Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximal Biclique Subgraphs and Closed Pattern Pairs of the Adjacency Matrix: A One-to-One Correspondence and Mining Algorithms

Maximal biclique (also known as complete bipartite) subgraphs can model many applications in Web mining, business, and bioinformatics. Enumerating maximal biclique subgraphs from a graph is a computationally challenging problem, as the size of the output can become exponentially large with respect to the vertex number when the graph grows. In this paper, we efficiently enumerate them through the use of closed patterns of the adjacency matrix of the graph. For an undirected graph G without self-loops, we prove that 1) the number of closed patterns in the adjacency matrix of G is even, 2) the number of the closed patterns is precisely double the number of maximal biclique subgraphs of G, and 3) for every maximal biclique subgraph, there always exists a unique pair of closed patterns that matches the two vertex sets of the subgraph. Therefore, the problem of enumerating maximal bicliques can be solved by using efficient algorithms for mining closed patterns, which are algorithms extensively studied in the data mining field. However, this direct use of existing algorithms causes a duplicated enumeration. To achieve high efficiency, we propose an O(mn) time delay algorithm for a nonduplicated enumeration, in particular, for enumerating those maximal bicliques with a large size, where m and n. are the number of edges and vertices of the graph, respectively. We evaluate the high efficiency of our algorithm by comparing it to state- of-the-art algorithms on three categories of graphs: randomly generated graphs, benchmarks, and a real-life protein interaction network. In this paper, we also prove that if self-loops are allowed in a graph, then the number of closed patterns in the adjacency matrix is not necessarily even, but the maximal bicliques are exactly the same as those of the graph after removing all the self-loops.
Neural Networks as Interacting Particle Systems: Asymptotic Convexity of the Loss Landscape and Universal Scaling of the Approximation Error

Neural networks, a central tool in machine learning, have demonstrated remarkable, high fidelity performance on image recognition and classification tasks. These successes evince an ability to accurately represent high dimensional functions, potentially of great use in computational and applied mathematics. That said, there are few rigorous results about the representation error and trainability of neural networks. Here we characterize both the error and the scaling of the error with the size of the network by reinterpreting the standard optimization algorithm used in machine learning applications, stochastic gradient descent, as the evolution of a particle system with interactions governed by a potential related to the objective or "loss" function used to train the network. We show that, when the number $n$ of parameters is large, the empirical distribution of the particles descends on a convex landscape towards a minimizer at a rate independent of $n$. We establish a Law of Large Numbers and a Central Limit Theorem for the empirical distribution, which together show that the approximation error of the network universally scales as $O(n^{-1})$. Remarkably, these properties do not depend on the dimensionality of the domain of the function that we seek to represent. Our analysis also quantifies the scale and nature of the noise introduced by stochastic gradient descent and provides guidelines for the step size and batch size to use when training a neural network. We illustrate our findings on examples in which we train neural network to learn the energy function of the continuous 3-spin model on the sphere. The approximation error scales as our analysis predicts in as high a dimension as $d=25$.
Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of.
Resonance Analysis as a Tool for Characterizing Functional Division of Layer 5 Pyramidal Neurons

Evidence suggests that layer 5 pyramidal neurons can be divided into functional zones with unique afferent connectivity and membrane characteristics that allow for post-synaptic integration of feedforward and feedback inputs. To assess the existence of these zones and their interaction, we characterized the resonance properties of a biophysically-realistic compartmental model of a neocortical layer 5 pyramidal neuron. Consistent with recently published theoretical and empirical findings, our model was configured to have a “hot zone” in distal apical dendrite and apical tuft where both high- and low-threshold Ca2+ ionic conductances had densities 1–2 orders of magnitude higher than anywhere else in the apical dendrite. We simulated injection of broad spectrum sinusoidal currents with linearly increasing frequency to calculate the input impedance of individual compartments, the transfer impedance between the soma and key compartments within the dendritic tree, and a dimensionless term we introduce called resonance quality. We show that input resonance analysis distinguished at least four distinct zones within the model based on properties of their frequency preferences: basal dendrite which displayed little resonance; soma/proximal apical dendrite which displayed resonance at 5–23 Hz, strongest at 5–10 Hz and hyperpolarized/resting membrane potentials; distal apical dendrite which displayed resonance at 8–19 Hz, strongest at 10 Hz and depolarized membrane potentials; and apical tuft which displayed a weak resonance largely between 8 and 10 Hz across a wide range of membrane potentials. Transfer resonance analysis revealed that changes in subthreshold electrical coupling were found to modulate the transfer resonant frequency of signals transmitted from distal apical dendrite and apical tuft to the soma, which would impact the frequencies that individual neurons are expected to respond to and reinforce. Furthermore, eliminating the hot zone was found to reduce amplification of resonance within the model, which contributes to reduced excitability when perisomatic and distal apical regions receive coincident stimulating current injections. These results indicate that the interactions between different functional zones should be considered in a more complete understanding of neuronal integration. Resonance analysis may therefore be a useful tool for assessing the integration of inputs across the entire neuronal membrane.
Social Choice A Framework For Collective Decisions And Individual Judgements

Spend your few moment to read a book even only few pages. Reading book is not obligation and force for everybody. When you don't want to read, you can get punishment from the publisher. Read a book becomes a choice of your different characteristics. Many people with reading habit will always be enjoyable to read, or on the contrary. For some reasons, this social choice a framework for collective decisions and individual judgements tends to be the representative book in this website.
Layered fields for natural tessellations on surfaces

Mimicking natural tessellation patterns is a fascinating multi-disciplinary problem. Geometric methods aiming at reproducing such partitions on surface meshes are commonly based on the Voronoi model and its variants, and are often faced with challenging issues such as metric estimation, geometric, topological complications, and most critically, parallelization. In this paper, we introduce an alternate model which may be of value for resolving these issues. We drop the assumption that regions need to be separated by lines. Instead, we regard region boundaries as narrow bands and we model the partition as a set of smooth functions layered over the surface. Given an initial set of seeds or regions, the partition emerges as the solution of a time dependent set of partial differential equations describing concurrently evolving fronts on the surface. Our solution does not require geodesic estimation, elaborate numerical solvers, or complicated bookkeeping data structures. The cost per time-iteration is dominated by the multiplication and addition of two sparse matrices. Extension of our approach in a Lloyd's algorithm fashion can be easily achieved and the extraction of the dual mesh can be conveniently preformed in parallel through matrix algebra. As our approach relies mainly on basic linear algebra kernels, it lends itself to efficient implementation on modern graphics hardware.
A Chemical Investigation of the Leaves of Morus alba L.

The leaves of Morus alba L. are an important herbal medicine in Asia. The systematic isolation of the metabolites of the leaves of Morus alba L. was achieved using a combination of liquid chromatography techniques. The structures were elucidated by spectroscopic data analysis and the absolute configuration was determined based on electronic circular dichroism (ECD) spectroscopic data and hydrolysis experiments. Their biological activity was evaluated using different biological assays, such as the assessment of their capacity to inhibit the aldose reductase enzyme; the determination of their cytotoxic activity and the evaluation of their neuroprotective effects against the deprivation of serum or against the presence of nicouline. Chemical investigation of the leaves of Morus alba L. resulted in four new structures 1–4 and a known molecule 5. Compounds 2 and 5 inhibited aldose reductase with IC50 values of 4.33 μM and 6.0 μM compared with the potent AR inhibitor epalrestat (IC50 1.88 × 10−3 μM). Pretreatment with compound 3 decreased PC12 cell apoptosis subsequent serum deprivation condition and pretreatment with compound 5 decreased nicouline-induced PC12 cell apoptosis as compared with control cells (p < 0.001).
Robust multi-objective optimization of a photovoltaic system with grid connection

Photovoltaic panels are a way of providing electricity without altering natural resources. Due to its intermittent nature and to meet consumers demand, this energy has to be stored, for example by using batteries. In this work, an energy model for buildings with battery is developed based on electrical consumption and production data. This model takes into account the depth of discharge, state of charge and efficiency over a cycle of a lithium type battery. Three rule-based strategies are then described. This leads to our optimization problem. The optimization is applied on two time slots: one day (in order to explore rapidly the behavior of different algorithm strategies) and one week (for pseudo real-time simulation). Robust multi-objective optimization is performed in order to reduce the impact of consumption prediction errors.
Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation

Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evaluate the quality of multilingual topics. We introduce a new intrinsic evaluation of multilingual topic models that correlates well with human judgments of multilingual topic coherence as well as performance in downstream applications. Importantly, we also study evaluation for low-resource languages. Because standard metrics fail to accurately measure topic quality when robust external resources are unavailable, we propose an adaptation model that improves the accuracy and reliability of these metrics in low-resource settings.
Variable-Inverter-Rectifier-Transformer: A Hybrid Electronic and Magnetic Structure Enabling Adjustable High Step-Down Conversion Ratios

This paper proposes a hybrid electronic and magnetic structure named a variable-inverter-rectifier-transformer (VIRT) that enables a transformer with fractional and reconfigurable effective turns ratios (e.g., 12:0.5, 12:2/3, 12:1, and 12:2). This functionality is valuable in converters with wide operating voltage ranges and high step-up/down, as it offers a means to reduce the turns count and copper loss within the transformer while also facilitating voltage doubling and quadrupling. These properties are especially beneficial for miniaturizing the transformer stage in many power electronics applications, such as universal serial bus wall chargers. We introduce the principle of operation of the structure and present models for its magnetic and electrical behavior. The instance of VIRT described in this paper comprises four half-bridge switching cells distributed around a planar magnetic core and connected to two “half-turns” wound through that core. By controlling the operating modes of the half-bridge cells, we gain control over the flux paths and current paths used in the transformer, and this hybridization enables fractional and reconfigurable effective turns ratios. An experimental prototype integrates the VIRT with a stacked-bridge <italic>LLC</italic> converter to accommodate a widely varying input (120–380 <inline-formula><tex-math notation="LaTeX">$\text{V}_{\text{dc}}$</tex-math></inline-formula>) and output (5–20 <inline-formula><tex-math notation="LaTeX">$\text{V}_{\text{dc}}$</tex-math></inline-formula>), and VIRT is shown to be highly beneficial in keeping efficiency high over this wide output voltage range.
Secure Integer Comparisons Using the Homomorphic Properties of Prime Power Subgroups

Secure multi party computation allows two or more parties to jointly compute a function under encryption without leaking information about their private inputs. These secure computations are vital in many fields including law enforcement, secure voting and bioinformatics because the privacy of the information is of paramount importance. One common reference problem for secure multi party computation is the Millionaires’ problem which was first introduced by Turing Award winner Yao in his paper "Protocols for secure computation". The Millionaires’ problem considers two millionaires who want to know who is richer without disclosing their actual worth. There are public-key cryptosystems that currently solve this problem, however they use bitwise decomposition and Boolean algebra on encrypted bits. This type of solution is costly as each bit requires its own encryption and decryption. Our solution to the Millionaires’ problem and secure integer comparison looks at a new approach which doesn’t use the decomposition method and instead encrypts the full length of the message in one encryption (within scope). This method also extends in a linear fashion (with respect to the number of encryptions), so larger integers remain efficient to compare. In this thesis, we present a new cryptosystem with a novel homomorphic property used for secure integer comparison, as well as a protocol implementing the cryptosystem and a simulation security proof for the protocol. Finally, we implemented the system and compared it to systems that are being used today.
A Medical Image Classification using ID3 Classifier

Segmentation and classification of brain tumors using Magnetic Resonance Imaging(MRI) and Computed Tomography(CT) is a difficult task due to the various complexity of tumors.This paper presents ID3 classifier with association rule mining for solving classification problems of the MRI images.The proposed method consists of mainly three stages, Preprocessing, Association Rule Mining and Classification.ID3 classifier is used for predicting classification based on sensitivity,specificity and accuracy.The main goal of this method is to achieve the higher accuracy rate and lower error rate.
Impact of left atrial appendage closure on cardiac functional and structural remodeling: A difference-in-difference analysis of propensity score matched samples

Background Although the safety and efficacy of left atrial (LA) appendage (LAA) closure (LAAC) in nonvalvular atrial fibrillation (NVAF) patients have been well documented in randomized controlled trials and real-world experience, there are limited data in the literature about the impact of LAAC on cardiac remodeling. The aim of the study was to examine the impact of LAAC on cardiac functional and structural remodeling in NVAF patients. Methods Between March 2014 and November 2016, 47 NVAF patients who underwent LAAC were included in this study (LAAC group). A control group (non-LAAC group) was formed from 141 NVAF patients without LAAC using propensity score matching. The difference-in-difference analysis was used to evaluate the difference in cardiac remodeling between the two groups at baseline and follow-up evaluations. Results The LAAC group had a larger increase in LA dimension, volume and volume index than the non-LAAC group (+3.9 mm, p = 0.001; +9.7 mL, p = 0.006 and +5.9 mL/m2, p = 0.011, respectively). Besides, a significant increase in E and E/e’ ratio was also observed in the LAAC group (+14.6 cm/s, p = 0.002 and +2.3, p = 0.028, respectively). Compared with the non-LAAC group, left ventricular (LV) ejection fraction and fractional shortening decreased in LAAC patients, but were statistically insignificant (−3.5%, p = 0.109 and −2.0%, p = 0.167, respectively). Conclusions There were significant increases in LA size and LV filling pressure among NVAF patients after LAAC. These impacts of LAAC on cardiac functional and structural remodeling may have some clinical implications that need to be addressed in future studies.
A Class-AB Bulk-Driven Amplifier with Enhanced Transconductance Using Quasi-Floating Gate Method

This paper presents a supper class-AB adaptive biasing bulk-driven amplifier for ultra-low-power applications. In the proposed structure, two bulk-driven flipped voltage follower (FVF) cells are reconfigured as nonlinear tail currents using quasi-floating gate method to enhance transconductance and slew rate. In addition, two idle current controllers are employed as common source amplifiers to provide a supper class-AB structure without increasing total current consumption. The proposed structure is simulated in 0.18-μm CMOS technology at 0.5V supply with 35nW power budget. The results show a 57.9dB DC gain, 8.8kHz gain bandwidth and 38.2V/ms slew rate for the proposed amplifier.
Monotherapy Is Good Enough for Patients With Mild‐to‐Moderate Alzheimer's Disease: A Network Meta‐Analysis of 76 Randomized Controlled Trials

Memantine and the acetylcholinesterase inhibitors (AChEIs) are two classes of drugs that are used to treat patients with Alzheimer's disease (AD). We conducted a network meta‐analysis of randomized controlled trials to compare the treatment effectiveness of monotherapy or combination therapy A total of 23,707 AD patients in 76 randomized trials were identified. In patients with mild‐to‐moderate AD, monotherapy with donepezil, galantamine, or rivastigmine were superior to placebo in enhancing cognitive functions and activities of daily living, whereas monotherapy with donepezil or memantine were superior to placebo in improving behavioral symptoms. However, combination therapy with AChEIs and memantine did not show additional benefit than monotherapy. In patients with moderate‐to‐severe AD, neither monotherapy nor combination therapy were superior to placebo in any domain measurement. Combination therapy with memantine and AChEIs is confirmed to have no additional benefits over monotherapy.
ADAPTING MODELS IN METAMODELS COMPOSITION PROCESSES

In Model Driven Engineering (MDE) approaches, metamodels can change after the creation of conformant models. Moreover, changes applied on one metamodel can be result of a composition process. When metamodels change, model conformity can be broken. Once the conformity is broken, the model is unuseful and it is not possible to regain the conformity with the composite metamodel. This paper presents a proposal to solve models adaptation through a Domain Speci c Language (DSL). This DSL is used by metamodelers who are the people that know the domains abstracted by several metamodels, and know how to combine those meta-models in order to generate the composite metamodel. In addition, the DSL allows metamodelers to include the solu-tion for conformant models adaptation.
Primary Tactile Thalamus Spiking Reflects Cognitive Signals

Little is known about whether information transfer at primary sensory thalamic nuclei is modified by behavioral context. Here we studied the influence of previous decisions/rewards on current choices and preceding spike responses of ventroposterior medial thalamus (VPm; the primary sensory thalamus in the rat whisker-related tactile system). We trained head-fixed rats to detect a ramp-like deflection of one whisker interspersed within ongoing white noise stimulation. Using generative modeling of behavior, we identify two task-related variables that are predictive of actual decisions. The first reflects task engagement on a local scale (“trial history”: defined as the decisions and outcomes of a small number of past trials), whereas the other captures behavioral dynamics on a global scale (“satiation”: slow dynamics of the response pattern along an entire session). Although satiation brought about a slow drift from Go to NoGo decisions during the session, trial history was related to local (trial-by-trial) patterning of Go and NoGo decisions. A second model that related the same predictors first to VPm spike responses, and from there to decisions, indicated that spiking, in contrast to behavior, is sensitive to trial history but relatively insensitive to satiation. Trial history influences VPm spike rates and regularity such that a history of Go decisions would predict fewer noise-driven spikes (but more regular ones), and more ramp-driven spikes. Neuronal activity in VPm, thus, is sensitive to local behavioral history, and may play an important role in higher-order cognitive signaling. SIGNIFICANCE STATEMENT It is an important question for perceptual and brain functions to find out whether cognitive signals modulate the sensory signal stream and if so, where in the brain this happens. This study provides evidence that decision and reward history can already be reflected in the ascending sensory pathway, on the level of first-order sensory thalamus. Cognitive signals are relayed very selectively such that only local trial history (spanning a few trials) but not global history (spanning an entire session) are reflected.
Orphan CpG islands as alternative promoters

ABSTRACT CpG islands (CGIs) are associated with ∼60% of mammalian promoters. Most unmethylated CGIs exhibit transcriptional activity, which has led to their co-option as promoters by retrogenes. CGIs may also serve as alternative promoters for downstream genes with methylated promoters, with implications on aberrant activation of oncogenes in cancer phenotypes.