Miracle-dz/UniFAR-FTUs huggingface dataset:
L EARNING D ISCONNECTED M ANIFOLDS : A VOIDING T HE N O GAN’ S L AND B Y L ATENT R EJECTION

Standard formulations of GANs, where a continuous function deforms a connected latent space, have been shown to be misspecified when fitting disconnected manifolds. In particular, when covering different classes of images, the generator will necessarily sample some low quality images in between the modes. Rather than modify the learning procedure, a line of works aims at improving the sampling quality from trained generators. Thus, it is now common to introduce a rejection step within the generation procedure. Building on this, we propose to train an additional network and transform the latent space via an adversarial learning of importance weights. This idea has several advantages: 1) it provides a way to inject disconnectedness on any GAN architecture, 2) since the rejection happens in the latent space, it avoids going through both the generator and the discriminator saving computation time, 3) this importance weights formulation provides a principled way to reduce the Wasserstein’s distance to the target distribution. We demonstrate the effectiveness of our method on different datasets, both synthetic and high dimensional.
Neural Correlates of Task-order Preparation in Dual Tasks: An EEG Study

Abstract Dual-task scenarios require a coordinated regulation of the processing order of component tasks in light of capacity limitations during response selection. A number of behavioral and neuroimaging findings suggest a distinct set of control processes involved in preparing this task order. In this study, we investigated electrophysiological correlates of task-order preparation in a variant of the overlapping dual-task paradigm with cue-determined task order that resulted in trials with blockwise fixed task order as well as trials with repeated and switched task order in blocks with variable task order. During the cue–stimulus interval, we found an earlier centroparietal order-mixing positivity and a later parietal order-switch positivity. A decoding approach based on multivariate pattern analysis showed that the order-mixing positivity is a necessary prerequisite for successful order selection, whereas the order-switch positivity appears to facilitate the implementation of a new task order after its selection. These correlates of order preparation share striking similarities to commonly found potentials involved in the preparation of individual tasks in the (single-)task-switching paradigm, which is strong empirical support for the account that the underlying preparatory processes are to be considered as higher-level control signals that are implemented independently of specific task representations.
gender-baSed ViCtimization, miSogyny and the hate Crime paradigm in england and WaleS

In England and Wales, legislation pertaining to hate crime recognizes hostility based on racial identity, religious affiliation, sexual orientation, disability or transgender identity. Discussions abound as to whether this legislation should also recognize hostility based on gender or misogyny. Taking a socio-legal analysis, the chapter examines hate crime, gender-based victimization and misogyny alongside the impact of victim identity construction, access to justice and the international nature of gendered harm. The chapter provides a comprehensive investigation of gender-based victimization in relation to targeted hostility to assess the potential for its inclusion in hate crime legislation in England and Wales.
62-68 CO-0010.fm

Artificial neural network techniques show an excellent ability to predict the data (output) for various complex characteristics (input). It is primarily specialized to solve nonlinear relationship problems. This study is an experimental investigation that applies artificial neural network techniques and an experimental design to predict the cyclic polarization curves of the super-austenitic stainless steel AL-6XN alloy with sensitization. A cyclic polarization test was conducted in a 3.5% NaCl solution based on an experimental design matrix with various factors (degree of sensitization, temperature, pH) and their levels, and a total of 36 cyclic polarization data were acquired. The 36 cyclic polarization patterns were used as training data for the artificial neural network model. As a result, the supervised learning algorithms with back-propagation showed high learning and prediction performances. The model showed an excellent training performance (R2=0.998) and a considerable prediction performance (R2=0.812) for the conditions that were not included in the training data.
Different Behavioral Types of Distributional Preferences Are Characterized by Distinct Neural Signatures

Abstract There are many situations where resources are distributed between two parties and where the deciding party has information about the initial distribution and can change its outcome, for example, the allocation of budget for funds or bonuses, where the deciding party might have self-interested motives. Although the neural underpinnings of distributional preferences of resources have been extensively studied, it remains unclear if there are different types of distributional preferences and if these types underlie different disposing neural signatures. We used source-localized resting EEG in combination with a data-driven clustering approach to participants' behavior in a distribution game in order to disentangle the neural sources of the different types of distributional preferences. Our findings revealed four behavioral types: Maximizing types always changed initial distributions to maximize their personal outcomes, and compliant types always left initial distributions unchanged. Disadvantage-averse types only changed initial distributions if they received less than the other party did, and equalizing types primarily changed initial distributions to fair distributions. These behavioral types differed regarding neural baseline activation in the right inferior frontal gyrus. Maximizing and compliant types showed the highest baseline activation, followed by disadvantage-averse types and equalizing types. Furthermore, maximizing types showed significantly higher baseline activation in the left orbitofrontal cortex compared to compliant types. Taken together, our findings show that different types of distributional preferences are characterized by distinct neural signatures, which further imply differences in underlying psychological processes in decision-making.
A study on various optimization algorithms for task scheduling in cloud computing

The prompt development of storage technologies, processing and the internet’s success has made the computing resources more available, more influential and cheaper than before. This drift in the technological field has paved way for the realization of a new computational model which is the Cloud Computing. One of the most challenging problems faced in cloud computing is task scheduling, where the clients want their task to be finished as per the deadline within a shortest time possible. Task scheduling in cloud is done based on various parameters such as the priority, cost, time, bandwidth, resource utilization, performance, etc. The current work focuses on providing a detailed review on various optimization algorithms for scheduling in cloud. This study is useful in understanding various optimization algorithms that are used for task scheduling, the way they behave and function. The algorithms presented here are mainly focused to minimize the computational cost and execution time there by increasing the overall efficiency.
Five thousand years of fire history in the high North Atlantic region: natural variability and ancient human forcing

Abstract. Biomass burning influences global atmospheric chemistry by releasing greenhouse gases and climate-forcing aerosols. There is controversy about the magnitude and timing of Holocene changes in biomass burning emissions from millennial to centennial timescales and, in particular, about the possible impact of ancient civilizations. Here we present a 5 kyr record of fire activity proxies levoglucosan, black carbon, and ammonium measured in the RECAP (Renland ice cap) ice core, drilled in coastal eastern Greenland, and therefore affected by processes occurring in the high North Atlantic region. Levoglucosan and ammonium fluxes are high from 5 to 4.5 kyr BP (thousand years before 2000 CE) followed by an abrupt decline, possibly due to monotonic decline in Northern Hemisphere summer insolation. Levoglucosan and black carbon show an abrupt decline at 1.1 kyr BP, suggesting a decline in the wildfire regime in Iceland due to the extensive land clearing caused by Viking colonizers. All fire proxies reach a minimum during the second half of the last century, after which levoglucosan and ammonium fluxes increase again, in particular over the last 200 years. We find that the fire regime reconstructed from RECAP fluxes seems mainly related to climatic changes; however over the last millennium human activities might have influenced wildfire frequency/occurrence substantially.
2008 Stealing from Thieves : Firm Governance and Performance when States are Predatory Art Durnev and

We investigate how predatory government policies (expropriation, lack of property rights protection, corruption, crime) interact with managerial incentives in shaping firm governance structure. Our model shows that owners have lower incentives to encourage valuemaximization by managers if the government is likely to expropriate firm profits. This result emerges because it is more difficult for governments to seize firm profits that managers have already stolen and hidden from the owners. The model also demonstrates that the positive valuation effect of stronger firm governance is lower in states with more predatory governments. We test these predictions using several distinct data sets on firm governance and disclosure practices, and the business and financing obstacles firms face due to government intervention. The empirical results are consistent with the model’s predictions. Specifically, we find that firms located in countries with more predatory governments practice weaker governance and disclose less information. Further, the previously documented positive relation between firm governance and firm performance is weaker or disappears altogether when governments pursue predatory policies. Finally, in countries with more predatory governments, firm-specific characteristics are less important in explaining variation in governance and firms have more similar governance structures.
Studying and Visualizing Contagion on Graphs

In this report, we investigate the NP-complete minimum contagious set problem in bootstrap percolation. We present the following: three new bounds providing approximations for the size of minimum contagious sets, efficient and optimal algorithms for special types of graphs, and an optimal algorithm for any graph. Furthermore, we created a publicly available software product named ‘Booper’, a web application that can help mathematicians research and visualize bootstrap percolation. Users of Booper are able to upload a custom graph, find the minimum contagious set of a graph, perform iterations of bootstrap percolation with user-defined threshold and probability parameters, and much more.
C ONTINUAL LEARNING WITH NEURAL ACTIVATION IMPORTANCE

Continual learning is a concept of online learning along with multiple sequential tasks. One of the critical barriers of continual learning is that a network should learn a new task keeping the knowledge of old tasks without access to any data of the old tasks. In this paper, we propose a neuron importance based regularization method for stable continual learning. We propose a comprehensive experimental evaluation framework on existing benchmark data sets to evaluate not just the accuracy of a certain order of continual learning performance also the robustness of the accuracy along with the changes in the order of tasks.
Volumetry of Olfactory Structures in Mild Cognitive Impairment and Alzheimer’s Disease: A Systematic Review and a Meta-Analysis

Olfactory decline is an early symptom of Alzheimer’s disease (AD) and is a predictor of conversion from mild cognitive impairment (MCI) to AD. Olfactory decline could reflect AD-related atrophy of structures related to the sense of smell. The aim of this study was to verify whether the presence of a clinical diagnosis of AD or MCI is associated with a volumetric decrease in the olfactory bulbs (OB) and the primary olfactory cortex (POC). We conducted two systematic reviews, one for each region and a meta-analysis. We collected articles from PsychNet, PubMed, Ebsco, and ProQuest databases. Results showed large and heterogeneous effects indicating smaller OB volumes in patients with AD (k = 6, g = −1.21, 95% CI [−2.19, −0.44]) and in patients with MCI compared to controls. There is also a trend for smaller POC in patients with AD or MCI compared to controls. Neuroanatomical structures involved in olfactory processing are smaller in AD and these volumetric reductions could be measured as early as the MCI stage.
Why are wasteyutes a ting?

This paper examines lexical enregisterment through TH/DH-stopping in Multicultural Toronto English (MTE), a multiethnolect emergent in the Greater Toronto Area. Sociolinguistic interview data from young MTE speakers reveals an overall ~10% rate of stopping, with teenage males being the primary stoppers. However, despite the presence of stopping in the vernacular of most speakers, certain terms referring to character archetypes e.g. wasteyutes, mandem have become sites of enregisterment of TH/DHstopping in MTE rather than enregisterment of stopping in more frequent words or of stopping itself. We argue that this is because these lexical items implement reflexive tropes, as speakers thought to be stoppers are those who might be labeled wasteyutes or mandem: young, male, suburban, typically nonwhite, and typically low status. As such, performance of these stereotypical personae fosters indexical linking between sound (TH/DH-stopping) and culturally salient identities (wasteyutes, mandem), cementing enregisterment of these terms in MTE. This working paper is available in University of Pennsylvania Working Papers in Linguistics: https://repository.upenn. edu/pwpl/vol26/iss2/3
Simultaneous Optimization of Transformer Tap Changer and Network Capacitors to Improve the Distribution System’s Static Security Considering Distributed Generation Sources

Voltage control and reactive power play an important role in the operation of the distribution network. Accordingly, conventional methods such as the installation of a capacitor in an optimum location with a proper capacity and optimal transformer tap setting which has an impressive effect on voltage control and reactive power are used. But the study on the simultaneous use of these two methods is limited and it seems necessary to be conducted. These days the presence of Distributed Generation (DG) resources has grown in distribution networks. The presence of distributed generation resources has a great influence on the voltage profile due to the radial structure of the distribution network and the low X/R ratio. Therefore, it is necessary to consider the optimal coordination of the use of switchable capacitors and the setting of transformer taps in the presence of distributed generation resources to improve the voltage profile and reduce losses. This paper examines the simultaneous use of capacitors and transformer taps in distribution networks to reduce the voltage deviation and distribution losses in the presence of distributed generation resources. In order to explain the objectives, six different operation scenarios have been defined and studied. The above study is implemented based on the IEEE, 13 and 34 bus standard networks and the results are presented. The presented results clearly indicate the necessity of coordinating the use of these tools in distribution networks.
Ecological relationships at a near-natural treeline, Rolwaling Valley, Nepal Himalaya: Implications for the sensitivity to climate change

At a global scale, heat deficits during the growing season result in growth limitations, which determine the elevation of natural alpine treelines. Thus, the expected response to global warming is a treeline advance to higher elevations. However, empirical studies of diverse mountain ranges have yielded evidence of both advancing alpine treelines as well as rather insignificant responses. Based on an extensive collection of field data, we analysed population structures and regeneration patterns, investigated population density-environment relationships and correlated tree growth with climate in order to assess the sensitivity to climate warming of a near-natural treeline ecotone in east-central Nepal. The presence of an elevational zone dominated by a gnarled growth form of Rhododendron campanulatum physiognomically classifies the treeline as a krummholz treeline. The fraction of juvenile tree individuals reflects prolific regeneration and stand densification. The species-specific variation in adult and juvenile stand density along the treeline ecotone depends not only on temperature but also on soil, topographic, and other microclimatic conditions. Rhododendron campanulatum shows highest competitiveness in the krummholz belt under a constellation of site conditions influenced by this species itself. By contrast, Abies spectabilis (Himalayan Silver Fir) and Betula utilis (Himalayan Birch) have gained predominance under warmer and more nutrient-rich habitat conditions in the closed forest below. The dense krummholz belt effectively controls the potential upslope migration of subalpine forest tree species. Abies spectabilis growth-climate correlations show changing growth limitations in the course of the 20th century, most likely due to intensified climate warming in recent decades, when decreasing moisture availability during the pre-monsoon season has affected Abies spectabilis' radial growth. It is evident from our results that to date the treeline has responded to climate warming in terms of stand densities, seed-based regeneration and growth patterns of trees, the treeline position, however, is rather stable. A treeline shift is to be expected in the midto long-term only.
Synthesising Realistic Calcium Traces of Neuronal Populations Using GAN

Calcium imaging has become a powerful and popular technique to monitor the activity of large populations of neurons in vivo. However, for ethical considerations and despite recent technical developments, recordings are still constrained to a limited number of trials and animals. This limits the amount of data available from individual experiments and hinders the development of analysis techniques and models for more realistic sizes of neuronal populations. The ability to artificially synthesize realistic neuronal calcium signals could greatly alleviate this problem by scaling up the number of trials. Here, we propose a Generative Adversarial Network (GAN) model to generate realistic calcium signals as seen in neuronal somata with calcium imaging. To this end, we propose CalciumGAN, a model based on the WaveGAN architecture and train it on calcium fluorescent signals with the Wasserstein distance. We test the model on artificial data with known ground-truth and show that the distribution of the generated signals closely resembles the underlying data distribution. Then, we train the model on real calcium traces recorded from the primary visual cortex of behaving mice and confirm that the deconvolved spike trains match the statistics of the recorded data. Together, these results demonstrate that our model can successfully generate realistic calcium traces, thereby providing the means to augment existing datasets of neuronal activity for enhanced data exploration and modelling.
Role of Thylakoid Protein Phosphorylation in Energy-Dependent Quenching of Chlorophyll Fluorescence in Rice Plants

Under natural environments, light quality and quantity are extremely varied. To respond and acclimate to such changes, plants have developed a multiplicity of molecular regulatory mechanisms. Non-photochemical quenching of chlorophyll fluorescence (NPQ) and thylakoid protein phosphorylation are two mechanisms that protect vascular plants. To clarify the role of thylakoid protein phosphorylation in energy-dependent quenching of chlorophyll fluorescence (qE) in rice plants, we used a direct Western blot assay after BN-PAGE to detect all phosphoproteins by P-Thr antibody as well as by P-Lhcb1 and P-Lhcb2 antibodies. Isolated thylakoids in either the dark- or the light-adapted state from wild type (WT) and PsbS-KO rice plants were used for this approach to detect light-dependent interactions between PsbS, PSII, and LHCII proteins. We observed that the bands corresponding to the phosphorylated Lhcb1 and Lhcb2 as well as the other phosphorylated proteins were enhanced in the PsbS-KO mutant after illumination. The qE relaxation became slower in WT plants after 10 min HL treatment, which correlated with Lhcb1 and Lhcb2 protein phosphorylation in the LHCII trimers under the same experimental conditions. Thus, we concluded that light-induced phosphorylation of PSII core and Lhcb1/Lhcb2 proteins is enhanced in rice PsbS-KO plants which might be due to more reactive-oxygen-species production in this mutant.
Removal of Ampicillin by Heterogeneous Photocatalysis: Combined Experimental and DFT Study

A long-term exposition of antibiotics represents a serious problem for the environment, especially for human health. Heterogeneous photocatalysis opens a green way for their removal. Here, we correlated the structural-textural properties of TiO2 photocatalysts with their photocatalytic performance in ampicillin abatement. The tested nanoparticles included anatase and rutile and their defined mixtures. The nominal size range varied from 5 to 800 nm, Aeroxide P25 serving as an industrial benchmark reference. The degradation mechanism of photocatalytic ampicillin abatement was studied by employing both experimental (UPLC/MS/MS, hydroxyl radical scavenger) and theoretical (quantum calculations) approaches. Photocatalytic activity increased with the increasing particle size, generally, anatase being more active than rutile. Interestingly, in the dark, the ampicillin concentration decreased as well, especially in the presence of very small nanoparticles. Even if the photolysis of ampicillin was negligible, a very high degree of mineralization of antibiotic was achieved photocatalytically using the smallest nanoparticles of both allotropes and their mixtures. Furthermore, for anatase samples, the reaction rate constant increases with increasing crystallite size, while the degree of mineralization decreases. Importantly, the suggested degradation pathway mechanism determined by DFT modeling was in very good agreement with experimentally detected reaction products.
ALGORITHMS FOR DATA RELIABILITY BASED ON A CRYPTOGRAPHIC DISTRIBUTED DATABASE (BLOCKCHAIN)

This research paper presents algorithms for solving the problem of increasing data flow by forming a distributed database. Based on this, methods for optimizing the storage and processing of data in computer networks are presented. A distributed registry and cryptographically sensitive data processed by blockchain technology are considered. In the general case, blockchain is a multifunctional and multi-level information technology designed to reliably record various procedures and operations carried out on the network, a technology for reliable distributed storage of records of all ever completed transactions. The blockchain is a chain of data blocks, the volume of which is constantly growing as new blocks are added with records of the most recent transactions.