Miracle-dz/UniFAR-FTUs huggingface dataset:
SpiNNaker 2: A 10 Million Core Processor System for Brain Simulation and Machine Learning

SpiNNaker is an ARM-based processor platform optimized for the simulation of spiking neural networks. This brief describes the roadmap in going from the current SPINNaker1 system, a 1 Million core machine in 130nm CMOS, to SpiNNaker2, a 10 Million core machine in 22nm FDSOI. Apart from pure scaling, we will take advantage of specific technology features, such as runtime adaptive body biasing, to deliver cutting-edge power consumption. Power management of the cores allows a wide range of workload adaptivity, i.e. processor power scales with the complexity and activity of the spiking network. Additional numerical accelerators will enhance the utility of SpiNNaker2 for simulation of spiking neural networks as well as for executing conventional deep neural networks. These measures should increase the simulation capacity of the machine by a factor $>$50. The interplay between the two domains, i.e. spiking and rate based, will provide an interesting field for algorithm exploration on SpiNNaker2. Apart from the platforms' traditional usage as a neuroscience exploration tool, the extended functionality opens up new application areas such as automotive AI, tactile internet, industry 4.0 and biomedical processing.
TEMPORAL FLUCTUATIONS IN HEAT WAVES AT PRAGUE – KLEMENTINUM , THE CZECH REPUBLIC , FROM 1901 – 97 , AND THEIR RELATIONSHIPS TO ATMOSPHERIC CIRCULATION

Temporal fluctuations in heat wave occurrence and severity are analysed in long-term daily temperature series at Prague–Klementinum, the Czech Republic. Although the observations have been continuous since 1775, the period 1901–97 with the most credible data is mainly examined. Most of the warmest summers of the 20th century appeared within the periods 1943–52 and 1992–95; the temporal distribution of heat waves corresponds to this pattern and shows two maxima, in the 1940s to early 1950s and in the 1990s. A very low occurrence of heat waves was typical of the beginning of the 20th century and around 1980. The peak of heat wave severity in the 1940s–early 1950s, as well as their almost total absence in the first two decades of the 20th century, may be a common feature for a large area, as indicated by the comparison between two stations within central Europe, Prague–Klementinum and Basel (Switzerland), and by other studies. An extraordinary heat wave occurred in July and August 1994. It greatly exceeded other heat waves, mainly in the much higher cumulative temperature excess above 30 °C and a record-breaking duration of a continuous period of tropical days. Relationships between heat wave characteristics in warm and cold decades and circulation conditions were analysed using the subjective Hess–Brezowsky catalogue of weather types (Grosswetterlagen). The link to the atmospheric circulation is evident, e.g. situations with an anticyclone or a ridge over central Europe were more (less) frequent during all the warm (cold) decades. Moreover, the occurrence of long and severe heat waves in the 1990s may reflect an enhanced persistence of the atmospheric circulation over Europe in the summer season because all groups of weather types have considerably increased residence times in 1988–97 compared with long-term means. Copyright  2002 Royal Meteorological Society.
Optimal Design of Experiments on Riemannian Manifolds

Traditional optimal design of experiment theory is developed on Euclidean space. In this paper, new theoretical results of optimal design of experiments on Riemannian manifolds are provided. In particular, it is shown that D-optimal and G-optimal designs are equivalent on manifolds and provide a lower bound for the maximum prediction variance. In addition, a converging algorithm that finds the optimal experimental design on manifold data is proposed. Numerical experiments demonstrate the competitive performance of the new algorithm.
Analysis of growth curves of Guinea fowl (Numida meleagris) fed diets containing dry oregano (Origanum vulgare L.) in an organic system

In this study, 240 day-old guinea fowl ( Numida meleagris ) keets were utilized. They were divided into four treatment groups each containing 20 chicks and were randomly distributed into 12 mobile coops placed in a 100-m2 grazing area. Guinea fowl chicks were randomly allocated to 4 treatment diets containing 0%, 5%, 10%, and 15% dry oregano leaf (DOL) supplements. Nonlinear Gompertz and logistic growth models were used to estimate the mean age-body weight. The growth curve parameters for these models and the following characteristics for fowl were estimated: β0, the asymptotic weight parameter; β1, the scaling parameter; β2, the instantaneous per week growth rate; weight at age of inflection point (WIP); maximum weight gain at inflection point (MWG); and age at the inflection point (AIP). The goodness of fit (GF) for the models was assessed using the following variables: coefficients of determination (r2), mean square error (MSE), adjusted determination coefficient (ADR2), Akaike’s information criteria (AIC), chi-square test (Chi.Sq2) and residual standard deviation (RSD). The different nonlinear function results of the individual data indicated that supplementation of diets with DOL had no significant effects on growth curve parameters when compared with the control diet. Greater correlation values were estimated among β0, β1, β2, WIP, MWG and AIP in the Gompertz equation, and similar results were estimated in the logistic equation, but there was no significant correlation between β2-β1 and β2-MWG. According to the results obtained from the GF, high r2 and ADR2 were estimated in Gompertz and logistic equations (above 0.96).
Box 1 Common causes of hypernatremia and hyponatremia in neurologically critically ill patients Traumatic brain injury Infections Meningitis Encephalitis Neurovascular Thrombotic or embolic stroke Hemorrhagic stroke Neoplasm Pituitary adenoma Meningioma Germinoma

Dysnatremia is a common finding in the intensive care unit (ICU) and has been suggested to be a predictor for mortality and poor clinical outcomes. Depending on the time of onset (ie, on admission vs later in the ICU stay), the incidence of dysnatremias in critically ill patients ranges from 6.9% to 15%, respectively. The symptoms of sodium derangement and their effect on brain physiology make early recognition and correction paramount in the neurologic ICU (NICU). Hyponatremia in brain injured patients can lead to life-threatening conditions such as seizures and may worsen cerebral edema and contribute to alterations in intracranial pressure.
Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching

This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing.
Exploring effects on magnifications due to line-of-sight galaxies in the Hubble Frontier Fields

Cluster lensing has become an important tool in the search for high-redshift galaxies through its ability to magnify sources. In order to determine the intrinsic properties of these galaxies, lensing mass models must be constructed to determine the magnification of the images. These models are traditionally 2D, focusing on the mass within the cluster and either ignoring or approximating any contribution from line-of-sight galaxies. In this paper, we present the first full set of 3D mass models of the six Hubble Frontier Fields and use them to test for systematic biases in magnifications due to using the traditional 2D approach. We find that omitting foreground or background galaxies causes image position offsets between 0.1 and 0.4 arcsec, a non-negligible fraction of the typical 0.3–0.7 arcsec residuals of current state-of-the-art models. We also find that median image magnifications can shift by up to 6 per cent, though it is dependent on the field. This can be alleviated in some cases by approximating the mass in the lensing plane, but a 5 per cent magnification bias still exists in other cases; image position offsets are also improved, but are still present at 0.10 arcsec.
Relaxed micromorphic broadband scattering for finite-size meta-structures -- a detailed development

The conception of new metamaterials showing unorthodox behaviors with respect to elastic wave propagation has become possible in recent years thanks to powerful modeling tools, allowing the description of an inﬁnite medium generated by periodically architectured base materials. Nevertheless, when it comes to the study of the scattering properties of ﬁnite-sized structures, dealing with the correct boundary conditions at the macroscopic scale becomes challenging. In this paper, we show how ﬁnite-domain boundary value problems can be set-up in the framework of enriched continuum mechanics (relaxed micromorphic model) by imposing continuity of macroscopic displacement and of generalized tractions, as well as additional conditions on the microdistortion tensor and on the double-traction.Thecaseofa metamaterial slab of ﬁnite width is presented, its scattering properties are studied via a semi-analytical solution of the relaxed micromorphic model and compared to ﬁnite element simulations encoding all details of the selected microstructure. The reﬂection coeﬃcient obtained via the two methods is presented as a function of the frequency and of the direction of propagation of the incident wave. We ﬁnd excellent agreement for a large range of frequencies going from the long-wave limit to frequencies beyond the ﬁrst band-gap and for angles of incidence ranging from normal to near parallel incidence. The case of a semi-inﬁnite metamaterial is also presented and is seen to be a reliable measure of the average behavior of the ﬁnite meta-structure. A tremendous gain in terms of computational time is obtained when using the relaxed micromorphic model for the study of the considered meta-structure. The present paper is an important stepping stone for the development of a theoretical paradigm shift enabling the exploration of meta-structures at large scales.
Organizational Choices and Venturing Modes: An Analysis of Corporate Venture Capital Activities in Legacy Media

Traditional media companies increasingly search for know-how and novel input outside their own networks to keep up with the rapidly changing environment. As an instrument to explore and exploit new business opportunities, corporate venture capital (CVC) has become particularly important. However, there is little research on the CVC investments by legacy media companies, despite these having been responsible for some of the largest investments in past years. To lay a foundation for research in this field, we investigate how traditional media companies organize their CVC activities. Using an extensive analysis of the 90 largest legacy media companies in Germany, the United Kingdom, and the United States, we explore the organizational structure, investment objectives, investment focus, and industry related- ness of investments. Our findings show the majority of investments are conducted through different forms of direct investments, predominantly focusing on strategic goals and using both exploration and exploitation. Moreover, we identify a trend toward investments in content- and commerce-related fields.
Surfing multiple conformation-property landscapes via machine learning: Designing magnetic anisotropy

The advent of computational statistical disciplines, such as machine learning, is leading to a paradigm shift in the way we conceive the design of new compounds. Today computational science does not only provide a sound understanding of experiments, but also can directly design the best compound for specific applications. This approach, known as reverse engineering, requires the construction of models able to efficiently predict continuous structure-property maps. Here we show that reverse engineering can be used to tune the magnetic properties of a single-ion molecular magnet in an automated intelligent fashion. We design a machine learning model to predict both the energy and magnetic properties as function of the chemical structure. Then, a particle-swarm optimization algorithm is used to explore the conformational landscapes in the search for new molecular structures leading to an enhanced magnetic anisotropy. We find that a 5% change in one of the coordination angles leads to a 50% increase in the anisotropy. Our approach paves the way for a machine-learning-driven exploration of the chemical space of general classes of magnetic materials. Most importantly, it can be applied to any structure-property relation and offers an effective way to automatically generate new materials with target properties starting from the knowledge of previously synthesized ones.
Data Poisoning Attacks to Local Differential Privacy Protocols

Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform privacy-preserving data analytics. In particular, each user locally perturbs his/her data to preserve privacy before sending it to the data collector, who aggregates the perturbed data to obtain statistics of interest. Over the past several years, researchers from multiple communities--such as security, database, and theoretical computer science-- have proposed many LDP protocols. These studies mainly focused on improving the utility of the LDP protocols. However, the security of LDP protocols is largely unexplored. In this work, we aim to bridge this gap. We focus on LDP protocols for frequency estimation and heavy hitter identification, which are two basic data analytics tasks. Specifically, we show that an attacker can inject fake users into an LDP protocol and the fake users send carefully crafted data to the data collector such that the LDP protocol estimates high frequencies for certain items or identifies them as heavy hitters. We call our attacks data poisoning attacks. We theoretically and/or empirically show the effectiveness of our attacks. We also explore two countermeasures against our attacks. Our experimental results show that they can effectively defend against our attacks in some scenarios but have limited effectiveness in others, highlighting the needs for new defenses against our attacks.
Unsupervised Multi-Document Opinion Summarization as Copycat-Review Generation

Summarization of opinions is the process of automatically creating text summaries that reflect subjective information expressed in input documents, such as product reviews. While most previous research in opinion summarization has focused on the extractive setting, i.e. selecting fragments of the input documents to produce a summary, we let the model generate novel sentences and hence produce fluent text. Supervised abstractive summarization methods typically rely on large quantities of document-summary pairs which are expensive to acquire. In contrast, we consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a multi-product review collection. Intuitively, we want to design such a model that, when generating a new review given a set of other reviews of the product, we can control the `amount of novelty' going into the new review or, equivalently, vary the degree of deviation from the input reviews. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and products they correspond to are associated with stochastic latent codes, and the review generator ('decoder') has direct access to the text of input reviews through the pointer-generator mechanism. In experiments on Amazon and Yelp data, we show that in this model by setting at test time the review's latent code to its mean, we produce fluent and coherent summaries.
Experimental study and modeling using Super Pro Designer of the elimination of a dispersed azo dye by coagulation-microfiltration

The main objective in this study was to optimize the removal of a dispersed azo dye (Red Terasil) widely used in the textile industry, combining coagulation and microfiltration processes. The purpose of coagulation was to destabilize and aggregate the suspended matter mainly responsible for the coloration of the solution. The study was completed by a simulation of the process using the SuperPro Designer software (SD) (Version 9) followed by a comparison of the obtained results with the corresponding experimental values. A priori the experimental part consisted of determining the optimal coagulation conditions on a Jar-test, namely the coagulant dose and pH of the solution. The resulting solution from the coagulation process was used as a feed to the microfiltration pilot where the variation with time of several parameters such as the dye concentration, the turbidity of the permeate and the concentrate solutions, the permeate flux and the transmembrane pressure (TMP) were followed, determining also the membrane rejection coefficient. SuperPro Designer software (SD) was used to simulate the combination coagulation-microfiltration process, the monitored parameters with filtration time, in this part, were the dye concentration in the permeate and the concentrate and the permeate flux. The obtained results showed that the coagulation-microfiltration process was very effective in eliminating the dye with a considerable concentration decrease, an important reduction of the turbidity of the solution in the permeate, a reduction of its flux and the membrane rejection coefficient exceeding 98%. The results calculated by SD were in quite good agreement with the experimental results.
Deregulation and the Determinants of Entry: Evidence from the German Interurban Bus Industry

Two years after the deregulation of the German interurban bus industry in January 2013, two new entrants emerged as market leaders: MeinFernbus (MFB) and FlixBus (FB). We use a comprehensive route-level data set to investigate the determinants of route entry for both providers. Applying survival models, we find that both companies show an increased probability to enter populous, centrally located routes with large shares of young inhabitants; however, they both avoid entries into routes including an airport or with low quality rail connection. Furthermore, both market leaders refrain from entering small and medium-sized routes in which another provider is already operating. In large markets, however, they both show an increased entry probability independent of the presence of a competitor.
Do Individual Investors Ignore Transaction Costs ? May 19 , 2017

Using close to 800,000 (2,000,000) transactions by 66,000 (303,000) households in the United States (in Finland), we show that individual investors with longer holding periods choose to hold less liquid stocks in their portfolios, consistent with Amihud and Mendelson’s (1986) theory of liquidity clienteles. The relationship between holding periods and transaction costs is stronger amongst more financially sophisticated households. Households whose holding periods are positively related to transaction costs also earn higher gross returns on their investments before accounting for transaction costs, suggesting that attention to non-salient transaction costs is an indication of investing ability. We confirm our findings by analyzing changes to investors’ holding periods around exogenous shocks to stock liquidity. JEL Classifications: G11, G12, G14, G32, G33, M4, L14, D82
Smart device system to manage virtual information in small scale settings using augmented reality interfaces

Smart devices have become more commonplace in the past years, surpassing mobile phones in use. Partially, this rise can be attributed to the possibility of customizing the devices with new software, which in turn are easier to create. These applications exists for many purposes, allowing new ways for the users to create and share their experiences but also provide useful services, such as GPS enabled navigation. Augmented reality (AR), meaning the addition of virtual information to the real world in a seamless manner, can be explored with the devices as well. AR requires a combination of specific hardware, that are found in compact smart devices, to be functional. Smart devices, with their large displays and cameras allow vision based detection and visual presentation of augmentations. AR applications enable smart devices to function as a “magnifying glass” to the underlying data of the observed objects or locations. This thesis explores the new paradigm of interaction that AR allows on smart devices. This is achieved through the construction of an Android smart device application, utilizing free or open source software development tools. The main application concept features linking of user specified instances, objects or locations, to information that the user associates with the instances. By using the application, the user can create, modify and detect the added information from their environment. The research was initiated by exploring the literature related to the research domain and performing a review of the existing AR applications available on Android devices. The knowledge from the existing literature and reviews was used to formalize the research question and to define the thesis's limitations. As the creation of a new application was the principal target of this thesis, Systems Development Research (SDR), a research approach relating and contributing to Design Science (DS), was chosen to be utilized. The application was designed with the use of scenarios, that detailed the key features that the system should implement. The scenarios were used in the evaluation of the application as well, by identifying the deviation from the design that had occurred during the implementation. Volunteers also used the scenarios to evaluate how well the key features were implemented within the application. The applications performance and stability were also evaluated. The results of the evaluation show that the application implements the features described in the scenarios, with volunteers stressing the importance of feedback from the application to improve its use. The application is also shown to be stable and developed conforming to the design guidelines and requirements expected of it. The creation of a AR smart device application using vision based detection requires identification of the systems requirements and the capabilities of the software development tools. For vision based detection, OpenCV offers comprehensive and cross-platform capable libraries, which can be used to implement the required AR functions.