Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cardiodynamicsgram Based Early Detection of Myocardial Ischemia Using the Lempel-Ziv Complexity

Background: Electrocardiogram (ECG) is a routine method for detecting myocardial ischemia in clinical practice, but more than half of ECGs are without specific ischemic changes. Cardiodynamicsgram (CDG) is an effective method to detect ischemia with non-diagnostic ECG. The Lyapunov exponent (LYE) and the Fourier transform coefficient are combined to characterize the spatial and temporal features of CDG. However, in some cases, the Lyapunov exponent does not accurately enough describe the degree of irregular morphology of CDG for ischemic patients. In this context, this study aims to improve the characterization of CDG using the Lempel-Ziv (LZ) complexity instead of the Lyapunov exponent. Methods: The cardiodynamics information inside ECG is extracted via deterministic learning from the ST-T segments of ECG and then the CDG is generated by plotting the extracted three-dimensional cardiodynamics information. The Lyapunov exponent and LZ complexity are calculated from CDG and coupled with the Fourier transform coefficient respectively to construct the LYE model and LZ model for detecting myocardial ischemia. Results: 393 subjects presenting non-diagnostic ECG are enrolled in the study. 312 of them are ischemic patients selected as the myocardial ischemia group, and the other 81 non-ischemic subjects are selected as the healthy control group. The average sensitivity, specificity, and accuracy of the LYE model and the LZ model are 90.7% vs 93.4%, 86.4% vs 86.8%, and 89.0% vs 90.8%, respectively. Meanwhile, the proposed method achieves better performance on the PTB database than most of the previous studies in detecting ischemia or infarction. Conclusion: The results indicate that LZ complexity can accurately characterize the cases that cannot be accurately depicted by Lyapunov exponent, and the corresponding model is more accurate for the early detection of myocardial ischemia.
Multispectral Facial Recognition: A Review

Multispectral images are images with more than one channel acquired in different bands or spectral ranges of the electromagnetic spectrum. Each one has specific details that can be exploited in facial recognition applications. In particular, to detect facial expression variations, pose variations and presentation attacks, a facial analysis system can benefit not only of images from the visible spectral band but also of infrared images. In this paper we perform a review of the state of the art methods used in multispectral facial recognition using images from the visible spectral band and also from the Near Infrared, Short Wavelength Infrared and Long Wavelength Infrared sub-bands. The public multispectral databases for facial analysis are identified, and a comparison is made, taking into consideration their specifications. The multispectral facial recognition methods are classified according to their basic working principle, from the traditional Fusion and Subspace methods to the more recent Deep Neural Networks.
Improved Normalized Probabilistic Minimum Summation Algorithm for LDPC Decoding

The hardware design of the probabilistic min-sum algorithm can be efficiently realized by group searching. Although it can accelerate calculation and reduce hardware complexity, it degrades error correction capability. Thus, an improved method for simplified min-sum algorithm is proposed in this paper. In order to recover the error correction capability, we apply the accurate first minimum for adjusting the reliability of the probabilistic second minimum. Maintaining the value of the first minimum and proportionally combining the first two minima can flexibly improve the reliability of probabilistic second minimum. Compared with the original simplified min-sum algorithm, the improved method not only gains the efficient hardware design but also obtains the better error correction capability.
A Recommender System for Metaheuristic Algorithms for Continuous Optimization Based on Deep Recurrent Neural Networks

As revealed by the no free lunch theorem, no single algorithm can outperform any others on all classes of optimization problems. To tackle this issue, methods for recommending an existing algorithm for solving given problems have been proposed. However, existing recommendation methods for continuous optimization suffer from low practicability and transferability, mainly due to the difficulty in extracting features that can effectively describe the problem structure and lack of data for training a recommendation model. This work proposes a generic recommender system to address the above two challenges. First, a novel method is proposed to represent an analytic objective function of a continuous optimization problem as a tree, which is directly used as the features of the problem. For black-box optimization problems whose objective function is unknown, a symbolic regressor is adopted to estimate the tree structure. Second, a large number of benchmark problems are randomly created based on the proposed tree representation, providing an abundant amount of training data with various levels of difficulty. By employing a deep recurrent neural network, a recommendation model is trained to recommend a most suitable metaheuristic algorithm for white- or black-box optimization, making a significant step forward towards fully automated algorithm recommendation for continuous optimization. Experimental results on 100 000 benchmark problems show that the proposed recommendation model achieves considerably better performance than existing ones, and exhibits high transferability to real-world problems.
Toward Silicon-Proven Detailed Routing for Analog and Mixed-Signal Circuits

Detailed routing is an intricate and tedious procedure in design automation and has become a crucial step for advanced node enablement. Compared with its advances in digital design, detailed routing for analog/mixed-signal (AMS) integrated circuits (ICs) is still heavily manual. In AMS designs, the sensitive net coupling issues and analog-specific constraints make detailed routing even more challenging. This work presents a novel and efficient detailed routing framework for automated AMS layout synthesis considering industrial design rules as well as analog-specific geometric and electrical constraints. Experimental results demonstrate the efficiency and effectiveness of our approach in optimizing circuit performance while satisfying the specified constraints. Post-layout simulations further prove that our detailed routing results can achieve sign-off quality.
IMPROVEMENT OF DOUGH RHEOLOGY AND BREAD QUALITY BY ENZYMES COMBINATION

In present, most mills from our country obtain brown wheat flour in intermediate extraction, besides white flour, and its quality is mostly inadequate and unconstant. Our paper's purpose was the optimization of the brown wheat flour's quality with the help o enzymes, using some of its beneficial properties. The sinergic effect of Aspergillus niger laccase and phytase, and cereal ? -amylase (Belpan) on the rheological properties of wheat flour were examined with the Mixolab test. The bread obtained from wheat flour with laccase and ? -amylase displayed an increased volume, without the problem of soaking during processing, compared with the bread obtained from the witness sample. An indirect correlation between falling number values and Belpan doses was obtained.
Discriminative Training of PLDA for Speaker Veriﬁcation with X-vectors

—This paper proposes a novel approach to discriminative training of probabilistic linear discriminant analysis (PLDA) for speaker veriﬁcation with x-vectors. Model over-ﬁtting is a well-known issue with discriminative PLDA (D-PLDA) for speaker veriﬁcation. As opposed to prior approaches which address this by limiting the number of trainable parameters, the proposed method parameterizes the discriminative PLDA (D-PLDA) model in a manner which allows for intuitive regularization, permitting the entire model to be optimized. Speciﬁcally, the within-class and across-class covariance matrices which comprise the PLDA model are expressed as products of orthonormal and diagonal matrices, and the structure of these matrices is enforced during model training. The proposed approach provides consistent performance improvements relative to previous D-PLDA methods when applied to a variety of speaker recognition evaluations, including the Speakers in the Wild Core-Core, SRE16, SRE18 CMN2, SRE19 CMN2, and VoxCeleb1 Tasks. Additionally, when implemented in Tensorﬂow using a modern GPU, D-PLDA optimization is highly efﬁcient, requiring less than 20 minutes.
Mathematical modeling of signal transduction pathways in mammalian cells at the example of the EGF induced MAP kinase cascade and TNF receptor crosstalk

In this work we apply mathematical modeling to signal transduction networks in mammalian cells. In particular, we have developed models for a survival pathway, the EGF induced MAP kinase cascade and an apoptotic signal trans-duction network the TNF receptor crosstalk. The models presented here are based on and were validated with own experimental data. In the field of signal transduction the major proteins involved and their interactions are fairly well known and biochemically characterized . One characteristic of signal transduction networks is that they are highly interconected by positive and negative feedbacks. Therefore, the dynamics of these networks can not be understood by intuition alone. Mathematical modeling has proven to be a valuable tool in engineering that deals effectively with complexity. In both casese were able to verify hypotheses, which were obtained by the models, experimentally. This work shows that mathematical modeling in combination with quantitative experimental data can give new insights into the potential mechanisms of intracellular signal transduction and regulation. nnerhalb der vorliegende Dissertation wurden mathematische Modelle fuer die EGF induzierte MAP-Kinase-Kaskade und fuer die TNF induzierte Apoptose entwickelt und analysiert. Bei der Modellentwicklung wurde darauf geachtet, dass die mathematischen Modelle auf experimentellen Daten beruhen und soweit moeglich, auch experimentell validiert wurden. Die hier entwickelten Modelle implizieren den derzeitigen Stand des Wissens ueber die jeweiligen Signaltransduktionsnetzwerke und bilden somit ein Forum fuer das publizierte Detailwissen. Waehrend die interagierenden Molekuele in Signaltransduktionsnetzwerken meist strukturell und biochemisch gut charakterisiert sind, ist es nicht moeglich, das Systemverhalten des Netzwerks aufgrund der positiven und negativen Feedback-Regulation rein intuitiv zu verstehen. In dieser Arbeit wird gezeigt, dass es moeglich ist, mathematische Modelle fuer grosse Signalnetzwerke mit praediktiven Charakter zu entwickeln. Wir konnten Hypothesen, die mit Hilfe des Modells gewonnen wurden, experimentell verifizieren und somit die Modelle innerhalb ihrer Grenzen validieren. Die in dieser Arbeit entwickelten Modelle ermoeglichen somit ein ganzheitliches Verstaendnis dieser komplexen biologisch Signalrtansduktions- systeme und die Identifizierung regulatorischer Mechanismen innerhalb der Netzwerke.
Expressing Temporality In Graphical User Interface

Temporality has been given attention in HCI research, with scholars arguing that temporal aspects in function-oriented graphical user interface are overlooked. However, these works have not adequately addressed practical approaches to manifest time in the design of such. This paper presents an approach for implementing temporal metaphors in the design of graphical user interface. In this design research, I materialize temporal metaphors into material qualities, in order to manifest time into the design of graphical user interface and shape the experiences of such designs. I argue that the design of temporal metaphors may express traces of time in graphical user interface differently from contemporary designs. I discuss implications and significance of unfolding experience over time. In conclusion, this design research, by articulating the experiences of its design works, sheds new light on the meanings of expressing temporal metaphors in the design of graphical user interface.
IL20RA signaling enhances stemness and promotes the formation of an immunosuppressive microenvironment in breast cancer

Rationale: Tumor microenvironment interacts with tumor cells to regulate their stemness properties through various cytokines and cytokine receptors. Previous studies revealed the possible role of interleukin 20 receptor subunit alpha (IL20RA) signaling in the progression of several types of tumors. However, its regulatory effects on the stemness and the microenvironment of breast cancer need to be studied. Methods: Immunohistochemical staining and western blot analysis were used to evaluate the association between IL20RA and SOX2 in breast tumors and noncancerous tissues. Enzyme-linked immunosorbent assay and TCGA dataset analysis were performed to determine the function of IL20RA signaling in breast cancer progression. Gain- and loss-of-function methods were performed to examine the effects of IL20RA on the stemness of breast cancer cells. The stemness features were analyzed by detecting the expression of core stemness genes, side population (SP), sphere formation ability, and aldehyde dehydrogenase (ALDH) activity. Flow cytometric analysis was applied to detect the changes of tumor-infiltration lymphocytes in tumor tissues in mice. Based on the relevant molecular mechanisms elucidated in this study, a novel IL20RA-targeted liposomal nanoparticle encapsulating the signal transducer and activator of transcription 3 (STAT3) inhibitor stattic (NP-Stattic-IL20RA) was synthesized. These NPs were combined with anti-programmed death ligand 1 (PD-L1) antibody and chemotherapy to inhibit the development of breast tumors in mice. Results: IL20RA is highly expressed in human breast cancers and is positively associated with the SOX2 expression. IL20RA increases the SP and ALDHbr proportions of breast cancer cells, enhances the sphere formation ability, and promotes the expression of core stemness genes, such as Sox2 and Oct4, as well as increases chemoresistance of breast cancer cells. IL20RA promotes the tumor-initiating ability and lung metastasis of breast cancer cells in vivo. In addition, IL20RA activates the Janus kinase 1 (JAK1)-STAT3-SOX2 signaling pathway, leading to increased expression of PD-L1 and reduced recruitment of anti-cancer lymphocytes, including CD8+ T cells and natural killer cells. Meanwhile, IL20RA signaling enhances the proportion of myeloid-derived suppressor cells. Combined with anti-PD-L1 antibody and NPs-Stattic-IL20RA, the chemotherapeutic efficacy was increased in breast cancer mouse models in vivo. Conclusion: Collectively, our results reveal that the IL20RA pathway is a novel signaling pathway involved in promoting the stemness features of breast cancer along with the formation of a tumor-favorable immune microenvironment. Targeting the IL20RAhi population with STAT3 signaling inhibition combined with anti-PD-L1 antibody can increase the therapeutic efficacy of chemotherapeutic agents for breast cancer. This study thus introduces a promising novel strategy for breast cancer therapy.
Orchard: Differentially Private Analytics at Scale

This paper presents Orchard, a system that can answer queries about sensitive data that is held by millions of user devices, with strong differential privacy guarantees. Orchard combines high accuracy with good scalability, and it uses only a single untrusted party to facilitate the query. Moreover, whereas previous solutions that shared these properties were custom-built for speciﬁc queries, Orchard is general and can accept a wide range of queries. Orchard accomplishes this by rewriting queries into a distributed protocol that can be executed efﬁciently at scale, using cryptographic primitives. Our prototype of Orchard can execute 14 out of 17 queries chosen from the literature; to our knowledge, no other system can handle more than one of them in this setting. And the costs are moderate: each user device typically needs only a few megabytes of trafﬁc and a few minutes of computation time. Orchard also includes a novel defense against malicious users who attempt to distort the results of a query.
132493_Anna Tegunimataka

This dissertation examines socio-economic outcomes of first and second generation immigrants in Denmark and adds to our understanding of different trajectories of immigrant integration by studying policy and family related factors. The association between family composition and socio-economic outcomes of the individual is in this thesis examined by studying the effects of intermarriage. Comparably high intermarriage premiums are found for foregin born who experience limited possibilities in the Danish labor market. Positive effects of intermarriage are also found when comparing the educational performance of children of intermarriage to the educational performance of children with two foreign born parents, also when taking school and family level characteristics into account. Policy and legislation constitute other factors that can either stimulate or hinder immigrant integration. Rules and regulations concerning naturalization is one such policy area. Consistent naturalization premiums are found in this dissertation, but only for immigrants who are more marginalized in the Danish labor market. Also education policies matter. An educational reform with the aim of enhancing immigrant integration by increasing school children’s Danish language proficiency is studied. The assumption was that by removing supplementary mothertongue education, the focus on learning the Danish language would be strengthened. The results, however, rather show negative effects of the reform, as the removal led to lower grades in Danish, thus the results support the argument that mother-tongue proficiency matters for second language acquisition. Common findings in all studies included in this dissertation are the large group differences in terms of results. For immigrants who originate from countries that are more culturally and geographically proximate to Denmark, naturalization and intermarriage are less important as integration tools. These groups already tend to be economically and socially integrated in Danish society, and they seem to be less in need of an additional boost from changing citizenship or marrying a Dane. Instead groups with more distant countries of origin are benefiting from intermarriage and naturalization. Children of intermarriage indeed tend to perform better in school than children belonging to the second generation of immigrants, and their performance is more in line with the performance of native Danes. But when taking parental heterogeneity into account differences emerge, and children with a non-native parent originating from a country more culturally and geographically distant from Denmark, tend to have an educational performance more in line with the second generation of immigrants.
Cefaléia menstrual: estudo semiológico de 100 casos

About 60% of women with migraine associate their headache attacks to the menstrual period. Regarding the non-migrainous headaches, this relationship is not so clear. The aim of this study is to present a clinical evaluation of menstrual headaches. Menstrual headache in our study was defined as the headache that begins in the interval of time from 2 days before menstruation until the last day of the menstrual period. The analyzed parameters were: intensity, character and localization of the pain, and associated symptoms. The headaches of 100 women (154 periods) whose ages ranged from 20 to 45 years were analyzed. Most headaches were classified as migraine without aura; the headaches had strong intensity in the first day of pain and reduced gradually until the last day of pain. The headaches were mostly of throbbing character and, in some moment of the menstrual period, referred as unilateral; the duration of these headaches was of longer duration than the migraine attacks reported in medical literature. Most part of the headaches began two days before the first day of the menstrual flow. Nauseas and/or vomiting were the most frequent associated symptoms. Finally, we found, among the menstrual headaches, 9 cases of tension type headache, 2 cases of cervicogenic headache and 1 case of stabbing headache.
A prospective study of infection and cardiovascular diseases: the Busselton Health Study.

Background: Infectious agents might play a role in the aetiology of cardiovascular diseases. The aim was to determine the association of antibodies to implicated infectious agents with coronary heart disease (CHD) and stroke in a population-based prospective study. Design: This study was based on a cohort of 1612 cardiovascular disease-free adults in the 1981 Busselton Health Survey. Primary risk factors were measured from stored serum and case-cohort sampling was used to reduce costs and preserve serum. The outcomes of interest were time to first CHD or stroke event Serum antibody tests were carried out for all 218 CHD cases, all 119 stroke cases and a random subset of 451 subjects. Methods: Sera were tested for antibodies to Chlamydia pneumoniae (IgG and IgA), and for IgG antibodies to Helicobacter pylori and cytomegalovirus (CMV). The association between serum antibody and risk of cardiovascular diseases was analysed using Cox proportional hazards regression. Results: The estimated population prevalence was 24% for C. pneumoniae IgG, 7% for C. pneumoniae IgA, 58% for H. pylori and 85% had CMV antibody levels greater than 15 AU/mL. The estimated relative risk of CHD was around 1.2 for all antibodies examined, except for C. pneumoniae IgA for which it was less than one, and the estimated relative risk of stroke was around 0.85, however in all cases the 95% confidence interval included one. Conclusions: This study of an Australian population does not support an association between serum antibody levels to C. pneumoniae, H. pylori and CMV with development of cardiovascular diseases.
Resposta de cultivares de feijoeiro comum à adubação nitrogenada em sistema de plantio direto

The objective of this experiment was to evaluate the response of common bean cultivars (Perola and IAC Una) to topdressed nitrogen (0, 50, 00 e 50 kg ha - of N) in a no-tillage system. The cultivar Perola, in comparison with IAC Una showed greater response and efficiency in the use of topdressed nitrogen, and this resulted in higher yield. Topdressed nitrogen doses promote linear increase in yield of Perola in a no-tillage system. Additional keywords: Phaseolus vulgaris , yield components, yield, agronomic efficiency.
Analysis of Limit Order Book and Order Flow

This paper extensively employs the order and trade data to analyze the shape of limit order book and the behavior of strategic order submission. The order book of stocks exhibits weakly convex pattern on the bid side due to wide price spreads away from the market. This characteristic of liquidity is particularly strong for the small stocks with large minimum tick size. In addition, the same order type occurs more frequently after the event had occurred than it would unconditionally. This diagonal effect is not fully explained by the order splitting. Moreover, the determinants driving order aggressiveness include bid-ask spread, market depths, other price spreads and depths away from the market, and market sentiment. Responding to the limit order book movement, an order aggressiveness revision behavior of market order traders is opposite to limit order traders, and contrarian traders react stronger than momentum traders.