Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive Value of Molecular Subtyping for Locoregional Recurrence in Early-Stage Breast Cancer with N1 without Postmastectomy Radiotherapy

Purpose This study was designed to investigate the relationship between molecular subtype and locoregional recurrence (LRR) in patients with early-stage breast cancer with 1–3 positive axillary lymph nodes (ALNs) and improve the individualized indications for postmastectomy radiotherapy (PMRT). Methods The records of 701 patients with pT1-2N1M0 breast cancer who did not undergo PMRT were retrospectively analyzed. Tumors were subclassified as follows: luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, and basal-like subtypes. Multivariate Cox analysis was used to determine the risk of LRR associated with the different subtypes and to adjust for clinicopathologic factors. Results Luminal A, luminal B, HER2-enriched, and basal-like subtypes accounted for 51.2%, 28.0%, 8.1%, and 12.7% of cases, respectively. The median follow-up duration was 67 months (range, 9–156 months). Univariate analysis revealed that, compared with the luminal A subtype, the HER2-enriched and basal-like subtypes were associated with significantly higher 5-year LRR rates (5.6% vs. 21.6% and vs.15.7% respectively; p=0.002 each), lower 5-year LRR-free survival (LRFS) rates (90.6% vs. 73.8% and 78.5%, respectively; p=0.001 each), and poorer 5-year breast cancer-specific survival (BCSS) rates (93.7% vs. 82.2% [p=0.002] and 84.9% [p=0.001], respectively). Multivariate analysis revealed that the HER2-enriched and basal-like subtypes, age ≤35 years, a medial tumor, and pT2 stage were poor prognostic factors for LRR and LRFS; furthermore, 2 to 3 positive ALNs represented an independent prognostic factor affecting LRR. The 10-year LRR rates of patients with 0, 1, 2, 3, and 4 risk factors were 1.0%, 6.9%, 14.3%, 30.4%, and 54.3%, respectively (p<0.001); the 10-year BCSS rates were 86.6%, 88.5%, 84.4%, 79.7%, and 38.8%, respectively (p<0.001). Conclusion Molecular subtyping allows for individualized evaluation of LRR risk in patients with pT1-2N1M0 breast cancer. PMRT should be recommended for patients with ≥3 LRR risk factors.
Distributed Transmitter Optimization for the Wyner-Type Downlink via Belief Propagation

We study a Wyner-type downlink network, where any two adjacent base stations (BSs) can communicate with each other and cooperate on transmission of discrete data symbols to their intended mobile stations (MSs) situated along a circular array. With focus on the binary case, we take the symbol detection point of view and formulate a novel non-linear precoding optimization problem by imposing minimum Symbol Error Probabilities as Quality-of-Service constraints at MSs. We then cast the problem into a factor graph and propose a belief propagation (BP) framework to solve the problem in a distributed manner based on only local communication between adjacent BSs. Numerical results confirm the convergence of the distributed algorithm and demonstrate the transmit power saving from the proposed precoder over two state-of-the-art linear precoders. To the best of our knowledge, this is the first work that leverages the BP method for non-linear precoding.
Critical Review of Internationalisation of Vocational Education and Training: An Adapting Curve for Teachers and Learners by Ly Thi Tran and Understanding VET Teachers Challenges in Providing Quality Education to International Students by Sonal Nakar

Vocational Education and Training (VET) in Australia has experienced a strong growth in International student enrolments since 1997. A study carried out by Australian Education International (AEI) showed that in 2009 there were approximately 73,100 International student enrolments in the VET sector (AEI 2010). However, Bruce Bairdrsquos review (AEI 2010) points out that alongside the strong International student enrolment growth in the VET sector, the quality education outcome at VET providers has been in question. Nayak and Venkatraman (2010, p. 5) indicate that VET curriculums are not specifically tailored to the learning needs of International students. On this point, Marginson and Eijkman (2007) underscore the need to develop Internationalised curricula to acknowledge the divergent learning needs of International students within the Australian VET context. Guthrie (2008) argues that the learning and assessment processes of the Competency-Based-Training and Assessment (CBTA) simply immersed in compliant context that in fact hindered the adoption of the International VET curriculum. Given the myriad issues associated with the management of the learning and assessment of students from culturally and linguistically diverse backgrounds, it is crucial that VET teachers have sufficient confidence and willingness to confront the complex issues associated with managing International students. With these issues in mind, this paper reviews two research articles that deal with the growing trend of International pedagogy following the Australian VET framework and the challenges faced by the VET teachers in responding to the learning needs and expectations of International students in vocational education.
Fiscal Dominance and Money Growth in Italy: The Long Record

Fiscal dominance, that is, the extent to which government deficits condition the growth of the money supply, has been the prevailing regime in Italian monetary history from the creation of the state in 1861 to the 1980s. The nature of the institutional structure linking budget deficits to monetary base creation has changed over time. In the early days, the profit-seeking banks of issue exceeded intermittently the legal ceiling on their outstanding currency to lend to government. The influence of public finance on monetary policy became even stronger, first, in the 1930s and, later, in the 1970s. The joint event of an independent central bank and lower budget deficits are responsible for a reversal of fiscal dominance in the 1980s and the 1990s.
Schooling and Citizenship: Evidence from Compulsory Schooling Reforms

This paper examines whether schooling has a positive impact on individual's political interest, voting turnout, democratic values, political involvement and political group membership, using the German General Social Survey (ALLBUS). Between 1949 and 1969 the number of compulsory years of schooling was increased from eight to nine years in the Federal Republic of Germany, gradually over time and across federal states. These law changes allow one to investigate the causal impact of years of schooling on citizenship. Years of schooling are found to be positively correlated with a broad range of political outcome measures. However, when exogenous increase in schooling through law changes is used, there is no evidence of a causal effect running from schooling to citizenship in Germany.
Development of an efficient and secure mobile communication system with new future directions

This chapter presents performance of a new technique for constructing Quasi-Cyclic Low-Density ParityCheck (QC-LDPC) encrypted codes based on a row division method. The new QC-LDPC encrypted codes are flexible in terms of large girth, multiple code rates, and large block lengths. In the proposed algorithm, the restructuring of the interconnections is developed by splitting the rows into subrows. This row division reduces the load on the processing node and ultimately reduces the hardware complexity. In this method of encrypted code construction, rows are used to form a distance graph. They are then transformed to a parity-check matrix in order to acquire the desired girth. In this work, matrices are divided into small sub-matrices, which result in improved decoding performance and reduce waiting time of the messages to be updated. Matrix sub-division increases the number of sub-matrices to be managed and memory requirement. Moreover, Prototype architecture of the LDPC codes has been implemented by writing Hardware Description Language (VHDL) code and targeted to a Xilinx Spartan-3E XC3S500E FPGA chip.
Analysis of Microblogs

In this project we attempt to perform sentiment based classification of Micro-blogs using Machine Learning techniques. Sentiment Analysis of short messages posted on Micro-blogging tools can be helpful in determining the current usability and acceptance of any target product or service. It can help in raising alarms in the wake of sudden shifts in user sentiments or attitude towards the service. We present a system that can read messages relevant to a particular topic from a micro-blogging service such as Twitter, analyze the messages for the sentiments they carry and classify them as neutral,positive or negative. We try out different feature selection and classification algorithms in our search for the best combination. We also evaluate different strategies in order to get the best performance over the imbalanced dataset caused by relatively infrequent changes in user sentiments and to understand the underlying nature of this problem.
IMPROVED TEXT CLUSTERING WITH NEIGHBORS

With ever increasing number of documents on web and other repositories, the task of organizing and categorizing these documents to the diverse need of the user by manual means is a complicated job, hence a machine learning technique named clustering is very useful. Text documents are clustered by pair wise similarity of documents with similarity measures like Cosine, Jaccard or Pearson. Best clustering results are seen when overlapping of terms in documents is less, that is, when clusters are distinguishable. Hence for this problem, to find document similarity we apply link and neighbor introduced in ROCK. Link specifies number of shared neighbors of a pair of documents. Significantly similar documents are called as neighbors. This work applies links and neighbors to Bisecting K-means clustering in identifying seed documents in the dataset, as a heuristic measure in choosing a cluster to be partitioned and as a means to find the number of partitions possible in the dataset. Our experiments on real-time datasets showed a significant improvement in terms of accuracy with minimum time.
A strict stability limit for adaptive gradient type algorithms

This paper considers gradient type algorithms with a regression vector allowed to be different from the input vector. To cover the most general case, no restrictions are imposed on the dependency between the excitation vector and the regression vector. In terms of l2-stability, for the real-valued domain, a convergence analysis is performed based on the singular value decomposition. It reveals that such algorithms are potentially unstable if the input vector and the regression vector do not have the same direction. For the conventional gradient type algorithm (for which latter vectors are parallel), an l2-stability bound, known from literature to be sufficient, can be shown to be actually strict. Simulations demonstrate how the presented method can be used to discover unstable modes of an apparently stable algorithm.
Diffusion in Social and Information Metworks: Research Problems; Probabilistic Models & Machine Learning Methods

In recent years, there has been an increasing effort on developing realistic models, and learning and inference algorithms to understand, predict, and influence diffusion over networks. This has been in part due to the increasing availability and granularity of large-scale diffusion data, which, in principle, allows for understanding and modeling not only macroscopic diffusion but also microscopic (node-level) diffusion. To this aim, a bottom-up approach has been typically considered, which starts by considering how particular ideas, pieces of information, products, or, more generally, contagions spread locally from node to node apparently at random to later produce global, macroscopic patterns at a network level. However, this bottom-up approach also raises significant modeling, algorithmic and computational challenges which require leveraging methods from machine learning, probabilistic modeling, event history analysis and graph theory, as well as the nascent field of network science. In this tutorial, we will present several diffusion models designed for fine-grained large-scale diffusion data, present some canonical research problem in the context of diffusion, and introduce state-of-the-art algorithms to solve some of these problems, in particular, network estimation, influence estimation and influence control.
Learning the Initial State of a Second-Order Recurrent Neural Network during Regular-Language Inference

Recent work has shown that second-order recurrent neural networks (2ORNNs) may be used to infer regular languages. This paper presents a modified version of the real-time recurrent learning (RTRL) algorithm used to train 2ORNNs, that learns the initial state in addition to the weights. The results of this modification, which adds extra flexibility at a negligible cost in time complexity, suggest that it may be used to improve the learning of regular languages when the size of the network is small.
Health literacy, health empowerment and health information search in the field of MMR vaccination: a cross-sectional study protocol

Objectives Although public health offices have a detailed record of the vaccination coverage among adolescents in Switzerland, little is known about the factors that determine the decisions of parents to get their children vaccinated. Based on Schulz & Nakamoto's Extended Health Empowerment Model, the present study aims at surveying parents of adolescents in Ticino (Switzerland) to get insights into the role of health literacy, health empowerment, information search behaviour and potential confounding variables that influence whether adolescents are not at all vaccinated, undervaccinated or fully covered against measles, mumps and rubella (MMR). Methods and analysis A survey including concepts of the Extended Health Empowerment Model will be administered to all families with adolescents attending the third year of middle school in Ticino. Subsequently, survey responses will be matched with actual data on MMR vaccination coverage of adolescents collected from the Cantonal Office of Public Health in Ticino. Discussion The results of this study will allow one to draw more comprehensive conclusions about the factors that play a role in parents’ decisions regarding the vaccination of their children. At the same time, the study will provide useful insights on which are the main issues to be considered when addressing parents (on an interpersonal as well as a mass communication level) regarding the vaccination of their children.
A Two-Step Nanofiltration Process for the Production of Phenolic-Rich Fractions from Artichoke Aqueous Extracts

Commercial nanofiltration (NF) membranes in spiral-wound configuration (NP030 from Microdyn Nadir and Desal DK from GE Water & Process Technologies) were used in a sequential design in order to produce a separated fraction of phenolic and sugar compounds from an aqueous artichoke extract. For both membranes, the effect of transmembrane pressure (TMP) on the permeation flux was evaluated. In optimized conditions of TMP, the NP030 membrane exhibited high rejections of apigenin, cynarin and chlorogenic acid (higher than 85%); on the other hand, very low rejections of fructose, glucose and sucrose (lower than 4%) were measured. Starting from an extract with a total antioxidant activity (TAA) of 5.28 mM trolox a retentate fraction with a TAA of 47.75 mM trolox was obtained. The NF permeate from the NP030 membrane was processed with the Desal DK membrane in optimized conditions of TMP producing a permeate stream free of phenolic and sugar compounds. Accordingly, as most part of phenolic compounds was removed in the first NF step, the concentration of sugar compounds in the NF retentate had much higher results than that of phenolic compounds.
Uncoupling between cerebral perfusion and oxygenation during incremental exercise in an athlete with postconcussion syndrome: a case report

High‐intensity exercise may pose a risk to patients with postconcussion syndrome (PCS) when symptomatic during exertion. The case of a paralympic athlete with PCS who experienced a succession of convulsion‐awakening periods and reported a marked increase in postconcussion symptoms after undergoing a graded symptom‐limited aerobic exercise protocol is presented. Potential mechanisms of cerebrovascular function failure are then discussed.
Sparsity enhancing window functions for analogue-to-information conversion with compressed sensing

We show that data reconstruction with analogue-to-information converters can generally be improved by applying a window function. For data recovery via compressed sensing, the choice of window function depends on the number of samples acquired, and any window is better than no window. We also demonstrate that windows can be applied a posteriori in random sampling analogue-to-information converter systems.
Bursty event detection from text streams for disaster management

In this paper, an approach to automatically identifying bursty events from multiple text streams is presented. We investigate the characteristics of bursty terms that appear in the documents generated from text streams, and incorporate those characteristics into a term weighting scheme that distinguishes bursty terms from other non-bursty terms. Experimental results based on the news corpus show that our approach outperforms the existing alternatives in extracting bursty terms from multiple text streams. The proposed research is expected to contribute to increasing the situational awareness of ongoing events particularly when a natural or economic disaster occurs.
Effect of Magnetic Tacrine-Loaded Chitosan Nanoparticles on Spatial Learning, Memory, Amyloid Precursor Protein and Seladin-1 Expression in the Hippocampus Of Streptozotocin-Exposed Rats

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by memory and cognitive dysfunction due to neuronal cell loss in higher brain centers. Senile plaques containing amyloid β (Aβ) are associated with this disease as well as a reduction in cholinergic neuron numbers. Tacrine is a reversible cholinesterase inhibitor in clinical use to treat moderate forms of AD. Chitosan nanoparticles represent an effective systemic delivery system for drugs. The application of tacrine-loaded chitosan nanoparticles has been shown to selectively increase tacrine concentrations in the brain tissue. In this study, we compared magnetic and non-magnetic tacrine-loaded chitosan nanoparticles for their bioactivity and neuroprotective potency in streptozotocin (stz)-induced neurodegeneration, an accepted animal model for AD. Male rats received a single injection of stz via an implanted cannula into the lateral brain ventricle. Tacrine (tac)-loaded chitosan nanoparticles were delivered into the tail vein. Spatial learning and memory were analyzed using the Morris water maze task. Amyloid precursor protein gene (APP) and seladin-1 gene expression were studied in the hippocampus by real time-PCR. Tac-loaded non-magnetic and tac-loaded magnetic chitosan nanoparticles improved spatial learning and memory after stz treatment with magnetic nanoparticles being most effective. Similarly, tac-loaded chitosan nanoparticles increased seladin-1 and reduced APP gene expression. Again, magnetic nanoparticles were more effective. These data reveal that tac-loaded non magnetic and tac-loaded magnetic chitosan nanoparticles to a higher extent improve brain deficits related to stz application. We conclude that the magnetic target drug delivery system is a promising therapeutic strategy to protect AD-related degenerating in the CNS.
Software/Configware Implementation of Combinatorial Algorithms

This paper discusses an approach for solving combinatorial problems by combining software and dynamically reconfigurable hardware (configware). The suggested technique avoids instance-specific hardware compilation and, as a result, allows obtaining higher performance than currently available pure software approaches as well as instance-specific reconfigurable solutions. Moreover, the technique permits problems to be solved that exceed the resources of the available reconfigurable hardware. The architecture of dynamically reconfigurable hardware problem solver is modeled in software allowing to estimate different characteristics, such as the time of reconfiguration, performance, etc., and to speed up the overall design process.
Performance analysis of 16-channel 80-gbps optical fiber communication system

Wavelength division multiplexing allow the multiple channel to transmit the data at high speed at the same instant. For large distance communication, Single mode fiber is preferred over Multimode fiber. Quality factor decreases as data rate and optical fiber length increases. In this proposed work Optisystem 13.0 simulator is used to analyze dispersion effect on 16 channel dense WDM system at 80 Gbps data rate. A single chirped fiber bragg grating is used for compensate the dispersion in single channel and 16 cascaded chirped FBG for 16 channel. At 80km optical fiber length performance metrics such as minimum bit error rate 1.3 × 10-316 and quality factor is 38.016. Signal is amplified at each multiple of 80km optical fiber length using Loop control unit. The performance of optical system has been given using BER and eye analyzer.
Characteristic cycles for the loop Grassmannian and nilpotent orbits

α (X), which is a linear combination of closures of conormal bundles to submanifolds of X. Intuitively, the microlocal multiplicities cα() me asurethesingularity of at α. In settings related to representation theory, a group G acts on X, is G-equivariant, and the microlocal multiplicities play a significant but only partially understood role in representation theory (see (Ro), (SV), (ABV), and (KaSa), e.g.). In this paper, we compute microlocal multiplicities for certain cases of interest in representation theory. Let G be a connected reductive group with loop group LG, and let P bethesubgroup of LG consisting of loops with positive Fourier coefficients. Then P -orbits λ on theloop Grassmannian LG/P = correspond to the irreducible representations L(λ) of thedual re ductivegroup ˇ G. For dominant weights µ and λ of a torus of ˇ
Learning to grasp from point clouds

We study how to encode the local information of the point clouds in such a way that a robot can learn by experimentation the graspability of objects. After learning, the robot should be able to predict the graspability of unknown objects. We consider two well known descriptors in the computer vision community: Spin images and shape context. In addition, we consider two recent and efficient descriptors: the Point Feature Histogram and the Viewpoint Feature Histogram. We evaluate the discriminative properties of the descriptors on a synthetic scenario and a simulator scenario, classifying the points with a standard learning algorithm, the Support Vector Machines. The results suggest the addition of more global information to the local descriptors.
Fast Parallel Extract-Shift and Parallel Deposit-Shift in General-Purpose Processors

This paper describes a new basis for the implementation of the extract-shift and deposit-shift functional units in microprocessors that can implement new advanced bit manipulations as well as standard shift, extract and deposit operations. Our design is based on the inverse butterfly (butterfly) data path circuits, rather than omega-flip, crossover and crossbar designs used. We show how this new unit can implement the standard shift, rotate, extract and deposit operations, as well as more advanced extract-shift and deposit-shift operations. Furthermore, it can perform important new kinds of even more advanced bit manipulation instructions like arbitrary bit permutations, bit gather (or parallel extract-shift), and bit scatter (or parallel deposit-shift) instructions. Thus, our new functional unit performs the functionality of two functional units -- the basic shifter and the advanced bit manipulation functional unit, while having a less latency and area than that of the shift-permute functional unit.