Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting the number of clusters in a network

Many clustering algorithms for complex networks depend on the choice for the number of clusters and it is often unclear how to make this choice. The number of eigenvalues located outside a circle in the spectrum of the non-backtracking matrix was conjectured to be an estimator of the number of clusters in a graph. We compare the estimate of the number of clusters obtained from the spectrum of the non-backtracking matrix with three estimators based on the concept of modularity and evaluate the methods on several benchmark graphs. We find that the non-backtracking method detects the number of clusters better than the modularity-based methods for the graphs in our simulation study, especially when the clusters have slightly different sizes. The estimates of the non-backtracking method are narrowly distributed around the true number of clusters for all benchmark graphs considered. Additionally, for graphs without a clustering structure, the non-backtracking method detects exactly one cluster, which is a convenient property of an estimator of the number of clusters. However, the lack of a well-defined concept of a cluster prevents sharp conclusions.
Some Notes on the Internal Structure of Complex Reflexives*

It has been observed since Faltz (1977) that reflexive pronouns come in two types, i.e. either pronominal (e.g. German sich, Russian sebja) or compound (e.g. English himself, Dutch zichzelf), which Reinhart and Reuland (1991, 1993) called SE (= simplex expression) and SELF anaphors, respectively. Pica (1987) as well as Faltz (1977) observed that this distinction correlates with the syntactic domain in which reflexive pronouns are bound: complex anaphors are bound in a local domain, whereas simplex ones are bound in a non-local domain. This correlation is so striking that a number of researchers have tried to explore its coverage in cross-linguistic investigations. Much of the work has focused on the domain issue, however, and not much attention has been paid to the internal syntax of reflexive pronouns per se. What has been observed in the literature is that complex reflexive pronouns predominantly have the form SE+SELF, with the two morphemes in this order, although there are certain exceptions. To the best of my knowledge, this point has escaped many of the linguists’ notice, and the question arises as to why this is so and not the other way around. One might argue that this is due to the general property of word structure, i.e. that a head comes at the right-hand edge in the sense of Williams (1981). This conjecture is justified only if the complex reflexive form is a word at all the relevant levels. The purpose of this paper is to make a preliminary investigation of this issue and to show that the observed morpheme order arises as a result of the DP syntax in each language. The paper is organized in the following manner. In Section 2, I will outline the basic issue to be addressed in this paper. A proposal will be presented in Section 3, followed by a discussion of some exceptions in Section 4. I will turn to some consequences in Section 5. The paper concludes in Section 6.
Mastery Behavior and Brain Injury in Infancy

This study aims to determine the combined effect of degree of brain injury and age on mastery behavior among infants. Specifically, it investigates whether degree of brain injury in infancy can predict later competence, or mastery motivation behavior at both 7 and 10 months of age. In this context, mastery motivation is defined as “persistence” or the percent of time spent engaging in persistent behavior. To test the hypothesis that there would be a significant interaction between age and brain injury on mastery scores, participants engaged in 12-15-minute toy play sessions at 7and 10-months-old. Data was analyzed using a two-way mixed ANOVA. Although a statistically significant interaction between age and brain injury was not found, the results showed a small main effect in the direction hypothesized: more brain injury was associated with lower mastery motivation scores. Also found in the direction hypothesized was that on average, mastery motivation scores were higher at age 10 months than at age 7 months among infant participants. Lastly, there was a significant difference in mastery motivation scores found among the severe brain injury group, where scores were statistically significantly higher at 10 months old than 7 months old. These results suggest that brain injury may remain relatively stable throughout infancy, unless the brain injury is severe. In the case of severe brain injury, mastery behavior appears to show a natural incline, as there were no interventions used in this study. On this understanding, varying degrees of brain injury should be considered when investigating brain injury in infancy and its effects on mastery behavior.
Influence of biofilms growth on corrosion potential of metals immersed in seawater

The changes of corrosion potential (Ecorr) of metals immersed in seawater were investigated with electrochemical technology and epifluoresence microscopy. In natural seawater, changes of Ecorr were determined by the surface corrosion state of the metal. Ecorr of passive metals exposed to natural seawater shifted to noble direction for about 150 mV in one day and it didn't change in sterile seawater. The in‐situ observation showed that biofilms settled on the surfaces of passive metals when Ecorr moved in noble direction. The bacteria number increased on the metal surface according to exponential law and it was in the same way with the ennoblement of Ecorr. The attachment of bacteria during the initial period played an important role in the ennoblement of Ecorr and it is believed that the carbohydrate and protein in the biofilm are reasons for this phenomenon. The double layer capacitance (Cdl) of passive metals decreased with time when immersed in natural seawater, while remained almost unchanged in sterile seawater. The increased thickness and reduced dielectric constant of Cdl may be reasons.
Cu superstructures fabricated using tree leaves and Cu–MnO2 superstructures for high performance supercapacitors

Copper (Cu) superstructures have been originally synthesized by using natural sapless leaves. A novel hybrid nanoarchitecture, Cu–MnO2 composite, is prepared through facile coating of a thin MnO2 nanofilm onto the highly electronically conductive Cu superstructures. Benefiting from their unique structures, as electrode materials of supercapacitors the composite superstructures have a high specific capacity of 1024 F g−1 at 1.5 A g−1 and good capacity retention of around 96% after 2000 electrochemical cycles.
New approach to stress analysis based on grazing-incidence X-ray diffraction

A new development in the determination of residual stresses in thin surface layers and coatings is presented. The procedure, based on the grazing-incidence X-ray diffraction geometry (referred to here as the `g-sin2 ψ' geometry), enables non-destructive measurement at a chosen depth below the sample surface. The penetration depth of the X-ray radiation is well defined and does not change during the experiment. The method is particularly useful for the analysis of non-uniform stresses in near-surface layers. The g-sin2 ψ geometry was applied for measurements of the residual stresses in TiN coatings. Anisotropic diffraction elastic constants of textured material were used to determine the stress value from the measured lattice strains. A new method of data treatment enables reference-free measurements of residual stresses.
Fabrication and structural characterization of large-scale uniform SnO2 nanotube arrays by sol-gel method

Semiconductor SnO2 nanotube arrays were fabricated by sol-gel method based on highly ordered nanoporous anodic alumina membrane. Their microstructures were characterized by scanning electron microscopy, transmission electron microscopy, selective electron diffraction spectroscopy and X-ray diffraction. Results indicated that SnO2 nanotubes have a thickness of about 20–30 nm, and a diameter of about 100–200 nm. The length of the nanotubes is about 1 μm, XRD and SEDS indicated that these SnO2 nanotubes are polycrystalline.
Sélection de caractéristiques par l’algorithme AdaBoost régularisé : Application à l’extraction de l’information textuelle dans les images naturelles

We address the feature and weak classiﬁer selection based on complexity in boosting process. Recent works (vehicle and pedestrian detection) suggest to use complex and heterogeneous features and weak classiﬁers for accurate detection. Detection speed is another important consideration for real time applications. With these aspects in mind, we propose a regularized version of AdaBoost algorithm (AdaBoost.RG) which integrates complexity. The approach is employed to detect text in natural scene images, a very complex problem in the domain of camera based document analysis and recognition.
Working Paper

There is a consensus among the majority of economists that the credit supply is limited by current household saving. If governments or foreigners ran deficits, they would absorb this limited saving so that firms could not borrow any longer and had to reduce their investment. This is the “Loanable Funds” theory. Ben Bernanke’s “Global Saving Glut” thesis is based on this view. According to Bernanke, the US depended on South East Asian and commodity exporting countries’ scarce saving to finance the US real estate boom. Using simple accounting rules, the article shows however that credit is never limited by current saving. Often, the exact opposite is true: people can only save after others have taken on credit and paid incomes. This is also the case with the US and its trade partners and creditors: since non-Americans accept the US-Dollar as a means of payment – which only the US can produce – Americans give credit to themselves to finance their current account deficits. Each Dollar that nonAmericans invest in the US has either been earned or borrowed in the US before. By their deficits, the US does not absorb scarce saving but allows other countries to increase their income and saving. 1 Macroeconomic Policy Institute (IMK) in the Hans-Böckler Foundation, fabian-lindner@boeckler.de. This is a revised version of an article originally published in German (Lindner, 2014). I thank Michael Watt for the translation and Ben Schumann for helpful comments. 155 July 2015
Representing Non-Aﬀine Parallel Algorithms by means of Recursive Polyhedral Equations

Polyhedral equations allow parallel program to be expressed, analyzed, and compiled automatically, but they cannot express divide-and-conquer approaches. This limitation is basically due to the affine nature of the dependence functions imposed by the model. In this paper, we describe how this limitation can be overcome by extending a structured polyhedral equational language to recursive calls of polyhedral programs. Doing so, we preserve the affine property inside a given call, whereas the non-affine part is carried by the recursive expression of subsystem calls. We describe the basic mechanisms of this extension, show that the fundamental results of polyhedral equations hold, in particular, the schedule of such a system can be found automatically. We illustrate this approach on several well known algorithms, including the FFT.
Three Level Converter and Its Control Strategy

Diode Neutral Point Clamped (D-NPC) three-level topology with simple structure and low withstand voltage of single power device is suitable for medium voltage applications. Due to the long working time and high temperature of the inner tube in the main bridge arm of the d-npc structure, the design of the radiator is difficult, and the output current and switching frequency are limited. In order to solve this problem, this paper proposes an improved d-npc three-phase three-level topology, optimizes the traditional seven segment SVPWM modulation strategy, builds a simulation model based on PMSM control device, and proves the correctness and feasibility of the improved structure.
Olfactory-Trigeminal Integration in the Primary Olfactory Cortex

Humans naturally integrate signals from the olfactory and intranasal trigeminal systems. A tight interplay has been demonstrated between these two systems, and yet the underlying neural circuitry that mediates olfactory-trigeminal integration remains poorly understood. Using functional magnetic resonance imaging (fMRI), combined with psychophysics, this study investigated the neural mechanisms underlying olfactory-trigeminal integration. Fifteen participants with normal olfactory function performed a localization task with air-puff stimuli, phenylethyl alcohol (PEA; rose odor), or a combination thereof while being scanned. The ability to localize PEA to either nostril was at chance. Yet, its presence significantly improved the localization accuracy of weak, but not strong, air-puffs, relative to air-puff localization without concomitant PEA when both stimuli were delivered concurrently to the same nostril, but not when different nostrils received the two stimuli. This enhancement in localization accuracy, exemplifying the principles of spatial coincidence and inverse effectiveness in multisensory integration, was associated with multisensory integrative activity in the primary olfactory (POC), orbitofrontal (OFC), superior temporal (STC), inferior parietal (IPC) and cingulate cortices, and in the cerebellum. Multisensory enhancement in most of these regions, except the OFC, correlated with behavioral multisensory enhancement, as did increases in connectivity between some of these regions. We interpret these findings as indicating that the POC is part of a distributed brain network mediating integration between the olfactory and trigeminal systems. HIGHLIGHTS Psychophysical and neuroimaging study of olfactory-mechanosensory (OM) integration Behavior, cortical activity and network connectivity show OM integration OM integration obeys principles of inverse effectiveness and spatial coincidence Behavioral and neural measures of OM integration are correlated
Fine structure in field emission resonances at surfaces

We have measured field‐emission resonances (FER) to much higher quantum states than previously observed enabling fine structure in the resonance amplitudes to be investigated. We show that this electron wave interference effect carries additional information in the resonance amplitudes and energies and is sensitive to lateral tip position in a scanning tunnelling microscope experiment. We propose a new method of data analysis that can distinguish between some of the possible origins of the fine structure.
Predicting Corruption Crimes with Machine Learning. A Study for the Italian Municipalities

Using police archives, we apply machine learning algorithms to predict corruption crimes in Italian municipalities during the period 2012-2014. We correctly identify over 70% (slightly less than 80%) of the municipalities that will experience corruption episodes (an increase in corruption crimes). We show that algorithmic predictions could strengthen the ability of the 2012 Italy’s anti -corruption law to fight white-collar delinquencies.
Direct and Indirect Communication in Multi-Human Multi-Robot Interaction

How can multiple humans interact with multiple robots? The goal of our research is to create an effective interface that allows multiple operators to collaboratively control teams of robots in complex tasks. In this paper, we focus on a key aspect that affects our exploration of the design space of human-robot interfaces -- inter-human communication. More specifically, we study the impact of direct and indirect communication on several metrics, such as awareness, workload, trust, and interface usability. In our experiments, the participants can engage directly through verbal communication, or indirectly by representing their actions and intentions through our interface. We report the results of a user study based on a collective transport task involving 18 human subjects and 9 robots. Our study suggests that combining both direct and indirect communication is the best approach for effective multi-human / multi-robot interaction.
Deep High-Resolution Network for Low Dose X-ray CT Denoising

Low Dose Computed Tomography (LDCT) is clinically desirable due to the reduced radiation to patients. However, the quality of LDCT images is often sub-optimal because of the inevitable strong quantum noise. Inspired by their unprecedent success in computer vision, deep learning (DL)-based techniques have been used for LDCT denoising. Despite the promising noise removal ability of DL models, people have observed that the resolution of the DL-denoised images is compromised, decreasing their clinical value. Aiming at relieving this problem, in this work, we developed a more effective denoiser by introducing a high-resolution network (HRNet). Since HRNet consists of multiple branches of subnetworks to extract multiscale features which are later fused together, the quality of the generated features can be substantially enhanced, leading to improved denoising performance. Experimental results demonstrated that the introduced HRNet-based denoiser outperforms the benchmarked UNet-based denoiser in terms of superior image resolution preservation ability while comparable, if not better, noise suppression ability. Quantitative metrics in terms of root-mean-squared-errors (RMSE)/structure similarity index (SSIM) showed that the HRNet-based denoiser can improve the values from 113.80/0.550 (LDCT) to 55.24/0.745 (HRNet), in comparison to 59.87/0.712 for the UNet-based denoiser.
The effect of differential victim crime reporting on predictive policing systems

Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are commonly known as hot spot predictive policing. These systems predict where future crime is likely to concentrate such that police can allocate patrols to these areas and deter crime before it occurs. Previous research on fairness in predictive policing has concentrated on the feedback loops which occur when models are trained on discovered crime data, but has limited implications for models trained on victim crime reporting data. We demonstrate how differential victim crime reporting rates across geographical areas can lead to outcome disparities in common crime hot spot prediction models. Our analysis is based on a simulation1 patterned after district-level victimization and crime reporting survey data for Bogotá, Colombia. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas. This may lead to misallocations both in the form of over-policing and under-policing.
Circumbinary Disk Accretion into Spinning Black Hole Binaries

Supermassive black hole binaries are likely to accrete interstellar gas through a circumbinary disk. Shortly before merger, the inner portions of this circumbinary disk are subject to general relativistic effects. To study this regime, we approximate the spacetime metric of close orbiting black holes by superimposing two boosted Kerr–Schild terms. After demonstrating the quality of this approximation, we carry out very long-term general relativistic magnetohydrodynamic simulations of the circumbinary disk. We consider black holes with spin dimensionless parameters of magnitude 0.9, in one simulation parallel to the orbital angular momentum of the binary, but in another anti-parallel. These are contrasted with spinless simulations. We find that, for a fixed surface mass density in the inner circumbinary disk, aligned spins of this magnitude approximately reduce the mass accretion rate by 14% and counter-aligned spins increase it by 45%, leaving many other disk properties unchanged.
Virtual Microgrid Management via Software-Defined Energy Network for Electricity Sharing: Benefits and Challenges

Digitalization has led to radical changes in the distribution of goods across various sectors. The tendency is to move from traditional buyer-seller markets to subscriptionbased, on-demand "smart" matching platforms enabled by pervasive information and communications technologies (ICTs). The driving force behind this lies in the fact that assets, which were scarce in the past, are readily abundant, approaching a regime of zero marginal costs. This is also becoming a reality in electrified energy systems because of the substantial growth of distributed renewable energy sources, such as solar and wind; the increasing number of small-scale storage units, such as batteries and heat pumps; and the availability of flexible loads that enable demand-side management (DSM).
TEE-based protection of cryptographic keys on embedded IoT devices

The Internet of Things (IoT) consists in billions of embedded devices connected to the Internet. Secure remote management of many of these devices require them to store and use long-term cryptographic keys. In this work, we propose to protect cryptographic keys in embedded IoT devices using a Trusted Execution Environment (TEE), which is supported on many embedded platforms. Our approach provides similar protection as secure co-processors, but does not actually require an additional secure hardware element.
Densely Connected Recurrent Residual (Dense R2UNet) Convolutional Neural Network for Segmentation of Lung CT Images

Deep Learning networks have established themselves as providing state of art performance for semantic segmentation. These techniques are widely applied specifically to medical detection, segmentation and classification. The advent of the U-Net based architecture has become particularly popular for this application. In this paper we present the Dense Recurrent Residual Convolutional Neural Network(Dense R2U CNN) which is a synthesis of Recurrent CNN, Residual Network and Dense Convolutional Network based on the U-Net model architecture. The residual unit helps training deeper network, while the dense recurrent layers enhances feature propagation needed for segmentation. The proposed model tested on the benchmark Lung Lesion dataset showed better performance on segmentation tasks than its equivalent models.
Theory of the local tunneling spectrum of a vibrating adsorbate

We present a model describing the essentials of tunneling between the tip of a scanning tunneling microscope (STM) and an electronic resonance on an isolated adsorbed molecule. Conditions for observing enhanced inelastic tunneling and its experimental manifestations are identified in terms of the energy and width of the resonance and of its coupling to a particular molecular vibration. For a broad resonance, this occurs if it is partially filled; we then predict a conductance decrease as large as 10% at the inelastic threshold for typical chemisorption systems. For a resonance weakly hybridized with the underlying electrode, strong multiphonon excitation is possible, although as a rule it will not be readily apparent. Relevant systems of interest for local spectroscopy with future STM’s are discussed.