our simtiation rats indicate convincingly that Patching offers . wbstanti~y better perforrnace.
Designing StyleGAN2 Model for Generating 2D Pokemon Character

In this paper, we propose a 2D image generation model using StyleGAN2 applied in the creative design of publications for entertainment purposes. There are only 898 Pokemons created throughout 26 years. Therefore, we focus on generating new Pokemon to signicantly reduce their cost. We built a dataset of approximately 800 images on Kaggle, applied DiffGANAugment, and trained them using the styleGAN2 model. The Frchet inception distance (FID) score evaluates the generated images' quality. The results show that the proposed model achieves an FID score of 53.23 after 33 epochs that are applicable in real hardware.
Constraint-Based MDL Principle for Semi-Supervised Classification of Time Series

We propose a constraint-based method for the self-training process in semi-supervised classification of time series. Our constraint uses the Minimum Description Length principle to decide whether the instance should be added into the positive set or not. If the Description Length decreases when adding the new instance, we accept to add it, otherwise, we reject it. After the constraint-based self-training process, we continue to select more positive instances in the boundary of the positive set and the negative set. For the second step, we define a safe distance which is the sum of mean and standard deviation of the distances between pairs of nearest instances in the positive set. We select more instances to add into the positive set if its distance to the positive set is less than or equal to the safe distance. Experimental results show that our novel method can provide more accurate semi-supervised classifiers of time series.
Web Page Categorization Using Artificial Neural Networks

Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, entertainment, sports, news & media, job search, and science. The whole process of the proposed system is done in three successive stages. In the first stage, the features are automatically extracted through analyzing the source of the web pages. The second stage includes fixing the input values of the neural network; all the values remain between 0 and 1. The variations in those values affect the output. Finally the third stage determines the class of a certain web page out of eight predefined classes. This stage is done using back propagation algorithm of artificial neural network. The proposed concept will facilitate web mining, retrievals of information from the web and also the search engines.
Dialogue Act Semantic Representation and Classification Using Recurrent Neural Networks

In this work, we present a model that incorporates Dialogue Act (DA) semantics in the framework of Recurrent Neural Networks (RNNs) for DA classiﬁcation. Speciﬁcally, we propose a novel scheme for automatically encoding DA semantics via the extraction of salient keywords that are representative of the DA tags. The proposed model is applied to the Switchboard corpus and achieves 1 . 7% (absolute) improvement in classiﬁcation accuracy with respect to the baseline model. We demonstrate that the addition of discourse-level features enhances the DA classiﬁcation as well as makes the algorithm more robust: the proposed model does not require the preprocessing of dialogue transcriptions.
“Overreaction” and “Underreaction”:-Evidence for the Portuguese Stock Market -

In the past two decades several studies show and explain the occurrence of financial phenomena that are contrary to the Efficient Markets Hypothesis (EMH) of Fama (1970). Among them, the phenomena of “overreaction” and “underreaction”, inspired by cognitive psychology studies, are one of the most important challenges to market efficiency, and helped to build the foundations of Behavioral Finance. We investigate the existence of both these phenomena in the Portuguese Stock Market and try to conciliate their simultaneous occurrence. We thus explore whether Portuguese stock returns are related to return past performance for an extended sample (all stocks listed in the main market) and time period (16 years). We start by exploring the existence of autocorrelation in stock returns: as in previous studies we evaluate whether there is negative autocorrelation in the long run, and positive autocorrelation in the short run. We then proceed in testing whether these phenomena stem from overreaction and underreaction by investors. We use several different testing methodologies to evaluate the robustness of the results (controlling for risk and non-risk factors) and assess the validity of alternative hypotheses that have been put forward to explain continuation and reversal patterns in returns. Finally we examine our findings at the light of the predictions that come out of the theoretical behavioral models that have been developed to explain momentum and reversal in returns. Our results seem to be supportive of the overreaction hypothesis: there is negative correlation in stock returns that is robust to risk and non-risk controls. Further “value” strategies show superior performance and this performance seems to be associated with extrapolation of past sales performance. As for the short run return pattern, we find weak evidence in support of momentum effects that persist after controlling for risk. The momentum effects seem to be associated with an insufficient reaction to earnings announcements surprises. The evidence we gather for the Portuguese stock market is consistent with the results found in well researched, large, liquid developed markets. Altogether the two pieces of evidence (continuation followed by reversal in returns) might reflect the dynamic interaction between “news watchers” and “momentum traders” predicted by the behavioral model of Hong and Stein (1999).