Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting rhythmic spiking through the power spectra of point process model residuals

Objective Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron’s spiking, one might attempt to seek peaks in the spike train’s power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period (“RP”, the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established “shuffling” procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection. Approach In a novel “residuals” method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding nr milliseconds. Finally, we compute the PSD of the model’s residuals. Main results We compared the residuals and shuffling methods’ ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true-versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey -- in which alpha-beta oscillations (8-30 Hz) were anticipated -- the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection. Significance These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.
ZZZ3 protects human embryonic stem cells from nucleolar stress by boosting mTOR/ribosome pathway

Embryonic stem cells (ESCs) are defined as stem cells with self-renewing and differentiation capabilities. These unique properties are tightly regulated and controlled by complex genetic and molecular mechanisms whose understanding is essential for both basic and translational research. A large number of studies have mostly focused on understanding the molecular mechanisms governing pluripotency and differentiation of ESCs, while the regulation of proliferation has received comparably less attention. In mouse ESCs, pluripotency and proliferation can be independent processes meaning that it is possible for mouse ESCs to maintain their pluripotent state without actively proliferating. Here, we investigate the role of ZZZ3 (Zinc Finger ZZ-Type Containing 3) function in human ESCs homeostasis. We found that knockdown of ZZZ3 strongly decreases ribosome biogenesis, translation, and mTOR signaling leading to nucleolar stress and significant reduction of cell proliferation. This process occurs without affecting pluripotency, suggesting that ZZZ3-depleted ESCs enter a dormant-like state and that proliferation and pluripotency can be uncoupled also in human ESCs.
Short-latency preference for faces in the primate superior colliculus

Face processing is fundamental to primates and has been extensively studied in higher-order visual cortex. Here we report that visual neurons in the midbrain superior colliculus (SC) display a preference for faces, that the preference emerges within 50ms of stimulus onset – well before “face patches” in visual cortex – and that this activity can distinguish faces from other visual objects with accuracies of ∼80%. This short-latency preference in SC depends on signals routed through early visual cortex, because inactivating the lateral geniculate nucleus, the key relay from retina to cortex, virtually eliminates visual responses in SC, including face-related activity. These results reveal an unexpected circuit in the primate visual system for rapidly detecting faces in the periphery, complementing the higher-order areas needed for recognizing individual faces. One-Sentence Summary An unexpected circuit through the primate midbrain reports the presence of a face in peripheral vision in 1/20th of a second.
Default Risk Sharing between Banks and Markets: The Contribution of Collateralized Debt Obligations

This paper contributes to the economics of financial institutions risk management by exploring how loan securitization affects their default risk, their systematic risk, and their stock prices. In a typical CDO transaction a bank retains through a first loss piece a very high proportion of the default losses, and transfers only the extreme losses to other market participants. The size of the first loss piece is largely driven by the average default probability of the securitized assets. If the bank sells loans in a true sale transaction, it may use the proceeds to expand its loan business, thereby affecting systematic risk. For a sample of European CDO issues, we find an increase of the banks%u2019 betas, but no significant stock price effect around the announcement of a CDO issue.
A graphical presentation of MLF types with a linear-time local unification algorithm .

MLF is a language that extends ML and System F and combines the benefits of both. We propose a dag representation of MLF types that superposes a term-dag, encoding the underlying term structure with sharing, and a tree encoding the binding structure. Compared to the original definition, this representation is more canonical as it factors out most of the notational details; it is also closely related to first-order terms. Moreover, it permits a simpler and more direct definition of type instance that combines type instance on firstorder term-dags, simple operations on the binding tree, and a control that allows or rejects potential instances. Using this representation, we build a local linear-time unification algorithm for MLF types, which we prove sound and complete with respect to the specification. Key-words: System F, MLF, Unification, Types, Graphs, Binders Une présentation par graphes de MLF avec un algorithme d’unification local linéaire Résumé : MLF est un langage qui étend ML et le système F, tout en combinant les avantages des deux systèmes. Nous proposons une représentation par dag des types de MLF, qui superpose un dag encodant la structure sous-jacente de terme avec partage explicite, et un arbre encodant la structure des lieurs. Comparée à la définition originelle, cette représentation est plus canonique, car elle évite la plupart des détails de notation; elle est également très proche de celle utilisée pour les termes du premier ordre. Par ailleurs, elle permet une définition plus simple et plus directe de la relation d’instance entre types, en combinant instance sur les types du premier ordre, des opérations simples sur l’arbre des lieurs, et un contrôle de permissions qui autorise ou rejette certaines instances. En utilisant cette représentation, nous construisons un algorithme d’unification incrémental et de complexité linéaire. Nous prouvons celui-ci correct et complet vis-à-vis de sa spécification. Mots-clés : Système F, MLF, Unification, Types, Graphes, Lieurs A graphical presentation of MLF types with a linear-time local unification algorithm. 3
Repositório ISCTE-IUL

—With thousands of mobile applications submitted to online application stores the mobile application market has experienced a signiﬁcant growth. This growth is however accompanied by an increase in malware presence which is detected after infecting users or when a it is reported to the store. A possible solution would be to leverage those reports, across all mobile ecosystem, creating a shared reputation system, to provide more accurate feedback to the app stores quality assurance and security teams. To support this sharable reputation management system, we present a scalable blockchain-based solution, that provides the necessary scalability, data privacy and trust requirements, while being cost-effective. This paper also presents a real case study and respective results on performance, scalability, and cost evaluation. case study and respective results on performance, scalability, and cost evaluation.
Credit-based Differential Privacy Stochastic Model Aggregation Algorithm for Robust Federated Learning via Blockchain

By encapsulating model parameters in blocks when training machine learning models collaboratively, blockchain is recognized as a promising enabling technology to facilitate reliable federated learning under a distributed and untrusted environment. However, storing updated models of each worker in blockchain induces potential privacy risks, such as membership inference attacks. Besides, the volatile network conditions in the distributed environment may cause the deterioration of system robustness. This paper aims at addressing the privacy and robustness issues mentioned above. Specifically, a Credit-based Differential Privacy stochastic model aggregation algorithm combined with SIGN operation (Cre-DPSIGN) is adopted in our peer-to-peer network, which can realize the trade-off between privacy and accuracy. Furthermore, leveraging the transparency and tamper-proofing of blockchain, we design practical and reliable smart contracts for unbiased sampling based on the credit of workers to improve system robustness against Byzantine workers. In addition, we have demonstrated that the use of biased differential privacy mechanisms can lead to performance degradation. Therefore, we have introduced two unbiased differential privacy mechanisms and have proven their convergence and privacy guarantee. Extensive experiments conducted on MNIST datasets show that our algorithm can achieve byzantine fault tolerance rate with a private loss ϵ = 0.4. Compared with the state-of-the-art, aka DP-RSA (IJCAI-22), Cre-DPSIGN shows lower privacy loss consumption and better system robustness.
Sustainable Procurement: Integrating Classroom Learning with University Sustainability Programs.

Organizations are facing increased pressure from various stakeholders to address issues ofsustainability,resultinginagrowingdemandforsustainabilityeducationandtraining. Procurement groups remain the key drivers of many sustainability-related strategies, placing pressure on universities to integrate sustainability concepts into the business curriculum. This article describes a replicable educational model that allows students to learn about sustainable procurement while concurrently promoting sustainability within the university community.
Paradigms: The Business of Discovering the Future

With this book, readers learn how to break out of old patterns that are now outdated and slowing down progress. Joel Barker shares his ideas for change that he has taught to thousands of executives from such companies as General Mills, 3M, Texaco, AT&T and General Foods. The author explains how to find the people in an organization most likely to spot a new trend, how to help key people adapt when a massive change is occurring and how to learn to effectively manage "intractable problems" and dramatically improve results. This book is full of concrete examples of paradigm shifts and predictions for the future that will help guide companies in their decisions, as well as new material for the paperback edition with examples to further clarify the key concepts for successful change in the 1990s.
Multispectroscopic methodology to study Libyan Desert Glass and its 1 formation conditions 2

17 The Libyan Desert Glass (LDG) is a melt product whose origin is still a matter 18 of controversy. With the purpose of adding new information about this enigma, the 19 present paper analyzes the inner part of the LDG specimens and compares it with the 20 results of the LDG surfaces. An integrated analytical methodology was used combining 21 different techniques such as Raman spectroscopy, in point-by-point and imaging modes, 22 Scanning Electron Microscopy with X-ray microanalysis (SEM-EDS), Energy- 23 Dispersive Micro X-ray Fluorescence spectrometry (µ-EDXRF), Electron Probe Micro 24 Analyzer (EPMA) and optical cathodeluminescence (Optical-CL). According to our results, flow structures of the melt and the amorphous nature of the matrix could be 26 discerned. Moreover, the observed displacement of the Raman bands, such as the cases 27 of quartz and zircon, and the identification of certain compounds such as coesite (the 28 most clarifying phase of high pressures), α-cristobalite, gypsum, anhydrite, corundum, 29 rutile, amorphous calcite, aragonite and calcite allowed us to know that LDGs could be 30 subjected to shock pressures between 6 and more than 30 GPa, and temperatures 31 between 300 and 1470°C. The differences of temperature and pressure would be 32 provoked by different cooling process during the impact. Besides, in most cases the 33 minerals corresponding to high pressure and temperatures were located in the inner part 34 of the LDGs, with some exceptions that could be explained because they were trapped 35 subsequently to the impact; there was more than one impact or heterogeneous cooling. 36 Furthermore, nitrogen and oxygen gases were identified inside bubbles, which 37 could have been introduced from the terrestrial atmosphere during the meteorite impact. 38 These data helped us to clarify some clues about the origin of these enigmatic 39 samples. 40
BVC-sjuksköterskans erfarenheter av hälsofrämjande insatser till barn med långvarig sjukdom

Introduction: One out of ten children in Sweden has a long-term illness, something that may have consequences for the entire family. The primary objective for the child health care services is to contribute as much as possible to ensure all childrens ́ health. Long-term illness does not automatically mean bad health, which suggests that the efforts of the child health care services may be significant to these families. Aim: To describe the experiences of the child health care nurse during health promotional efforts, when the child due to long-term illness is encountering numerous services. Method: A major qualitative study including 15-20 semi-structured interviews is planned. A pilot study where four child health care nurses were interviewed has been carried out to try the method. The answers were analysed with qualitative content analysis. Findings: Three categories were derived from analysis: ”To see the health in the illness”, ”Communication” and ”Ways to health”. Our findings indicate that the child health care nurses focus on what is healthy about the child. To be able to provide health promotional care, the child health care nurses call for improved communication with health care providers in hospitals. Discussion: Health care is perceived as being of great importance for children with long-term illness. The child health care nurse needs information about the child and its experiences. Insufficient information run the risk of producing an unprofessional response plus putting a great responsibility on the parents as the primary source of information. The principal strategies for health promotional efforts were to apply a family-centered approach and building a relationship with the family. Conclusion: It is possible to improve the care of families where the child has a long-term illness, in both the child health care services and the inpatient care. We accordingly find that there is a good reason for going further with a full scale study.
HighRPM: Combining Integrated Measurement and Sofware Power Modeling for High-Resolution Power Monitoring

In an era where power and energy are the first-class constraints of computing systems, accurate power information is crucial for energy efficiency optimization in parallel computing systems. Existing power monitoring techniques rely on either software-centric power models that suffer from poor accuracy or integrated hardware measurement schemes that have a low reading update frequency and coarse granularity. These result in a low spatiotemporal resolution for power monitoring. This paper introduces HighRPM, a new method for accurately measuring power consumption on parallel computing systems. HighRPM combines coarse-grained power sensor readings and software power modeling techniques to improve temporal and spatial resolutions. To provide high-frequent power readings in the temporal domain, HighRPM employs statistical modeling and machine learning techniques to predict the long-term power trend and the short-term fluctuations in power consumption. To improve spatial coverage, HighRPM takes low-time resolution node-level power consumption and uses a neural network to distribute the power readings to lower-level computing components like CPUs and memory components. We evaluate HighRPM by applying it to both ARM-based and X86-based platforms. Experimental results show that HighRPM improves time resolution by 10 times, provides accurate readings for CPUs and memory, and reduces error by 7-24% compared to other power modeling methods.
How Creative is the Crowd in Describing Smart Home Scenarios ?

Internet of Things (IoT) is recently attracting vendors like Google, Homey, and Samsung that have already brought to the market a plethora of devices and services supporting smart home automation. However, recent studies have shown that end-users having little knowledge of the features and possibilities of IoT devices, face difficulties in conjuring up meaningful use scenarios that combine such devices. Therefore, they fail to anticipate useful configurations beyond those provided by vendors and hence missing out on the vast potential of the IoT. We present an on-going investigation that explores the potential of sourcing IoT-relevant scenarios from a popular microtaskcrowdsourcing platform, and a preliminary evaluation of such scenarios with respect to their originality and practicality. This work paves the way for the automated leverage of crowdsourced user scenarios to support IoT end-users.
Dynamic Capabilities Trends: A brief review of the state of the art

Dynamic capabilities (DC) is a relatively young area, being a subject of interest and research of the recent year, hence its literature is mainly conceptual. In this article, we review briefly the current state-of-the-art, the literature over the past 10 years and how its related with competitive advantages and management. This article presents a brief literature review, proposing to guide future research, which will be conducted in a near future and work on a dynamic competency model which allows organizations to continuously develop these skills, aligned with the best models and to develop more effective strategies, which will lead to a continuously improvement.
Job scheduling strategies for parallel processing : IPPS '96 Workshop, Honolulu, Hawaii, April 16, 1996 : proceedings

Toward convergence in job schedulers for parallel supercomputers. - Workload evolution on the Cornell Theory Center IBM SP2. - The EASY - LoadLeveler API project. - A batch scheduler for the Intel Paragon with a non-contiguous node allocation algorithm. - Architecture-independent request-scheduling with tight waiting-time estimations. - Packing schemes for gang scheduling. - A gang scheduling design for multiprogrammed parallel computing environments. - Implementation of gang-scheduling on workstation cluster. - Managing checkpoints for parallel programs. - Using runtime measured workload characteristics in parallel processor scheduling. - Parallel application characterization for multiprocessor scheduling policy design. - Dynamic vs. static quantum-based parallel processor allocation. - Dynamic versus adaptive processor allocation policies for message passing parallel computers: An empirical comparison. - Dynamic partitioning in different distributed-memory environments. - Locality-information-based scheduling in shared-memory multiprocessors.
Hazard / Risk Assessment TOXICITY OF INDIVIDUAL PHARMACEUTICALS AND THEIR MIXTURES TO ALIIVIBRIO FISCHERI : EXPERIMENTAL RESULTS FOR SINGLE COMPOUNDS , CONSIDERATIONS OF THEIR MECHANISMS OF ACTION AND POTENTIAL ACUTE EFFECTS ON AQUATIC ORGANISMS

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A GA-Based Approach for Building Regularized Sparse Polynomial Models for Wind Turbine Power Curves

In this paper, the classical polynomial model for wind turbines power curve estimation is revisited aiming at an automatic and parsimonious design. In this regard, using genetic algorithms we introduce a methodoloy for estimating a suitable order for the polynomial as well its relevant terms. The proposed methodology is compared with the state of the art in estimating the power curve of wind turbines, such as logistic models (with 4 and 5 parameters), artificial neural networks and weighted polynomial regression. We also show that the proposed approach performs better than the standard LASSO approach for building regularized sparse models. The results indicate that the proposed methodology consistently outperforms all the evaluated alternative methods.
Individualization: Plant a Tree, Buy a Bike, Save the World?

An increasingly dominant, largely American response to the contemporary environmental crisis understands environmental degradation as the product of individual shortcomings and finds solutions in enlightened, uncoordinated consumer choice. Several forces promote this process of individualization, including the historical baggage of mainstream environmentalism, the core tenets of liberalism, the dynamic ability of capitalism to commodify dissent, and the relatively recent rise of global environmental threats to human prosperity. The result is to narrow our collective ability to imagine and pursue a variety of productive responses to the environmental problems before us. When responsibility for environmental problems is individualized, there is little room to ponder institutions, the nature and exercise of political power, or ways of collectively changing the distribution of power and influence in society. Confronting consumption requires individuals to understand themselves not primarily as consumers but rather as citizens in a participatory democracy, working together to change broader policy and larger social institutions. It also requires linking explorations of consumption to politically charged issues that challenge the political imagination.