Miracle-dz/UniFAR-FTUs huggingface dataset:
Tell Your Friends! Word of Mouth and Percolation in Social Networks

This paper studies the optimal strategies of a monopolist selling a good to consumers who engage in word of mouth communication. In the model consumers may spread news about the monopolists good to uninformed consumers through a social network. The monopolist may use the price it charges to inuence both the proportion of the population that is willing to purchase the good and the pattern of communication that takes place within the social network. I nd a number of results: (i) demand is more elastic in the presence of word of mouth and this induces a downward bias in estimates of consumersvaluation for the good which ignore word of mouth; (ii) the monopolist reduces the price to induce additional word of mouth for regular goods however for goods whose valuation is greater for well connected individuals the price may, in fact, be greater; (iii) the optimal pattern of di¤usion involves introductory prices which uctuate up and down; and (iv) exclusive (high priced) products will optimally target advertising towards individuals with many friends whereas common (low priced) products will target individuals with fewer friends. Massachusetts Institute of Technology, Department of Economics, E52, 50 Memorial Drive, Cambridge, MA, 02142. email: ards@mit.edu. I am particularly indebted to Glenn Ellison. I would also like to thank Muhamet Yildiz, Florian Ederer, Johannes Spinnewijn, Moshe Cohen, Jesse Edgerton, Daniel Gottlieb, Nicolas Arregui, Alex Wolinsky, Alp Simsek and participants at the MIT Theory and Industrial Organization lunches.
INFLATION AND OUTPUT GAP RECONSIDERED: ASSYMETRIES AND NONLINEAR PHILLIPS CURVE EVIDENCE FOR THE TURKISH ECONOMY

Bu calismada, 1990:1 – 2008:5 donemi aylik verileri kullanilarak, Turkiye ekonomisi acisindan enflasyon farki ile cikti acigi arasindaki iliski arastirilmis ve Markov rejim degisim modeli yardimiyla, soz konusu iliskinin dogrusal olmadigi yonunde bulgulara ulasilmistir. Calismanin temel katkisi, Markov rejim degisim modeli cercevesinde, Chen (2006) tarafindan onerilen gecis olasiliklarinin hesaplanmis olmasidir
IMPROVING FREQUENCY RESPONSE OF MICROSTRIP FILTERS USING DEFECTED GROUND AND DEFECTED MICROSTRIP STRUCTURES

In this work, the introduction of Defected Microstrip Structures and Defected Ground Structures is presented to improve the performance of a traditional stepped-impedance microstrip lowpass fllter. The attenuation in the stop-band is enhanced by more than 15dB and selectivity is increased, without modifying the insertion loss in the pass band. A comparison of characteristics in fllters is made when the combination of Defected Ground and Defected Microstrip Structures, as well as when only the flrst one are used. A model of the Defected Microstrip Structure and the corresponding equations to obtain the equivalent lumped and distributed element values are also given.
Deepauth: in-situ authentication for smartwatches via deeply learned behavioural biometrics

This paper proposes DeepAuth, an in-situ authentication framework that leverages the unique motion patterns when users entering passwords as behavioural biometrics. It uses a deep recurrent neural network to capture the subtle motion signatures during password input, and employs a novel loss function to learn deep feature representations that are robust to noise, unseen passwords, and malicious imposters even with limited training data. DeepAuth is by design optimised for resource constrained platforms, and uses a novel split-RNN architecture to slim inference down to run in real-time on off-the-shelf smartwatches. Extensive experiments with real-world data show that DeepAuth outperforms the state-of-the-art significantly in both authentication performance and cost, offering real-time authentication on a variety of smartwatches.
Study of Photon Propagation with various Energies through Bone by Monte Carlo Simulation

The aim of radiation therapy is to maximise the dose to the tumour, while minimising the dose to healthy tissue. The Monte Carlo (MC) simulation by advantages of high accuracy, low cost, and describing the coupled transport of electrons and photons in complex geometries is used increasingly in radiation therapy. Since 50-80% of the human body is water, many studies have carried out the photons propagation in tissue, although no serious study has performed in bone. The bone, because of its high density, as well high ability of scattering and absorption of photons is important matter in radiation therapy. The aim of present work was the MC simulation of photons propagation with various energies in bone. The MC simulation was written in Fortran 77 program. Emigration of photons in bone was determined using MCNP-4C code to evaluate the results of MC method. The depth of first interaction of photons 1, 5, and 10 MeV in bone were determined in 2.26, 2.90, and 4.40 cm using the MC method, which were a good consistent with those of MCNP results 2, 3, 4 cm, respectively.
Economic Event Detection in Company-Specific News Text

This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups
High-Performance Consensus Mechanisms for Blockchains

The popularity of Bitcoin and other blockchain platforms has increased remarkably over the last few years. However, the mining process based on Proof-of-Work (PoW) needed to reach consensus leads to high energy consumption. This increases participation cost, leads to network centralization due to expensive, specialized hardware, and is problematic for the environment. An alternative to this PoW mining are Byzantine agreement protocols. Current Byzantine fault tolerant (BFT) protocols, however, are not suited for high numbers of participants, and therefore have to be adapted with regard to performance and security, e. g. by utilizing novel networking techniques and trusted computing.
Open Data Integration

Open data plays a major role in supporting both governmental and organizational transparency. Many organizations are adopting Open Data Principles promising to make their open data complete, primary, and timely. These properties make this data tremendously valuable to data scientists. However, scientists generally do not have a priori knowledge about what data is available (its schema or content). Nevertheless, they want to be able to use open data and integrate it with other public or private data they are studying. Traditionally, data integration is done using a framework called query discovery where the main task is to discover a query (or transformation) that translates data from one form into another. The goal is to find the right operators to join, nest, group, link, and twist data into a desired form. We introduce a new paradigm for thinking about integration where the focus is on data discovery, but highly efficient internet-scale discovery that is driven by data analysis needs. We describe a research agenda and recent progress in developing scalable data-analysis or query-aware data discovery algorithms that provide high recall and accuracy over massive data repositories.
Cladosiphon umezakii sp. nov. (Ectocarpales, Phaeophyceae) from Japan

The new species Cladosiphon umezakii Ajisaka (Ectocarpales, Phaeophyceae) is described from Japan based on morphology and DNA sequences. The species resembles Cladosiphon okamuranus Tokida in its gross morphology; somewhat slimy, cylindrical, multiaxial and sympodial erect thallus, arising from a small disc‐shaped holdfast, and branching once to twice. However, C. umezakii has considerably longer assimilatory filaments (up to 840 μm long, composed of up to 90 cells) than any known taxa of the genus. The species is a winter to spring annual, growing on lower intertidal to subtidal rocks of more or less exposed sites on the north‐eastern coast of Kyushu and on both the Pacific and the Sea of Japan coasts of Honshu. Specimens from the Sea of Japan coast had both unilocular and plurilocular zoidangia, whereas those from Kyushu and from the Pacific had only unilocular zoidangia. Unilocular zoidangia were formed on the basal part of assimilatory filaments, and plurilocular ones were transformed from the distal part of assimilatory filaments. DNA sequences of the Rubisco‐spacer (rbc‐spacer) region and the nuclear rDNA ITS region (ITS1, 5.8S and ITS2) supported the distinctness of the species.
Data for the problem of evolution in man. VI.—A first study of the correlation of the human skull

The substance of this paper was a thesis for the London D. Sc. degree; it was shown to Professor Pearson, at whose suggestion considerable modifications were made, and a revision undertaken with a view to publication. In order to deal exactly with the problem of evolution in man it is necessary to obtain in the first place a quantitative appreciation of the size, variation, and correlation of the chief characters in man for a number of local races. Several studies of this kind have been already undertaken at University College. These fall into two classes, (i) those that deal with a variety of characters in one local race, and (ii) those which study the comparative value of the constants from a variety of races.
Novel Statistical Network Methodology to Identify and Analyze Cancer Biomarkers

Abstract The global burden of cancer is expected to increase between 2008 and 2030 from 13 to 22 million new cases each year (Bray et al. 2012). The strain that this will put on health services around the world will be reduced by the development of better methods for early detection of disease risk and progression. The epigenome is thought of as the interface between the genome and the environment; hence measurements of DNA methylation, an epigenetic pattern, can indicate exposure to environmental risk factors. However these measurements are extremely noisy, making it a challenge to derive meaningful statistics from such data. Using canonical correlation analysis we have developed a novel statistical measure of the level of interaction between a pair of genes (network nodes) in a single sample/patient, based on DNA methylation data. Testing this interaction measure for association with patient outcome, we show how to construct prognostic networks for cancer, in which the presence of a network edge indicates that the network interaction between the corresponding pair of genes (nodes) is statistically significantly prognostic. Detecting community structure in these networks by fitting the stochastic blockmodel allows novel cancer biomarkers to be detected. These findings represent new statistical tools for use in the biomedical sciences.
Theory for off-specular reflection from roughened surfaces

The directional distribution of radiant flux reflected from roughened surfaces is analyzed on the basis of geometrical optics. The analytical model assumes that the surface consists of small, randomly disposed, mirror-like facets. Specular reflection from these facets plus a diffuse component due to multiple reflections and/or internal scattering are postulated as the basic mechanisms of the reflection process. The effects of shadowing and masking of facets by adjacent facets are included in the analysis. The angular distributions of reflected flux predicted by the analysis are in very good agreement with experiment for both metallic and nonmetallic surfaces. Moreover, the analysis successfully predicts the off-specular maxima in the reflection distribution which are observed experimentally and which emerge as the incidence angle increases. The model thus affords a rational explanation for the off-specular peak phenomenon in terms of mutual masking and shadowing of mirror-like, specularly reflecting surface facets.
TPM

With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services. The availability of spatial, temporal, and social information in LBSNs offers an unprecedented opportunity to enhance the spatial item recommendation. Many previous works studied spatial and social influences on spatial item recommendation in LBSNs. Due to the strong correlations between a user’s check-in time and the corresponding check-in location, which include the sequential influence and temporal cyclic effect, it is essential for spatial item recommender system to exploit the temporal effect to improve the recommendation accuracy. Leveraging temporal information in spatial item recommendation is, however, very challenging, considering (1) when integrating sequential influences, users’ check-in data in LBSNs has a low sampling rate in both space and time, which renders existing location prediction techniques on GPS trajectories ineffective, and the prediction space is extremely large, with millions of distinct locations as the next prediction target, which impedes the application of classical Markov chain models; (2) there are various temporal cyclic patterns (i.e., daily, weekly, and monthly) in LBSNs, but existing work is limited to one specific pattern; and (3) there is no existing framework that unifies users’ personal interests, temporal cyclic patterns, and the sequential influence of recently visited locations in a principled manner. In light of the above challenges, we propose a Temporal Personalized Model (TPM), which introduces a novel latent variable topic-region to model and fuse sequential influence, cyclic patterns with personal interests in the latent and exponential space. The advantages of modeling the temporal effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity, and a direct expression of the semantic meaning of users’ spatial activities. Moreover, we introduce two methods to model the effect of various cyclic patterns. The first method is a time indexing scheme that encodes the effect of various cyclic patterns into a binary code. However, the indexing scheme faces the data sparsity problem in each time slice. To deal with this data sparsity problem, the second method slices the time according to each cyclic pattern separately and explores these patterns in a joint additive model. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top-k recommendation process by extending the traditional LSH. We evaluate the performance of TPM on two real datasets and one large-scale synthetic dataset. The performance of TPM in recommending cold-start items is also evaluated. The results demonstrate a significant improvement in TPM’s ability to recommend spatial items, in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.
The Hydration Repulsion between Charged Surfaces as an Interplay of Volume Exclusion and Dielectric Saturation Effects

Abstract We demonstrate that the hydration repulsion between smooth charged surfaces can be attributed to the interplay of two effects which are not taken into account in the conventional DLVO theory. These are the finite size of the counterions and the variable dielectric permittivity across the electric double layers. We involve these two effects in the theory by coupling the Poisson–Boltzmann equation with the Bikerman and Booth equations. The resulting nonlinear ordinary differential equation of second order is solved numerically. The theory is applied to interpret available experimental data for the dependence of the surface force on the distance measured by means of the surface force apparatus. Excellent agreement between theory and experiment is obtained with reasonable values of the two adjustable parameters: the area per surface charge and the counterion adsorption energy. It turns out that the contribution of the ionic-excluded volume to the hydration force is several times greater than the contribution due to the dielectric saturation. The developed theoretical approach can find application for interpreting data about the interactions in aqueous thin films, colloidal dispersions, and micellar surfactant solutions in the presence of electrolyte at ionic strengths above 1 mM.
Simultaneous Localization and Mapping with Power Network Electromagnetic Field

Various sensing modalities have been exploited for indoor location sensing, each of which has well understood limitations, however. This paper presents a first systematic study on using the electromagnetic field (EMF) induced by a building's electric power network for simultaneous localization and mapping (SLAM). A basis of this work is a measurement study showing that the power network EMF sensed by either a customized sensor or smartphone's microphone as a side-channel sensor is spatially distinct and temporally stable. Based on this, we design a SLAM approach that can reliably detect loop closures based on EMF sensing results. With the EMF feature map constructed by SLAM, we also design an efficient online localization scheme for resource-constrained mobiles. Evaluation in three indoor spaces shows that the power network EMF is a promising modality for location sensing on mobile devices, which is able to run in real time and achieve sub-meter accuracy.
A scaling approach to capture sub-time-step rainfall variability in rainfall-runoff and erosion modelling

Scaling in space and time is a fundamental problem in hydrological and erosion modelling. Owing mainly to the lack of adequate data to support modelling at the process timescales, modelling of small time scale processes using coarse time scale data is often undertaken. Generally process descriptions are not modified but rather effective parameter values are used. A similar approach is taken spatially. It is generally recognized that this approach to scaling in process-based modelling can be problematic when non-linear interactions occur. This paper presents a method for modelling surface runoff and erosion processes that accounts for sub-timestep variability in rainfall while retaining a daily timestep and utilizing daily rainfall totals. The method uses the cumulative distribution function (CDF) of rainfall intensities to represent the effect of temporal variability of rainfall at a time-scale of minutes. While any CDF can be used, the rainfall intensity distribution model chosen here is the lognormal distribution. The distribution parameters are determined from daily rainfall totals. The rainfall CDF is modified by the interception, infiltration, and saturation excess processes to derive CDFs of throughfall, infiltration and surface runoff. These are then applied to the erosion algorithm to determine the erosion CDF. The resulting CDFs are integrated to predict respective daily loads. While a specific model is used here, it is worth noting that the approach used for rainfall scaling here is general and could be applied to many (but not all) rainfall-runoff and erosion models.
Seasonal Variation Characteristics and Forecasting Model of PM2.5 in Changsha, Central City in China

This paper describe the seasonal variation characteristics of PM2.5, PM2.5/PM10 and established the multivariable linear regression model of PM2.5 based on the observation during the period of January 1, 2014 to December 31, 2014 in central city of China. It is found that the mean concentration of PM2.5 has obvious seasonal variation characteristics, the lowest value in summer and the higher value in winter and autumn. The daily average mass concentration of PM2.5 in January is 161.93 μg/m3 and the over standard rate is 90 percent which is the annual maximum; the annual minimum in august. The ratio of PM2.5 and PM10 is high ratio of autumn and winter, up to 2.9, the spring and summer is relatively stable trend, the ratio between 0.6 to 0.9. The two multivariable linear regression model of PM2.5: [PM2.5]=-39.241+0.394 × [PM10]+44.253 × [CO]+0.4 × [BPM2.5] and [PM2.5]=-43.979+0.462 × [PM10]+70.083 × [CO]. The former is suitable for short-term prediction, high accuracy, the latter is suitable for long-term forecast. Certain probability model can predict the trend of the PM2.5 to explore the pros and cons of air quality. But there are still many deficiencies, need to further improve.
An Empirical Investigation into Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. We mine millions of bug-fixes from the change histories of GitHub repositories to extract meaningful examples of such bug-fixes. Then, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. Our model is able to fix hundreds of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9% of the cases.
Detecting and Resolving Shell Nouns in German

This paper describes the design and evaluation of a system for the automatic detection and resolution of shell nouns in German. Shell nouns are general nouns, such as fact, question, or problem, whose full interpretation relies on a content phrase located elsewhere in a text, which these nouns simultaneously serve to characterize and encapsulate. To accomplish this, the system uses a series of lexico-syntactic patterns in order to extract shell noun candidates and their content in parallel. Each pattern has its own classifier, which makes the final decision as to whether or not a link is to be established and the shell noun resolved. Overall, about 26.2% of the annotated shell noun instances were correctly identified by the system, and of these cases, about 72.5% are assigned the correct content phrase. Though it remains difficult to identify shell noun instances reliably (recall is accordingly low in this regard), this system usually assigns the right content to correctly classified cases. cases.
Development of Pareto-Optimal Transit Routes in Afghanistan through Mathematical Algorithms

Afghanistan is geo-strategically in an important zone in South and Central Asia with six neighbouring countries, but currently lacks of modern infrastructure. We present the construction of optimal transit routes in Afghanistan through mathematical optimization. Basically there are three different optimization goals a) shortest route w.r. t. the distance, b) the cheapest route w.r. t. the construction cost, and c) the most convenient route w.r. t. the elevation change. It is possible to combine two objectives by considering the Pareto front. For the design and modelling of the routes, a computer program named »CONTRA« (Computing an Optimal Network of Transit Routes through mathematical Algorithms) was developed. As a demonstrator example, we compute Pareto-optimal routes between two