Miracle-dz/UniFAR-FTUs huggingface dataset:
Intellectual Capital Management and Reporting in Universities and Research Institutions

The paper intends to show how the IC principles, developed mainly for companies over the last two decades, can be applied as well to Universities and Research Institutions. Firstly, it explores recent theoretical backgrounds which affect Universities, such as the “Mode 2” of Knowledge production and the Triple Helix Model. Secondly it analyses how similar objectives and procedures are when managing intangibles in both companies and Universities. Then it examines whether the structure of IC reporting in companies may be useful for Universities, providing some examples for Europe, and how the harmonization and external control principles are relevant for public institutions. The main conclusion is that IC principles can broadly apply to Universities and that reporting on their IC would be beneficial for them and the society as a whole, which provides the rationale for public intervention. Mediante este articulo se pretende mostrar como los principios en los que se basa el ‘Capital Intelectual’, desarrollados principalmente durante las dos ultimas decadas en el ambito empresarial, pueden ser tambien aplicados a las Universidades e Instituciones de investigacion. Para ello se exponen, en primer lugar, los fundamentos teoricos mas recientes que afectan a las Universidades, tales como el ‘Modo 2’ de produccion de conocimiento y el ‘Modelo de triple helice’. En segundo termino, se analiza la existencia de objetivos y procedimientos similares para gestionar los intangibles, tanto en las empresas como en las Universidades. Seguidamente se examina si la estructura del Informe de Capital Intelectual utilizado por las empresas podria servir para el de las Universidades, proporcionando algunos ejemplos relativos a Europa, y poniendose igualmente de manifiesto que la armonizacion y los principios de control externo son tambien relevantes para las instituciones publicas. La principal conclusion a la que se llega es que la aplicacion de los principios que informan el Capital Intelectual puede extenderse al ambito de las Universidades y que la emision por parte de estas de un Informe de Capital Intelectual podria ser beneficioso para ellas y para el conjunto de la sociedad, proporcionando asi una exposicion razonada de la intervencion publica.
Local rainfall‐SST relationship on subseasonal time scales in satellite observations and CFS

The present study documents local rainfall‐SST relationship on subseasonal time scales in the tropical Indo‐western Pacific region based on satellite observations and the Climate Forecast System (CFS). It is found that the intensity of subseasonal air‐sea coupling strongly depends on the season with most prominent interaction in the summer hemisphere. The time lag between rainfall and SST displays pronounced spatial variations and asymmetric feature. In observations, the regions of the atmosphere forcing the SST tend to shift to subtropics compared to those of the SST forcing the atmosphere. In the CFS, the regions of the atmosphere forcing the SST tend to be collocated with those of the SST forcing the atmosphere. The CFS captures reasonably well the seasonality in the intensity of air‐sea coupling, but probably with an overestimation of the coupling strength. The SST response to the atmosphere takes longer time in the CFS compared to observations.
Improving Performance of Private Federated Models in Medical Image Analysis

Federated learning (FL) is a distributed machine learning (ML) approach that allows data to be trained without being centralized. This approach is particularly beneﬁcial for medical applications because it addresses some key challenges associated with medical data, such as privacy, security, and data ownership. On top of that, FL can improve the quality of ML models used in medical applications. Medical data is often diverse and can vary signiﬁcantly depending on the patient population, making it challenging to develop ML models that are accurate and generalizable. FL allows medical data to be used from multiple sources, which can help to improve the quality and generalizability of ML models. Diﬀerential privacy (DP) is a go-to algorithmic tool to make this process secure and private. In this work, we show that the model performance can be further improved by employing local steps, a popular approach to improving the communication eﬃciency of FL, and tuning the number of communication rounds. Concretely, given the privacy budget, we show an optimal number of local steps and communications rounds. We provide theoretical motivations further corroborated with experimental evaluations on real-world medical imaging tasks.
Application of indexed snowmelt algorithms in a northern wetland regime

Applications of snowmelt models in Canada's north have been limited largely to energy balance models concentrating on micro scale studies or macroscale applications. The latter rely solely on basin-wide, optimized parameters for hydrological simulation and often neglect some of the major physical processes controlling melt production. Although physically realistic models can be implemented at the micrometeorological scale, the data requirements are often too numerous to make these types of models practical to apply at the meso- or macroscales. On the other hand, the standard lumped model approach often oversimplifies the physical processes and fails to reveal subtle differences between land cover types and their specific response to meteorological inputs. This paper focuses on the use of indexed snowmelt algorithms derived for individual land cover component characteristics of the wetland dominated region of the lower Liard River Valley. NWT, Canada. These algorithms use an hourly temperature index and a combination radiation-temperature index approach to estimate snowmelt within the different land cover types. The algorithms developed are incorporated into a fully distributed hydrological model (SPL7) that uses the grouped response unit (GRU) method for basin discretization. Snowmelt indices are estimated for both approaches using snow cover depletion data obtained during an extensive field campaign. The indices are then validated using historical data from complementary studies. Results show that the radiation-temperature algorithm provided slightly improved calibration results: however, both algorithms validated equally well.
Tight Bounds for ℓ1 Oblivious Subspace Embeddings

An \ell _p oblivious subspace embedding is a distribution over r \times n matrices \Pi such that for any fixed n \times d matrix A, \[ \Pr _{\Pi }[\textrm {for all }x, \ \Vert Ax\Vert _p \le \Vert \Pi Ax\Vert _p \le \kappa \Vert Ax\Vert _p] \ge 9/10, \] where r is the dimension of the embedding, \kappa is the distortion of the embedding, and for an n-dimensional vector y, \Vert y\Vert _p = (\sum _{i=1}^n |y_i|^p)^{1/p} is the \ell _p -norm. Another important property is the sparsity of \Pi , that is, the maximum number of non-zero entries per column, as this determines the running time of computing \Pi A . While for p = 2 there are nearly optimal tradeoffs in terms of the dimension, distortion, and sparsity, for the important case of 1 \le p \lt 2 , much less was known. In this article, we obtain nearly optimal tradeoffs for \ell _1 oblivious subspace embeddings, as well as new tradeoffs for 1 \lt p \lt 2 . Our main results are as follows: (1) We show for every 1 \le p \lt 2 , any oblivious subspace embedding with dimension r has distortion \[ \kappa = \Omega \left(\frac{1}{\left(\frac{1}{d}\right)^{1 / p} \log ^{2 / p}r + \left(\frac{r}{n}\right)^{1 / p - 1 / 2}}\right). \] When r = {\operatorname{poly}}(d) \ll n in applications, this gives a \kappa = \Omega (d^{1/p}\log ^{-2/p} d) lower bound, and shows the oblivious subspace embedding of Sohler and Woodruff (STOC, 2011) for p = 1 is optimal up to {\operatorname{poly}}(\log (d)) factors. (2) We give sparse oblivious subspace embeddings for every 1 \le p \lt 2 . Importantly, for p = 1 , we achieve r = O(d \log d) , \kappa = O(d \log d) and s = O(\log d) non-zero entries per column. The best previous construction with s \le {\operatorname{poly}}(\log d) is due to Woodruff and Zhang (COLT, 2013), giving \kappa = \Omega (d^2 {\operatorname{poly}}(\log d)) or \kappa = \Omega (d^{3/2} \sqrt {\log n} \cdot {\operatorname{poly}}(\log d)) and r \ge d \cdot {\operatorname{poly}}(\log d) ; in contrast our r = O(d \log d) and \kappa = O(d \log d) are optimal up to {\operatorname{poly}}(\log (d)) factors even for dense matrices. We also give (1) \ell _p oblivious subspace embeddings with an expected 1+\varepsilon number of non-zero entries per column for arbitrarily small \varepsilon \gt 0 , and (2) the first oblivious subspace embeddings for 1 \le p \lt 2 with O(1) -distortion and dimension independent of n. Oblivious subspace embeddings are crucial for distributed and streaming environments, as well as entrywise \ell _p low-rank approximation. Our results give improved algorithms for these applications.
Effective Exchange rate Volatility and MENA countries’ exports to EU

This paper investigates the impact of nominal and real effective exchange rate volatility on exports of six Middle Eastern and North Africa (MENA) countries to 15 member countries of the European Union (EU), for the period 1970Q1-2002Q4. Moving average standard deviation and conditional standard deviation at ARCH model are used to generate four different measures of volatility for each country. The cointegration results indicate a significant relationship, negative for four countries (Algeria, Egypt, Tunisia, and Turkey), positive for the last two (Israel and Morocco), between MENA exports and exchange rate volatility. The short run dynamics, using an error correction model, shows that the Granger – causality effects of the volatility on real exports are significant, whereas the effects of real exchange rate and the gross domestic product of EU are more contrasted. Indications on appropriate exchange rate regime are derived from these results.
Symmetric Rank-One Updates from Partial Spectrum with an Application to Out-of-Sample Extension

Rank-one update of the spectrum of a matrix is a fundamental problem in classical perturbation theory. In this paper, we consider its variant where only part of the spectrum is known. We address this variant using an efficient scheme for updating the known eigenpairs with guaranteed error bounds. Then, we apply our scheme to the extension of the top eigenvectors of the graph Laplacian to a new data sample. In particular, we model this extension as a perturbation problem and show how to solve it using our rank-one updating scheme. We provide a theoretical analysis of this extension method, and back it up with numerical results that illustrate its advantages.
A Trio of Database User Interfaces for Handling Vague Retrieval Requests.

We discuss three user interfaces for situations that involve vague retrieval requests: (1) BAROQUE, a browser for relational databases; (2) VAGUE, a query interpreter for relational databases that can handle neighborhood queries (formal queries with similar-to comparators); and (3) FLEX, an adaptive interface to relational databases that can service satisfactorily users with different levels of expertise (users who submit queries of different levels of correctness and well-formedness).
On turbulence and normal modes in a basin

The problem of forced, geostrophic turbulence in a basin is revisited. The primary focus is the time dependent field, which is shown to be approximately isotropic (in contrast to the strongly zonally anisotropic fields seen in periodic domains). It is also approximately homogeneous, away from the boundaries. Phenomenological arguments suggest the isotropy occurs because the inverse cascade of energy is arrested by basin normal modes rather than by free Rossby waves. Peaks in the velocity spectra at modal frequencies are consistent with basin modes, as has been noted previously. We discuss which modes would be excited and whether dissipation or the mean flow would be expected to alter the modes and their frequencies. A relatively novel feature is the use of Eulerian velocity statistics to quantify the wave and turbulence characteristics. These measures are more suitable to this environment than measures like wavenumber spectra, given the inhomogeneities associated with the boundaries. With regards to the mean, we observe a linear - relation in the region of the mean gyres (at the northern and southern boundaries), consistent with previous theories. This is of interest because our numerica. advection scheme has implicit rather than explicit small scale dissipation, and requires no boundary conditions on the vorticity. The gyre structure is however somewhat different than in an (inviscid) Fofonoff-type solution, suggesting dissipation cannot be neglected.
Software Entrepreneurship: Knowledge Networks and Performance of Software Ventures in China and Russia

This study examines the impact of entrepreneurs? network structure and knowledge homogeneity/heterogeneity of their network members on product development, and revenue growth of software ventures in China and Russia. The empirical data are composed of structured interviews with 159 software entrepreneurs in Beijing and Moscow. The study found that structural holes and knowledge heterogeneity affect positively product diversity in interactive ways. The study also found that knowledge homogeneity accelerates product development. Product development speed enhances revenue growth in the long term. However, the combination of speed with dense and homogeneous networks harms revenue growth over time. The effects of structural holes and knowledge heterogeneity on product diversity and revenue growth over time are more salient in Russia due to the unique institutional, social, and cultural conditions present in the country.
International Business and Development Economics: A Winning Combination; Generating a World's View Based on Buckley and Casson and Hirschman's Books

Much of the discussion in economics is concerned with growth. Economic growth can be discussed and measured in terms of a national state. It can be also discussed and measured in terms of a corporation, (often using the term value rather than growth). Development Economics is concerned with growth of countries run by governments; International Business is concerned with the behavior and the value of multinational enterprises run by management. This paper is about the interface between the two. The vehicle used in this paper to explore the interface is a comparative analysis between two very influential books; ? The Strategy of Development? by Hirschman, (1958), and the ? Future of the Multinational Enterprise? by Buckley and Casson, (1976). The main argument of the paper is that Development Economics and International Business do approach a very similar issue, but they do it from two different dimensions perpendicular to each other. Looking at the whole picture, (the matrix as a whole rather than along the two separate vectors), gives the observer a more meaningful picture. This is done in the paper through a critical comparison of the two texts focusing on the two dimensions on internalization, growth and internalization, investment choices and strategies, and multinational enterprises and the dynamics of development.
Boundary Distribution Estimation to Precise Object Detection

In principal modern detectors, the task of object localization is implemented by the box subnet which concentrates on bounding box regression. The box subnet customarily predicts the position of the object by regressing box center position and scaling factors. Although this approach is frequently adopted, we observe that the result of localization remains defective, which makes the performance of the detector unsatisfactory. In this paper, we prove the ﬂaws in the previous method through theoretical analysis and experimental veriﬁcation and propose a novel solution to detect objects precisely. Rather than plainly focusing on center and size, our approach reﬁnes the edges of the bounding box on previous localization results by estimating the distribution at the boundary of the object. Experimental results have shown the potentiality and generalization of our proposed method.
SRL4ORL: Improving Opinion Role Labeling Using Multi-Task Learning with Semantic Role Labeling

For over a decade, machine learning has been used to extract opinion-holder-target structures from text to answer the question “Who expressed what kind of sentiment towards what? ”. Recent neural approaches do not outperform the state-of-the-art feature-based models for Opinion Role Labeling (ORL). We suspect this is due to the scarcity of labeled training data and address this issue using different multi-task learning (MTL) techniques with a related task which has substantially more data, i.e. Semantic Role Labeling (SRL). We show that two MTL models improve significantly over the single-task model for labeling of both holders and targets, on the development and the test sets. We found that the vanilla MTL model, which makes predictions using only shared ORL and SRL features, performs the best. With deeper analysis we determine what works and what might be done to make further improvements for ORL.
SRI-Sarnoff AURORA System at TRECVID 2013 Multimedia Event Detection and Recounting

In Multimedia Event Detection 2013 evaluation, SRI Aurora team participated in EK100, EK10, and EK0 tasks with full system evaluation. We submitted 15 runs for both pre-specified events (PS-Events) and ad-hoc events (AH-Events). The majority of them achieved satisfactory results. In particular, thanks to the well-designed concept features, our EK10 system works consistently much better for both PSEvents and AH-Events. By creating the concept language model from the web source, we build our EK0 system to perform event detection without training examples. This system achieved promising results on PS-Events. In MER task, we developed an approach to provide a breakdown of the evidences of why the MED decision has been made by exploring the SVM-based event detector. Furthermore, we designed evidence specific verification and detection to reduce uncertainty and improve key evidence discovery.
Patterns and processes of global shrub expansion

Shrub expansion is a global phenomenon that is occurring on savannas, rangelands, and grasslands. In addition, this is an increasingly documented occurrence in the Arctic. Numerous recent studies have strived to pinpoint the drivers of this phenomenon, quantify the changes, and understand their implications for regional and global land use, disturbance regimes, and nutrient cycling. Inquiry into these topics has been facilitated by recent technological developments in satellite remote sensing, aerial photograph analysis, and computer simulation modeling. We provide a new review that accounts for more recent studies in these regions, Arctic shrub expansion, and technological and analytical developments. This four-part discussion focuses on observed patterns of shrub expansion in three rangeland types (desert grasslands, mesic grasslands, savannas) and the Arctic tundra, the primary causes of this expansion, critical comparisons and contrasts between these land types, and recommendations for future avenues of research. These new avenues can inform the development of future land management policies, as well as ongoing investigations to understand and mitigate the effects of climate change.
Alaska’s Freshwater Resources: Issues Affecting Local and International Interests 1

Alessa, Lilian, Mark Altaweel, Andrew Kliskey, Christopher Bone, William Schnabel, and Kalb Stevenson, 2011. Alaska’s Freshwater Resources: Issues Affecting Local and International Interests. Journal of the American Water Resources Association (JAWRA) 47(1):143‐157. DOI: 10.1111/j.1752‐1688.2010. 00498.x
SignGuard: Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering

—Gradient-based training in federated learning is known to be vulnerable to faulty/malicious worker nodes, which are often modeled as Byzantine clients. To this end, previous work either makes use of auxiliary data at parameter server to verify the received gradients (e.g., by computing validation error rate) or leverages statistic-based methods (e.g. median and Krum) to identify and remove malicious gradients from Byzantine clients. In this paper, we acknowledge that auxiliary data may not always be available in practice and focus on the statistic-based approach. However, recent work on model poisoning attacks have shown that well-crafted attacks can circumvent most of existing median-and distance-based statistical defense methods, making malicious gradients indistinguishable from honest ones. To tackle this challenge, we show that the element-wise sign of gradient vector can provide valuable insight in detecting model poisoning attacks. Based on our theoretical analysis of state-of-the-art attack, we propose a novel approach, SignGuard , to enable Byzantine-robust federated learning through collaborative malicious gradient ﬁltering. More precisely, the received gradients are ﬁrst processed to generate relevant magnitude, sign, and similarity statistics, which are then collaboratively utilized by multiple, parallel ﬁlters to eliminate malicious gradients before ﬁnal aggregation. We further provide theoretical analysis of SignGuard by quantifying its convergence with appropriate choice of learning rate and under non-IID training data. Finally, extensive experiments of image and text classiﬁcation tasks - including MNIST, Fashion-MNIST, CIFAR-10, and AG-News - are conducted together with recently proposed attacks and defense strategies. The numerical results demonstrate the effectiveness and superiority of our proposed approach. For example, our SignGuard can detect almost all malicious gradients from Little is Enough attack and lead to negligible accuracy drop.
An approach to large-scale collection of application usage data over the internet

Empirical evaluation of software systems in actual usage situations is critical in software engineering. Prototyping, beta testing, and usability testing are widely used to refine system requirements, detect anomalous or unexpected system and user behavior, and to evaluate software usefulness and usability. The World Wide Web enables cheap, rapid, and large-scale distribution of software for evaluation purposes. However, current techniques for collecting usage data have not kept pace with the opportunities presented by Web-based deployment. This paper presents an approach and prototype system that makes large-scale collection of usage data over the Internet a practical possibility. A general framework for comparing software monitoring systems is presented and used to compare the proposed approach to existing techniques.
Meeting energy needs

The Arabs performed a great service for the American people by demonstrating that there are limits to the availability of oil. But now with the end of the embargo, the advent of spring, and more gasoline, many people are resuming their wasteful habits. Some recent opinion polls that have been published in Science [Murray et al., 1974] show that the public does not understand the seriousness of our energy problems and that it believes they will be solved in a few years. The fact is that with our present rate of motion we could find ourselves enduring uncertainties of supply, inflation, and economic dislocations for a decade or more.