Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanotheranostics Targeting the Tumor Microenvironment

Cancer is considered the most aggressive malignancy to humans, and definitely the major cause of death worldwide. Despite the different and heterogenous presentation of the disease, there are pivotal cell elements involved in proliferation, differentiation, and immortalization, and ultimately the capability to evade treatment strategies. This is of utmost relevance when we are just beginning to grasp the complexity of the tumor environment and the molecular “evolution” within. The tumor micro-environment (TME) is thought to provide for differentiation niches for clonal development that results in tremendous cancer heterogeneity. To date, conventional cancer therapeutic strategies against cancer are failing to tackle the intricate interplay of actors within the TME. Nanomedicine has been proposing innovative strategies to tackle this TME and the cancer cells that simultaneously provide for biodistribution and/or assessment of action. These nanotheranostics systems are usually multi-functional nanosystems capable to carry and deliver active cargo to the site of interest and provide diagnostics capability, enabling early detection, and destruction of cancer cells in a more selective way. Some of the most promising multifunctional nanosystems are based on gold nanoparticles, whose physic-chemical properties have prompt for the development of multifunctional, responsive nanomedicines suitable for combinatory therapy and theranostics. Herein, we shall focus on the recent developments relying on the properties of gold nanoparticles as the basis for nanotheranostics systems against the heterogeneity within the TME.
Pre- and Post-therapy Assessment of Clinical Outcomes and White Matter Integrity in Autism Spectrum Disorder: Pilot Study

Objective: This pilot study aims to identify white matter (WM) tract abnormalities in Autism Spectrum Disorders (ASD) toddlers and pre-schoolers by Diffusion Tensor Imaging (DTI), and to correlate imaging findings with clinical improvement after early interventional and Applied Behavior Analysis (ABA) therapies by Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP). Methods: DTI scans were performed on 17 ASD toddlers/pre-schoolers and seven age-matched controls. Nine ASD patients had follow-up MRI 12 months following early intervention and ABA therapy. VB-MAPP was assessed and compared at diagnosis, 6 and 12 months after therapies. Tract-Based Spatial Statistics (TBSS) was used to measure fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial (RD) diffusivity. Results: VB-MAPP scores improved at 6 and 12 months after early intervention and ABA therapy compared to scores at baseline. TBSS analysis showed significant FA decrease and/or RD increase in ASD patients before therapy vs. controls in inferior fronto-occipital fasciculi, uncinate fasciculi, left superior fronto-occipital fasciculus, forceps minor, left superior fronto-occipital fasciculus, right superior longitudinal fasciculus, corona radiate bilaterally, and left external capsule. A significantly FA increase in 21 tracts and ROIs is reported in post- vs. pre-therapy DTI analysis. Conclusion: DTI findings highlighted ASD patient WM abnormalities at diagnosis and confirmed the benefits of 12 months of early intervention and ABA therapy on clinical and neuro imaging outcomes.
Modeling Selectivity in Households' Purchase Quantity Outcomes: A Count Data Approach

We present an econometric technique for modeling endogenous selectivity in households quantity outcomes as observed in scanner panel data. Simultaneous models of incidence, brand choice and quantity, that treat quantity outcomes as count data, ignore such self-selectivity considerations in quantity outcomes. Previously proposed approaches to modeling selectivity in continuous quantity outcomes do not apply to count data. Therefore, we adopt a recently proposed econometric technique to deal with selectivity in count data, and then appropriately extend it to handle correlations of quantity outcomes not only with incidence outcomes but also with brand choice outcomes. Our proposed methodology will be useful to researchers who want to estimate simultaneous models of whether, what and how much to buy decisions of households, treating quantity data as counts.
International Journal of Emerging Electric Power Systems A Novel Approach for Classifying Transient Phenomena in Power Transformers

The role of a power transformer protective relay is to rapidly operate the tripping during internal faults and block the tripping during magnetizing inrush. This paper presents a new approach for classifying transient phenomena in power transformer, which may be implemented in digital relaying for transformer differential protection. Discrimination between internal faults, external faults with current transformer saturation and magnetizing inrush currents is achieved by combining wavelet transforms and fuzzy logic. The wavelet transform is applied for the analysis of the power transformer transient phenomena because of its ability to extract information from the transient signals in both time and frequency domain. Fuzzy logic is used because of the uncertainty in the differential current signals and relay settings. MATLAB power system toolbox is used to generate current signals at both sides of a power transformer in a typical system with various conditions. The simulation results obtained show that the new algorithm provides a high operating sensitivity for internal faults and remains stable for external faults and inrush currents.
Analysis of SWT-SVD Digital Image Watermarking Technique

: Last few years digital watermarking has facilitated the protection of copyright information through embedding the hidden information into the digital content. Digital image watermarking is one such the technology that has been developed to protect digital images from illegal manipulations. In this paper a new SWT-SVD semi-blind composite images watermarking algorithm that is robust against various attacks is presented . We use SWT transform to obtain 4-diffirent frequency images. Then we apply SVD on each subsection to modifying their singular value, on the basis of MSE & PSNR values Experimental evaluation demonstrates that proposed algorithm is able to withstand variety of attacks as Salt & Pepper noise, Rotation, Median filter, Vertical Mirroring, Horizontal Mirroring, Gaussian noise, Cropping, Contrast etc. Watermark is recovered efficiently.
Bayesian Analysis of a Doubly Truncated ARMA-GARCH Model

We develop a new Markov Chain Monte Carlo procedure for a time series regression model truncated by upper and lower bounds. The regression error term is assumed to follow an ARMA– GARCH process. We use a convergence diagnostics with a simultaneous test of mean and covariance stationarity and discuss model selection criteria. Using MCMC procedure we test the purchasing power parity theory for the Japanese yen controlled to fluctuate in a narrow band and find that the theory is supported if double truncation is incorporated in estimation. ∗We thank Teruo Nakatsuma for providing us his Gauss code with MCMC algorithm for utruncated ARMA-GARCH model used in Nakatsuma, T. (2000a). Elena Goldman, Lubin School of Business, Pace University, One Pace Plaza, New York, NY 10038 Hiroki Tsurumi, Department of Economics, Rutgers University, 75 Hamilton Street, New Brunswick, NJ 08901
Debating Singlish

In his encyclopedia of language, Crystal (1995) identified two main perspectives contributing to a nation’s decisions around language: national identity and internationalism. In the former, the nation looks within itself at its societal structure and composition, defining its needs in relation to national identity. In the latter, the nation looks outside from itself at the world and global economy, and defines itself and its needs in relation to its position within that world. In multilingual Singapore, it is the juxtaposition of these two perspectives, which I call ‘glocalism’, that form the nexus of the nation’s ‘English’-plus-‘mother-tongue’ bilingual policy. And it defines the parameters of the ongoing debate about English and Singlish, the more colloquial and home-grown variety of English in Singapore. In this article, I will unpack this ‘Singlish debate’, positioning the debate in the growing body of literature on language ideologies and the ways in which they mediate between social structure and linguistic practices.
Range extension and first Brazilian record of the rare Hyladelphys kalinowskii (Hershkovitz, 1992) (Didelphimorphia, Didelphidae) / Extension de distribution et première occurrence au Brésil du rare Hyladelphys kalinowskii (Hershkovitz, 1992) (Didelphimorphia, Didelphidae)

The number of species of didelphid marsupials described has been regularly increasing in the last 10 years. Since the last comprehensive survey by Gardner (1993), listing 15 genera and 63 species, at least 19 species formerly considered to be junior synonyms were revalidated in the genera Didelphis (Lemos and Cerqueira 2002, Ventura et al. 2002), Gracilinanus (Voss et al. 2001, Voss and Jansa 2003), Marmosa (Voss et al. 2001), Marmosops (Mustrangi and Patton 1997, Patton et al. 2000, Voss et al. 2001), Micoureus (Emmons and Feer 1997, Patton and Costa 2003), Monodelphis (Lemos et al. 2000, Voss et al. 2001), Philander (Patton and da Silva 1997) and Thylamys (Flores et al. 2000, Solari 2003). Furthermore, at least one new species was named (Dı́az et al. 2002), and another described by Hershkovitz (1992) but not included by Gardner (1993) was placed in a new genus (Voss et al. 2001). Lastly, another new genus was proposed for the taxon formerly known as Marmosa canescens (Voss and Jansa 2003). This increases the total number of species to 86 (an increase of more than 35%) and the number of genera to 17. In Brazil, since the publication of an annotated checklist by Fonseca et al. (1996) that listed 44 species, at least eight new taxa have been added, either through the taxonomic changes mentioned above or through new country records. All of these data indicate that our current knowledge of the diversity and distribution of Neotropical and Brazilian marsupials is still far from complete; therefore, new information on the distribution of species, particularly rare ones, is essential.
Conflict and Conflict Management with Interdependent Instruments and Asymmetric Stakes, (The Good-Cop and the Bad-Cop Game)

This paper considers a partial equilibrium model of conflict where two asymmetric, rational and risk-neutral opponents clash in order to redistribute a divisible prize in their favour. Differently from common contest models agents have the option of choosing a second instrument to affect the outcome of the conflict. The second instrument is assumed to capture a complex bundle of Conflict Management Procedures (CMPs). Through comparative statics, different scenarios are studied. A Potential Settlement Region (PSR) is presented as the set of all possible settlement points. First, the role of asymmetry in the evaluation of the contested stake has been underlined. The agent with the lower evaluation will expend efforts in conflict management only when the asymmetry is extremely large. When agents are asymmetrical both in evaluation of the stake and in fighting abilities, there is also a smaller PSR. Once the destruction parameter is considered, agents clearly also take into account the opportunity cost of the conflict and enlarge a PSR. Finally, throughout the paper, the concept of entropy has been applied as a tentative tool for the measurement and evaluation of conflict and conflict management.
E-MAIL CLASSIFICATION USING DEEP NETWORKS

Email has become an essential communication tool in modern life, creating the need to manage the huge information generated. Email classification is a desirable feature in an email client to manage the email messages and categorize them into semantic groups. Statistical artificial intelligence and machine learning is a typical approach to solve such problem, driven by the success of such methods in other areas of knowledge management. Many classification methods exist, of which some have been already applied to email classification task, like Naive Bayes Classifiers and SVM. Deep architectures of pattern classifiers represent a wide category of classifiers. Recently, Deep Belief Networks have demonstrated good performance in literature, driven by the fast learning algorithm using Restricted Boltzmann Machine model by Hinton et al, and the improvement in computing power which enables learning deep neural networks in reasonable time. Many datasets and corpus exist for email classification, with the most famous one is Enron dataset, made public by FERC, annotated and processed by many entities like SRI and MIT. In this paper, a machine learning approach using Deep Belief Networks is applied to email classification task, using the Enron dataset to train and test the proposed model.
International Diversification Benefits between US, Turkish and Egyptian Stock Markets

International portfolio diversification benefits between US stock markets and corresponding markets in Turkey and Egypt are examined from a short- and long-term perspective. The Johansen cointegration procedure reveals cointegration at the general index level related to some but not all sub-indexes investigated. Granger causality indicates, in general, causality running between USA and Turkey but only isolated cases involving Egypt. Overall, the results suggest that US investors can obtain diversification benefits at a sub-index level given a long-term investment horizon restricted to positions in one but not both Middle Eastern markets in one and the same portfolio. Short-term benefits involve the Egyptian market related to two out of three sub-indexes.
Centre for Dynamic Macroeconomic Analysis Working Paper Series a Model of Near-rational Exuberance

the referees of this journal, the participants at the ECB conference as well as participants at many other conferences and seminars for their comments. Any views expressed are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of St. Louis, of the Federal Reserve System or of the Bank of Finland. ABSTRACT We study how the use of judgement or " add-factors " in forecasting may disturb the set of equilibrium outcomes when agents learn using recursive methods. We isolate conditions under which new phenomena, which we call exuberance equilibria, can exist in a standard self-referential environment. Local indeterminacy is not a requirement for existence. We construct a simple asset pricing example and find that exuberance equilibria, when they exist, can be extremely volatile relative to fundamental equilibria.
Toward Precise Stellar Ages: Combining Isochrone Fitting with Empirical Gyrochronology

We present a new age-dating technique that combines gyrochronology with isochrone fitting to infer ages for FGKM main-sequence and subgiant field stars. Gyrochronology and isochrone fitting are each capable of providing relatively precise ages for field stars in certain areas of the Hertzsprung–Russell diagram (HRD): gyrochronology works optimally for cool main-sequence stars, and isochrone fitting can provide precise ages for stars near the main-sequence turnoff. Combined, these two age-dating techniques can provide precise and accurate ages for a broader range of stellar masses and evolutionary stages than either method used in isolation. We demonstrate that the position of a star on the HRD or color–magnitude diagram can be combined with its rotation period to infer a precise age via both isochrone fitting and gyrochronology simultaneously. We show that incorporating rotation periods with 5% uncertainties into stellar evolution models improves age precision for FGK stars on the main sequence and can, on average, provide age estimates up to three times more precise than isochrone fitting alone. In addition, we provide a new gyrochronology relation, calibrated to the Praesepe cluster and the Sun, that includes a variance model to capture the rotational behavior of stars whose rotation periods do not lengthen with the square root of time and parts of the HRD where gyrochronology has not been calibrated. This publication is accompanied by an open-source Python package (stardate) for inferring the ages of main-sequence and subgiant FGKM stars from rotation periods, spectroscopic parameters, and/or apparent magnitudes and parallaxes.
Spontaneous cooperation for public goods

ABSTRACT Cooperation for public goods poses a dilemma, where individuals are tempted to free ride on others’ contributions. Classic solutions involve monitoring, reputation maintenance and costly incentives, but there are important collective actions based on simple and cheap cues only, for example, unplanned protests and revolts. This can be explained by an Ising model with the assumption that individuals in uncertain situations tend to conform to the local majority in their network. Among initial defectors, noise such as rumors or opponents’ provocations causes some of them to cooperate accidentally. At a critical level of noise, these cooperators trigger a cascade of cooperation. We find an analytic relationship between the phase transition and the asymmetry of the Ising model, which in turn reflects the asymmetry of cooperation and defection. This study thereby shows that in principle, the dilemma of cooperation can be solved by nothing more than a portion of random noise, without rational decision-making.
A Lyapunov analysis for accelerated gradient methods: from deterministic to stochastic case

Recent work by Su, Boyd and Candes made a connection between Nesterov's accelerated gradient descent method and an Ordinary Differential Equation (ODE). We show that this connection can be extended to the case of stochastic gradients, and develop Lyapunov function based convergence rates proof for Nesterov's accelerated stochastic gradient descent. In the gradient case, we show Nesterov's method arises as a straightforward discretization of a modified ODE. Established Lyapunov analysis is used to recover the accelerated rates of convergence in both continuous and discrete time. Moreover, the Lyapunov analysis can be extended to the case of stochastic gradients. The result is a unified approach to acceleration in both continuous and discrete time, and in for both stochastic and full gradients.
Counter-rotation and High-velocity Outflow in the Parsec-scale Molecular Torus of NGC 1068

We present 1.4 pc resolution observations of 256 GHz nuclear radio continuum and HCN (J = 3 → 2) in the molecular torus of NGC 1068. The integrated radio continuum emission has a flat spectrum consistent with free–free emission and resolves into an X-shaped structure resembling an edge-brightened bicone. HCN is detected in absorption against the continuum, and the absorption spectrum shows a pronounced blue wing that suggests a high-velocity molecular outflow with speeds reaching 450 km s−1. Analysis of the off-nucleus emission line kinematics and morphology reveals two nested, rotating disk components. The inner disk, inside r ∼ 1.2 pc, has kinematics that are consistent with the nearly edge-on, geometrically thin H2O megamaser disk in Keplerian rotation around a central mass of 1.66 × 107 M⊙. The outer disk, which extends to ∼7 pc radius, counter-rotates relative to the inner disk. The rotation curve of the outer disk is consistent with rotation around the same central mass as the megamaser disk but in the opposite sense. The morphology of the molecular gas is asymmetric around the nuclear continuum source. We speculate that the outer disk formed from more recently introduced molecular gas falling out of the host galaxy or from a captured dwarf satellite galaxy. In NGC 1068, we find direct evidence that the molecular torus consists of counter-rotating and misaligned disks on parsec scales.
Breaking the Bandwidth Limit: A Review of Broadband Doherty Power Amplifier Design for 5G

The next generation wireless network, 5G, is expected to provide ubiquitous connections to billions of devices as well as to unlock many new services through multigigabit-per-second data transmission. To meet the ever-increasing demands for higher data rates and larger capacities, new modulation schemes have been developed, and wider frequency bands, such as those at millimeter wave (mm-wave), have been designated for 5G [1], [2]. Massive multiple input/multiple output (MIMO), which uses a large number of antennas at the transmitter and receiver, has been considered one of the key 5G technologies to improve data throughput and spectrum efficiency [3]. These new application scenarios impose stringent requirements on wireless transceiver front ends and call for special considerations at the circuit and system design levels. In the transmitter, power amplifiers (PAs) should accommodate complex modulated signals, featuring a high peak-to-average-power ratio (PAPR) and a wide modulation bandwidth. Moreover, in massive MIMO arrays, PAs should maintain a high average efficiency to mitigate thermal heating issues.
A novel text representation which enables image classifiers to perform text classification, applied to name disambiguation

Patent data are often used to study the process of innovation and research, but patent databases lack unique identifiers for individual inventors, making it difficult to study innovation processes at the individual level. Here we introduce an algorithm that performs highly accurate disambiguation of inventors (named entities) in US patent data (F1: 99.09%, precision: 99.41%, recall: 98.76%). The algorithm involves a novel method for converting text-based record data into abstract image representations, in which text from a given pairwise comparison between two inventor name records is converted into a 2D RGB (stacked) image representation. We train an image classification neural network to discriminate between such pairwise comparison images, and then use the trained network to label each pair of records as either matched (same inventor) or non-matched (different inventors). The resulting disambiguation algorithm produces highly accurate results, out-performing other inventor name disambiguation studies on US patent data. Our new text-to-image representation method could potentially be used more broadly for other NLP comparison problems, as it allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems (such as disambiguation of academic publications, or data linkage problems).
An Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing

Multi-Task Learning (MTL) aims at boosting the overall performance of each individual task by leveraging useful information contained in multiple related tasks. It has shown great success in natural language processing (NLP). Currently, a number of MLT architectures and learning mechanisms have been proposed for various NLP tasks. However, there is no systematic exploration and comparison of different MLT architectures and learning mechanisms for their strong performance in-depth. In this paper, we conduct a thorough examination of typical MTL methods on a broad range of representative NLP tasks. Our primary goal is to understand the merits and demerits of existing MTL methods in NLP tasks, thus devising new hybrid architectures intended to combine their strengths.