Miracle-dz/UniFAR-FTUs huggingface dataset:
Tunable THz and Infrared Plasmonic Filters and Switches Based on Circular Graphene Resonator With 90$^\circ$ Bending of Output Port

Two types of novel graphene-based components, namely, filters and electro-optical switches in guided wave configuration are suggested and analysed. The filters differ from the known ones with collinear orientation of the input and output waveguides by geometry and symmetry. The components consist of a circular graphene disk and two nanoribbons oriented at $90^\circ$ to each other in the plane of the graphene layer. The graphene elements are placed on a dielectric substrate. We show that change in symmetry leads to a drastic change in the properties of the components. The physical principle of the devices is based on the dipole, quadrupole and hexapole resonances in the graphene disk which define stop-band, pass-band and stop-band frequency characteristics, respectively. A combination of stop-band and pass-band filter properties by shifing the electronic Fermi level allows one to design a switch. Numerical simulations show that the suggested components have very small dimensions, good characteristics and provide a dynamic control of their central frequency via electrostatic gating.
Two weight $L^{p}$-inequalities for dyadic shifts and the dyadic square function

We consider two weight $L^{p}\to L^{q}$-inequalities for dyadic shifts and the dyadic square function with general exponents $1<p,q<\infty$. It is shown that if a so-called quadratic $\mathscr{A}_{p,q}$-condition related to the measures holds, then a family of dyadic shifts satisfies the two weight estimate in an $\mathcal{R}$-bounded sense if and only if it satisfies the direct- and the dual quadratic testing condition. In the case $p=q=2$ this reduces to the result by T. Hyt\"onen, C. P\'erez, S. Treil and A. Volberg. The dyadic square function satisfies the two weight estimate if and only if it satisfies the quadratic testing condition and the quadratic $\mathscr{A}_{p,q}$-condition holds. Again in the case $p=q=2$ we recover the result by F. Nazarov, S. Treil and A. Volberg. An example shows that in general the quadratic $\mathscr{A}_{p,q}$-condition is stronger than the Muckenhoupt type $A_{p,q}$-condition.
Monetary Policy Rules and the International Monetary Transmission. CEPS Working Document No. 191, October 2002

This paper analyses alternative monetary policy rules for the ECB, using a two "country" model of the euro area and the US, that assumes monopolistic competition, sticky prices and optimizing agents. The alternative rules analyzed for the ECB are ranked by their ability to stabilize consumption, output, and inflation and maximize consumers’ welfare. The analysis contributes toward understanding the trade-offs faced by policymakers in open economies and provides some support for the current design of the ECB’s operational framework. The results suggest that stabilizing money-growth, in addition to inflation, gives an additional degree of freedom to stabilize output. Although price stability is likely to remain the primary objective of the ECB, monetary policy must “without prejudice of price stability (.) support the general economic policies in the Community. ” (Article 2). Hence monitoring money, under certain assumptions about the shocks hitting the economy, may deliver a better outcome in terms of output stabilization which should allow the ECB to fulfill its secondary but nonetheless important commitment.
The generalization error of max-margin linear classifiers: High-dimensional asymptotics in the overparametrized regime

Modern machine learning models are often so complex that they achieve vanishing classification error on the training set. Max-margin linear classifiers are among the simplest classification methods that have zero training error (with linearly separable data). Despite this simplicity, their high-dimensional behavior is not yet completely understood. We assume to be given i.i. d. data $(y_i,{\boldsymbol x}_i)$, $i\le n$ with ${\boldsymbol x}_i\sim {\sf N}({\boldsymbol 0},{\boldsymbol \Sigma})$ a $p$-dimensional Gaussian feature vector, and $y_i \in\{+1,-1\}$ a label whose distribution depends on a linear combination of the covariates $\langle {\boldsymbol \theta}_*,{\boldsymbol x}_i\rangle$. We consider the proportional asymptotics $n,p\to\infty$ with $p/n\to \psi$, and derive exact expressions for the limiting prediction error. Our asymptotic results match simulations already when $n,p$ are of the order of a few hundreds. We explore several choices for the the pair $({\boldsymbol \theta}_*,{\boldsymbol \Sigma})$, and show that the resulting generalization curve (test error error as a function of the overparametrization ratio $\psi=p/n$) is qualitatively different, depending on this choice. In particular we consider a specific structure of $({\boldsymbol \theta}_*,{\boldsymbol \Sigma})$ that captures the behavior of nonlinear random feature models or, equivalently, two-layers neural networks with random first layer weights. In this case, we observe that the test error is monotone decreasing in the number of parameters. This finding agrees with the recently developed `double descent' phenomenology for overparametrized models.
Attributed Sequence Embedding

Mining tasks over sequential data, such as click-streams and gene sequences, require a careful design of embeddings usable by learning algorithms. Recent research in feature learning has been extended to sequential data, where each instance consists of a sequence of heterogeneous items with a variable length. However, many real-world applications often involve attributed sequences, where each instance is composed of both a sequence of categorical items and a set of attributes. In this paper, we study this new problem of attributed sequence embedding, where the goal is to learn the representations of attributed sequences in an unsupervised fashion. This problem is core to many important data mining tasks ranging from user behavior analysis to the clustering of gene sequences. This problem is challenging due to the dependencies between sequences and their associated attributes. We propose a deep multimodal learning framework, called NAS, to produce embeddings of attributed sequences. The embeddings are task independent and can be used on various mining tasks of attributed sequences. We demonstrate the effectiveness of our embeddings of attributed sequences in various unsupervised learning tasks on real-world datasets.
Progress and Application Prospect of IPS Cells

Differentiated somatic cells can be reprogrammed into induced pluripotent stem cells (iPS cells) by introducing specific transcription factors. This technique avoids immune rejection and ethical problems in stem cell research. A great revolution in the fi eld of science. As with embryonic stem cells (ES cells), iPS cells are able to self-renew and maintain undiff erentiated state. In vitro, iPS cells can be induced to diff erentiate into a variety of mature cells, therefore, iPS cells in theoretical research and clinical applications are extremely valuable. IPS cell diff erentiation and transplantation in the treatment of blood diseases have a great use, iPS cells can treat nervous system diseases, to provide in vitro disease model, to study the mechanism of disease formation, screening new drugs and the development of new to provide a new treatment The The use of iPS cells as a nuclear donor cell, with the appropriate receptor cells after fusion can be directly obtained transgenic animals. Not only can improve the genetic nature of animals, but also can break the boundaries of species and get the new animal traits that cannot achieve by using traditional mating methods. The research of iPS cells has been widely concerned, and it is the research hotspot in cell biology and molecular biology. In this paper, the defi nition of iPS cells, the acquisition of iPS cells, the history of development, the signifi cance of research, the progress of research, the application of iPS cells, and the problems of iPS cells were reviewed.
Size increase in high elevation ground squirrels over the last century

There is increasing evidence for morphological change in response to recent environmental change, but how this relates to fluctuations in geographic range remains unclear. We measured museum specimens from two time periods (1902–1950 and 2000–2008) that vary significantly in climate to assess if and how two high elevation contracting species of ground squirrels in the Sierra Nevada of California, Belding's ground squirrel (Urocitellus beldingi) and the golden‐mantled ground squirrel (Callospermophilus lateralis), and one lower elevation, stable species, the California ground squirrel (Otospermophilus beecheyi), have responded morphologically to changes over the last century. We measured skull length (condylobasal length), an ontogenetically more labile trait highly correlated with body size, and maxillary toothrow length, a more developmentally constrained trait predictive of skull shape. C. lateralis and U. beldingi, both obligate hibernators, have increased in body size, but have not changed in shape. In contrast, O. beecheyi, which only hibernates in parts of its range, has shown no significant change in either morphometric trait. The increase in body size in the higher elevation species, hypothesized to be a plastic effect due to a longer growing season and thus prolonged food availability, opposes the expected direction of selection for decreased body size under chronic warming. Our study supports that population contraction is related to physiological rather than nutritional constraints.
FCSR-GAN: Joint Face Completion and Super-Resolution via Multi-Task Learning

Combined variations containing low-resolution and occlusion often present in face images in the wild, e.g., under the scenario of video surveillance. While most of the existing face image recovery approaches can handle only one type of variation per model, in this work, we propose a deep generative adversarial network (FCSR-GAN) for performing joint face completion and face super-resolution via multi-task learning. The generator of FCSR-GAN aims to recover a high-resolution face image without occlusion given an input low-resolution face image with occlusion. The discriminator of FCSR-GAN uses a set of carefully designed losses (an adversarial loss, a perceptual loss, a pixel loss, a smooth loss, a style loss, and a face prior loss) to assure the high quality of the recovered high-resolution face images without occlusion. The whole network of FCSR-GAN can be trained end-to-end using our two-stage training strategy. Experimental results on the public-domain CelebA and Helen databases show that the proposed approach outperforms the state-of-the-art methods in jointly performing face super-resolution (up to $8\times $ ) and face completion, and shows good generalization ability in cross-database testing. Our FCSR-GAN is also useful for improving face identification performance when there are low-resolution and occlusion in face images. The code of FCSR-GAN is available at: https://github.com/swordcheng/FCSR-GAN.
Detect Toxic Content to Improve Online Conversations

Social media is filled with toxic content. The aim of this paper is to build a model that can detect insincere questions. We use the 'Quora Insincere Questions Classification' dataset for our analysis. The dataset is composed of sincere and insincere questions, with the majority of sincere questions. The dataset is processed and analyzed using Python and its libraries such as sklearn, numpy, pandas, keras etc. The dataset is converted to vector form using word embeddings such as GloVe, Wiki-news and TF-IDF. The imbalance in the dataset is handled by resampling techniques. We train and compare various machine learning and deep learning models to come up with the best results. Models discussed include SVM, Naive Bayes, GRU and LSTM.
HYPOGLYCAEMIC ACTIVITY OF AERIAL PARTS OF ARGEMONE MEXICANA L. IN EXPERIMENTAL RAT MODELS Research Article

Experimental Design, Materials and Methods: The aim of the present study was focus on, to the evaluation of hypoglycaemic activity of hydroalcoholic extract of aerial parts of Argemone mexicana L. (HAAM). The study was carried out on normal, glucose loaded, and streptozotocin (STZ) induced hyperglycaemic rats, using standard experimental procedures at dose levels of 200 and 400 mg/kg by, p.o, by taking Metformin (300 mg/kg) as standard drug. Statistical Analysis: The data are analyzed by one way ANOVA followed by Turkey – Kramer Multiple Comparison test, p < 0.05 considered as significant. Results: In oral glucose loaded rat model (normal rats and hyperglycemic rats) and in single/multiple (14-days) dose rat models (hyperglycemic rats) HAAM at both the dose levels (200 mg/kg and 400 mg/kg, p.o.) reduces the fasting blood glucose level significantly (p < 0.001) in a dose dependent manner, when compared to control group of animals. The effect of the extract at higher dose (400 mg/kg) is comparable with the standard drug metformin (300 mg/kg), in most of the tested animal models. Conclusion: The hydro-alcoholic extract of Argemone mexicana stem is endowed with hypoglycemic potential and the test dose of 400 mg/kg for 14days is most parallel with that of the effect of standard drug metformin.
Averaging Attacks on Bounded Noise-based Disclosure Control Algorithms

Abstract We describe and evaluate an attack that reconstructs the histogram of any target attribute of a sensitive dataset which can only be queried through a specific class of real-world privacy-preserving algorithms which we call bounded perturbation algorithms. A defining property of such an algorithm is that it perturbs answers to the queries by adding zero-mean noise distributed within a bounded (possibly undisclosed) range. Other key properties of the algorithm include only allowing restricted queries (enforced via an online interface), suppressing answers to queries which are only satisfied by a small group of individuals (e.g., by returning a zero as an answer), and adding the same perturbation to two queries which are satisfied by the same set of individuals (to thwart differencing or averaging attacks). A real-world example of such an algorithm is the one deployed by the Australian Bureau of Statistics’ (ABS) online tool called TableBuilder, which allows users to create tables, graphs and maps of Australian census data [30]. We assume an attacker (say, a curious analyst) who is given oracle access to the algorithm via an interface. We describe two attacks on the algorithm. Both attacks are based on carefully constructing (different) queries that evaluate to the same answer. The first attack finds the hidden perturbation parameter r (if it is assumed not to be public knowledge). The second attack removes the noise to obtain the original answer of some (counting) query of choice. We also show how to use this attack to find the number of individuals in the dataset with a target attribute value a of any attribute A, and then for all attribute values ai ∈ A. None of the attacks presented here depend on any background information. Our attacks are a practical illustration of the (informal) fundamental law of information recovery which states that “overly accurate estimates of too many statistics completely destroys privacy” [9, 15].
Finite Blocklength Analysis of the MISO Coherent AdM 1 n MASSACHUSETS INSTr

The Coherent MISO Block Fading Channel is a wireless communication channel model in which the transmitter has access to multiple antennas while the receiver has access to one. This model is becoming increasingly important in communication networks because a base station is often able to use many antennas whereas smaller receivers, such as mobile devices, have a strict size limitation. The capacity of this channel is known, but the finite blocklength limits of codes over this channel is still unknown. In this thesis, we analyze these finite blocklength fundamental limits. Specifically, we give the coding theorem showing the achievable dispersion of this channel. We identify that this channel has non-unique capacity achieving input distributions, and although these distribution all yield the same capacity, some distributions in this set yield a better dispersion than others. We show that orthogonal design input distributions achieve capacity in dimensions where they exist, and that they strictly outperform schemes that purely aim to maximize multiplexing gain from a finite blocklength perspective. Thesis Supervisor: Yury Polyanskiy Title: Robert J. Shillman Assistant Professor
CommonVariants at 8q24AreAssociatedWith

Marcelo Chen, Yu-Chuen Huang, Stone Yang, Jong-Ming Hsu, Yen-Hwa Chang, William Ji-Shian Huang, and Yi-Ming Arthur Chen* Divisionof PreventiveMedicine, Institute of PublicHealth, SchoolofMedicine, National Yang-MingUniversity,Taipei,Taiwan DepartmentofUrology,HsinchuMackayMemorialHospital,Hsinchu,Taiwan MackayCollege ofNursingandMedicine,Taipei,Taiwan DepartmentofMedical Research,Genetics Center,ChinaMedical UniversityHospital,Taichung,Taiwan Graduate InstituteofChineseMedicalScience,CollegeofChineseMedicine,ChinaMedicalUniversity,Taichung,Taiwan DepartmentofUrology,TaipeiMackayMemorialHospital,Taipei,Taiwan DivisionofUrology,Departmentof Surgery,Taipei VeteransGeneralHospital,Taipei,Taiwan Institute ofMicrobiologyand Immunology, SchoolofMedicine,National Yang-MingUniversity,Taipei,Taiwan
Chemical systematics of an intermediate spreading ridge: The Pacific-Antarctic Ridge between 56°S and 66°S

Axial bathymetry, major/trace elements, and isotopes suggest that the Pacific-Antarctic Ridge (PAR) between 56°S and 66°S is devoid of any hotspot influence. PAR (56-66°S) samples have in average lower 87Sr/86Sr and 143 Nd/ 144 Nd and higher 206 PbP04 Pb than northern Pacific l11id­ ocean ridge basalts (MORB), and also than MORB From the other oceans. The high variability of pb isotopic ratios (compared to Sr and Nd) can be due to either a general high ~l (I-IIMU) (high U/Pb) affinity of the southern Pacific upper mantle or to a mantle event first recorded in time by Pb isotopes. Compiling the results ofthis study with those From the PAR between 53°S and 57°S gives a continuous vie~ of mantle characteristics fr~m sOl~th ,Pitman. Fracture Z? ne (FZ) to . Vacquier FZ, representll1g about 3000 km of spreadll1g aXIs. (he latitude ofUdmtsev FZ (56°S) IS a limit between, to the narth, a domain with large geochemical variations and, to the south, one with small variations. The spreading rate has intermediate values (54 mm/yr at 66°S to 74 mm/yr at 56°S) which increase along the PAR, while the axial morphology changes from valley to dome. The morphological transition is not recarded by the chemical properties of the ridge basalts nor by the inferred mantle temperature which displays few variations (30-40°C) along the PAR. Contrat y to what is observed along the South-East Indian Ridge, PAR morphology appears to be controlled more by spreading rate rather than by mantle temperature. Mu ch of the major and trace element variability results from segmentation control on the shallowest thermal structure of the mantle. The cold edge of a fracture zone seems to be more efficient when occurring in an axial dome context. It is expressed as an increase of the magnitude of the Transform Fault Effect along the valley-dome transition, resulting in a clear increase oftrace element ratio variability (such as Nb/Zr). There is no strong evidence for the previously proposed southwestward asthenospheric flow in the area. However, this flow model could explain the intrasegment asymmetric patterns.
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization

As well known, the huge memory and compute costs of both artificial neural networks (ANNs) and spiking neural networks (SNNs) greatly hinder their deployment on edge devices with high efficiency. Model compression has been proposed as a promising technique to improve the running efficiency via parameter and operation reduction, whereas this technique is mainly practiced in ANNs rather than SNNs. It is interesting to answer how much an SNN model can be compressed without compromising its functionality, where two challenges should be addressed: 1) the accuracy of SNNs is usually sensitive to model compression, which requires an accurate compression methodology and 2) the computation of SNNs is event-driven rather than static, which produces an extra compression dimension on dynamic spikes. To this end, we realize a comprehensive SNN compression through three steps. First, we formulate the connection pruning and weight quantization as a constrained optimization problem. Second, we combine spatiotemporal backpropagation (STBP) and alternating direction method of multipliers (ADMMs) to solve the problem with minimum accuracy loss. Third, we further propose activity regularization to reduce the spike events for fewer active operations. These methods can be applied in either a single way for moderate compression or a joint way for aggressive compression. We define several quantitative metrics to evaluate the compression performance for SNNs. Our methodology is validated in pattern recognition tasks over MNIST, N-MNIST, CIFAR10, and CIFAR100 datasets, where extensive comparisons, analyses, and insights are provided. To the best of our knowledge, this is the first work that studies SNN compression in a comprehensive manner by exploiting all compressible components and achieves better results.
Flow can order: Phases of live XY spins in two dimensions

We present the hydrodynamic theory of active XY spins coupled with flow fields, for systems both having and or lacking number conservation in two dimensions (2D). For the latter, with strong activity or nonequilibrium drive, the system can synchronize, or be phase-ordered with various types of order, e.g., quasi long range order (QLRO) or new kind of order weaker or stronger than QLRO for sufficiently strong active flow-phase couplings. For the number conserving case, the system can show QLRO or order weaker than QLRO, again for sufficiently strong active flow-phase couplings. For other choices of the model parameters, the system necessarily disorders in a manner similar to immobile but active XY spins, or 2D Kardar-Parisi-Zhang surfaces.
Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)

We implement a Tensor Train layer in the TensorFlow Neural Machine Translation (NMT) model using the t3f library. We perform training runs on the IWSLT English-Vietnamese '15 and WMT German-English '16 datasets with learning rates $\in \{0.0004,0.0008,0.0012\}$, maximum ranks $\in \{2,4,8,16\}$ and a range of core dimensions. We compare against a target BLEU test score of 24.0, obtained by our benchmark run. For the IWSLT English-Vietnamese training, we obtain BLEU test/dev scores of 24.0/21.9 and 24.2/21.9 using core dimensions $(2, 2, 256) \times (2, 2, 512)$ with learning rate 0.0012 and rank distributions $(1,4,4,1)$ and $(1,4,16,1)$ respectively. These runs use 113\% and 397\% of the flops of the benchmark run respectively. We find that, of the parameters surveyed, a higher learning rate and more `rectangular' core dimensions generally produce higher BLEU scores. For the WMT German-English dataset, we obtain BLEU scores of 24.0/23.8 using core dimensions $(4, 4, 128) \times (4, 4, 256)$ with learning rate 0.0012 and rank distribution $(1,2,2,1)$. We discuss the potential for future optimization and application of Tensor Train decomposition to other NMT models.