Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural attentional filters and behavioural outcome follow independent individual trajectories over the adult lifespan

Preserved communication abilities promote healthy aging. To this end, the age-typical loss of sensory acuity might in part be compensated for by an individual’s preserved attentional neural filtering. Is such a compensatory brain–behaviour link longitudinally stable? Can it predict individual change in listening behaviour? We here show that individual listening behaviour and neural filtering ability follow largely independent developmental trajectories modelling electroencephalographic and behavioural data of N = 105 aging individuals (39–82 yrs). First, despite the expected decline in hearing- threshold–derived sensory acuity, listening-task performance proved stable over 2 years. Second, neural filtering and behaviour were correlated only within each separate measurement timepoint (T1, T2). Longitudinally, however, our results raise caution on attention-guided neural filtering metrics as predictors of individual trajectories in listening behaviour: Neither neural filtering at T1 nor its two-year change could predict individual two-year behavioural change, under a combination of modelling strategies.
An Atlas of the Developing Drosophila Visual System Glia and Subcellular mRNA Localization of Transcripts in Single Cells

Glial cells are essential for proper nervous system development and function. To understand glial development and function, we comprehensively annotated glial cells in a single-cell mRNA-sequencing (scRNAseq) atlas of the developing Drosophila visual system. This allowed us to study their developmental trajectories, from larval to adult stages, and to understand how specific types of glia diversify during development. For example, neuropil glia that are initially transcriptionally similar in larvae, split into ensheathing and astrocyte-like glia during pupal stages. Other glial types, such as chiasm glia change gradually during development without splitting into two cell types. The analysis of scRNA-seq allowed us to discover that the transcriptome of glial cell bodies can be distinguished from that of their broken processes. The processes contain distinct enriched mRNAs that were validated in vivo. Therefore, we have identified most glial types in the developing optic lobe and devised a computational approach to identify mRNA species that are localized to cell bodies or cellular processes.
Somatosensory cortex microstimulation modulates primary motor and premotor cortex neurons with extensive spatial convergence and divergence

Intracortical microstimulation (ICMS) is known to affect distant neurons transynaptically, yet the extent to which ICMS pulses delivered in one cortical area modulate neurons in other cortical areas remains largely unknown. Here we assessed how the individual pulses of multi-channel ICMS trains delivered in the upper extremity representation of the macaque primary somatosensory area (S1) modulate neuron firing in the primary motor cortex (M1) and in the ventral premotor cortex (PMv). S1-ICMS pulses modulated the majority of units recorded both in the M1 upper extremity representation and in PMv, producing more inhibition than excitation. Effects converged on individual neurons in both M1 and PMv from extensive S1 territories. Conversely, effects of ICMS delivered in a small region of S1 diverged to wide territories in both M1 and PMv. The effects of this direct modulation of M1 and PMv neurons produced by multi-electrode S1-ICMS like that used here may need to be taken into account by bidirectional brain-computer interfaces that decode intended movements from neural activity in these cortical motor areas. Significance Statement Although ICMS is known to produce effects transynaptically, relatively little is known about how ICMS in one cortical area affects neurons in other cortical areas. We show that the effects of multi-channel ICMS in a small patch of S1 diverge to affect neurons distributed widely in both M1 and PMv, and conversely, individual neurons in each of these areas can be affected by ICMS converging from much of the S1 upper extremity representation. Such direct effects of ICMS may complicate the decoding of motor intent from M1 or PMv when artificial sensation is delivered via S1-ICMS in bidirectional brain-computer interfaces.
Detection and characterization of a novel bat filovirus (Dehong virus, DEHV) in fruit bats

Although bats are natural hosts of most filoviruses (FiVs), with the pathogenic ones frequently causing deadly haemorrhagic fevers in Africa, the viruses are difficult to trace. Recently, multiple divergent FiVs have been uncovered in China, raising concerns about their threat to public health. Here we report the detection of bat-borne FiVs at the interface of orchards between bats and humans, eventually resulting in the discovery of a novel FiV, Dehong virus (DEHV), from Rousettus leschenaultii (R. leschenaultii) bats. DEHV has the largest genome, and the phylogeny places it between the genera Dianlovirus and Marburgvirus, suggesting its classification as a prototype of a new genus within the family Filoviridae. Our study emphasizes the importance of further understanding the distribution and potential risk of FiVs in the region.
Individual-specific memory reinstatement patterns within human face-selective cortex

Humans have the remarkable ability to vividly retrieve sensory details of past events. According to the theory of sensory reinstatement, during remembering, brain regions involved in the sensory processing of prior events are reactivated to support this perception of the past. Recently, several studies have emphasized potential transformations in the spatial organization of reinstated activity patterns. In particular, studies of scene stimuli suggest a clear anterior shift in the location of retrieval activations compared with those during perception. However, it is not clear that such transformations occur universally, with evidence lacking for other important stimulus categories, particularly faces. Critical to addressing these questions, and to studies of reinstatement more broadly, is the growing importance of considering meaningful variations in the organization of sensory systems across individuals. Therefore, we conducted a multi-session neuroimaging study to first carefully map individual participants face-selective regions within ventral temporal cortex (VTC), followed by a second session to examine the correspondence of activity patterns during face memory encoding and retrieval. Our results showed distinct configurations of face-selective regions within the VTC across individuals. While a significant degree of overlap was observed between face perception and memory encoding, memory retrieval engagement exhibited a more selective and constricted reinstatement pattern within these regions. Importantly, these activity patterns were consistently tied to individual-specific neural substrates, but did not show any consistent direction of spatial transformation (e.g., anteriorization). To provide further insight to these findings, we also report on unique human intracranial recordings from VTC under the same experimental conditions. Our findings highlight the importance of considering individual variations in functional neuroanatomy in the context of assessing the nature of cortical reinstatement. Consideration of such factors will be important for establishing general principles shaping the neural transformations that occur from perception to memory.
This paper is included in the Proceedings of the Nineteenth Symposium on Usable Privacy and Security.

This paper presents the results of an online survey experiment with 746 participants that investigated whether social norms inﬂuence people’s choices about using technologies that can infer information they might not want to disclose. The results show both correlational and causal evidence that empirical expectations (beliefs about what others do) and normative expectations (beliefs about what others believe) inﬂuence choices to use mobile devices in ways that generate data that could be used to make sensitive inferences. However, participants also reported concern about data privacy, and lower behavioral intentions for vignettes involving more invasive inferences. Pluralistic ignorance is a phenomenon where individuals behave in ways they privately disagree with, because they see others around them behaving the same way and assume this is evidence most people approve of the behavior. These results are consistent with the existence of pluralistic ig-norance related to data privacy, and suggest that interventions focused on transparency about data practices are not enough to encourage people to make different privacy choices.
"Would You Give the Same Priority to the Bank and a Game? I Do Not!" Exploring Credential Management Strategies and Obstacles during Password Manager Setup

Password managers allow users to improve password security by handling large numbers of strong and unique pass-words without the burden of memorizing them. While users are encouraged to add all credentials to their password manager and update weak credentials, this task can require significant effort and thus jeopardize security benefits if not completed thoroughly. However, user strategies to add cre-dentials, related obstacles, and their security implications are not well understood. To address this gap in security research, we performed a mixed-methods study, including expert reviews of 14 popular password managers and an online survey with 279 users of built-in and third-party password managers. We extend previous work by examining the status quo of password manager setup features and investigating password manager users’ setup strategies. We confirm previous research and find that many participants utilize password managers for convenience, not as a security tool. They most commonly add credentials whenever a website is visited, and prioritize what they add. Similarly, passwords are often only updated when they are considered insecure. Additionally, we observe a severe distrust towards password managers, leading to users not adding important passwords. We conclude our work by giving recommendations for password manager developers to help users overcome the obstacles we identified.
Model-based Methodologies for Pervasive and Embedded Software

Model-based Methodologies for Pervasive and Embedded Software (MOMPES 2009) is the 6th edition of a workshop series. The workshops focus on the theoretical and practical aspects related to the adoption of model-based development methodologies for supporting the construction of software for pervasive and embedded systems. The workshops usually gather researchers from both industry and academia.
Microbial perspectives of the methane cycle in permafrost ecosystems in the eastern Siberian Arctic: Implications for the global methane budget

8 Zusammenfassung. 10 1 General Introduction. 12 1.1 Significance of Permafrost Ecosystems for the Global Methane Budget. 12 1.2 Astrobiological Aspects of Permafrost Environments. 15 1.3 Objectives and Research Strategy. 16 1.4 Study Area. 18 1.5 Expeditions. 21 2 Overview of the Publications. 24 3 Methane Release from Siberian Tundra Environments. 29 3.1 Methane Emission from Siberian Arctic Polygonal Tundra: Eddy Covariance Measurements and Modeling. 29 3.2 Effect of Microrelief and Vegetation on Methane Emission from wet Polygonal Tundra, Lena Delta, Northern Siberia. 44 3.3 Land Cover Classification of Tundra Environments in the Arctic Lena Delta Based on Landsat 7 ETM+ Data and its Application for Upscaling of Methane Emissions. 63 4 Permafrost Ecosystems and Their Microbial Processes. 77 4.1 Element Redistribution Along Hydraulic and Redox Gradients of Low-Centered Polygons, Lena Delta, Northern Siberia. 77 4.2 Microbial Controls on Methane Fluxes from a Polygonal Tundra of the Lena Delta, Siberia. 85 4.3 Abundance, Distribution and Potential Activity of Methane Oxidizing Bacteria in Permafrost Soils from the Lena Delta, Siberia. 96 4.4 Methanogenic Activity and Biomass in Holocene Permafrost Deposits of the Lena Delta, Siberian Arctic and its Implication for the Global Methane Budget. 105 Methane Cycle in Permafrost Ecosystems
Obfuscated Mobile Malware Detection by Means of Dynamic Analysis and Explainable Deep Learning

With the growth of the mobile market, malicious applications represent a risk to the security of the users. To mitigate this aspect, researchers proposed different techniques to spot and identify unsafe software placed on the market. On the other hand, malicious writers started to develop ever more sophisticated strategies to hide malicious payloads, in particular through the adoption of obfuscation techniques. The latter consists of hiding the behavior and purpose of malware from antimalware. In this paper, we propose and design a method aimed to detect obfuscated malware. The proposed method builds images directly from system call traces obtained from legitimate, malicious, and obfuscated Android applications. In addition, to show that dynamic analysis and deep learning can build resilient models we propose two experiments using a convolutional neural network. In the first experiment, we train and test the model using a dataset composed of malware, while in the second we train the model using the malware dataset but the model is evaluated using a dataset composed of obfuscated malware. Finally, we analyze the malware and obfuscated detection models from the point of view of explainability using two different class activation mapping algorithms, to understand whether the model predictions can be considered resilient.
Open Government Data: Towards Empirical Analysis of Open Government Data Initiatives

Open Government Data (OGD) initiatives, and in particular the development of OGD portals, have proliferated since the mid-2000s both at central and local government levels in OECD and non OECD countries. Understanding the preconditions that enable the efficient and effective implementation of these initiatives is essential for achieving their overall objectives. This is especially true in terms of the role played by OGD in relation to Open Government policies in general. This paper highlights the main principles, concepts and criteria framing open government data initiatives and the issues challenging their implementation. It underlines the opportunities that OGD and data analytics may offer policy makers, while providing a note of caution on the challenges this agenda poses for the public sector. Finally, the overall analysis of key concepts and issues aims to pave the way for an empirical analysis of OGD initiatives. So far, little has been done to analyse and prove the impact and accrued value of these initiatives. The paper suggests a methodology comprising an analytical framework for OGD initiatives (to be applied to ex post and ex ante analysis of initiatives) and a related set of data to be collected across OECD countries. The application of the analytical framework and the collection of data would enable the acquisition of a solid body of evidence that could ultimately lead to mapping initiatives across OECD countries (i.e. a typography of initiatives) and developing a common set of metrics to consistently assess impact and value creation within and across countries.
Differentially Private Distributed Online Learning

In the big data era, the generation of data presents some new characteristics, including wide distribution, high velocity, high dimensionality, and privacy concern. To address these challenges for big data analytics, we develop a privacy-preserving distributed online learning framework on the data collected from distributed data sources. Specifically, each node (i.e., data source) has the capacity of learning a model from its local dataset, and exchanges intermediate parameters with a random part of their own neighboring (logically connected) nodes. Hence, the topology of the communications in our distributed computing framework is unfixed in practice. As online learning always performs on the sensitive data, we introduce the notion of differential privacy (DP) into our distributed online learning algorithm (DOLA) to protect the data privacy during the learning, which prevents an adversary from inferring any significant sensitive information. Our model is of general value for big data analytics in the distributed setting, because it can provide rigorous and scalable privacy proof and have much less computational complexity when compared to classic schemes, e.g., secure multiparty computation (SMC). To tackle high-dimensional incoming data entries, we study a sparse version of the DOLA with novel DP techniques to save the computing resources and improve the utility. Furthermore, we present two modified private DOLAs to meet the need of practical applications. One is to convert the DOLA to distributed stochastic optimization in an offline setting, the other is to use the mini-batches approach to reduce the amount of the perturbation noise and improve the utility. We conduct experiments on real datasets in a configured distributed platform. Numerical experiment results validate the feasibility of our private DOLAs.
A Dynamic Pre x Tree for Service Discovery within Large Scale Grids

Within computational grids, some services (software components, linear algebra libraries, etc.) are made available by some servers to some clients. In spite of the growing popularity of such grids, the service discovery, although e cient in many cases, does not reach several requirements. Among them, the exibility of the discovery and its e ciency on wide-area dynamic platforms are two major issues. Therefore, it becomes crucial to propose new tools coping with such platforms. Emerging peer-to-peer technologies provide algorithms allowing the distribution and the retrieval of data items while addressing the dynamicity of the underlying network. We study in this paper the service discovery in a pure peer-to-peer environment. We describe a new trie-based approach for the service discovery that supports range queries and automatic completion of partial search strings, while providing faulttolerance, and partially taking into account the topology of the underlying network. We validate this approach both by analysis and simulation. Traditional metrics considered in peer-to-peer systems exhibits interesting complexities within our architecture. The analysis' results are con rmed by some simulation experiments run using several grid's data sets. Key-words: Service discovery, computational grids, peer-to-peer, pre x trees This text is also available as a research report of the Laboratoire de l'Informatique du Parallélisme http://www.ens-lyon.fr/LIP. in ria -0 01 16 11 1, v er si on 4 28 N ov 2 00 6 Un Arbre de Pré xe Dynamique pour la Découverte de Service dans les Grilles de Calcul Large Échelle Résumé : Dans les grilles des serveurs o rent des services aux clients a n de réaliser des calculs. Avant de pouvoir les utiliser, les clients doivent être à même de les retrouver. Bien que les di érentes solutions proposées depuis l'émergence des grilles soient e caces sur des plates-formes relativement statiques et de petite échelle, elles ne sont plus en adéquation avec la nature dynamique et à large échelle des grilles futures. Pour de tels environnements de nouveaux outils doivent être proposés, notamment des mécanismes pour la découverte de services, qui devront être exibles et passer à l'échelle dans des environnements dynamiques. Nous étudions dans ce papier la découverte de services pour des grilles de calcul pair-à-pair. Nous proposons une nouvelle architecture basée sur un arbre de plus long pré xes. Mots-clés : Découverte de services, grilles de calcul, pair-à-pair, arbres de pré xes in ria -0 01 16 11 1, v er si on 4 28 N ov 2 00 6 A Dynamic Pre x Tree for Service Discovery within Large Scale Grids 3
The Egyptian Cardiothoracic Surgeon

Background: Internal thoracic artery became the cornerstone graft in coronary artery bypass grafting. This study aimed to investigate sternal healing and wound infection in patients undergoing coronary artery bypass graft (CABG) surgery using skeletonized versus pedicled internal thoracic artery. Methods: 100 patients who underwent isolated CABG were divided into two groups; skeletonized internal thoracic artery (ITA) (50 patients) and pedicled internal thoracic artery (50 patients). The postoperative assessment was performed three months after surgery. Physical and radiological examinations were performed after surgery to assess sternal healing. Results: There was no significant difference in patients’ demographics between groups. Skeletonized group had more diabetic patients (65% vs 44%; p= 0.016). There was no significant difference between the two groups regarding the number of grafts (2.72 ± 0.89 vs. 2.68 ± 0.90; skeletonized vs pedicled group; p= 0.84). Harvesting time was longer in the skeletonized group (55.69 ± 8.80 vs. 44.28 ± 6.95 minutes; p=0.09). Superficial wound infection occurred more frequently in the pedicled group (24% vs. 8%; p=0.03). Conclusion: Skeletonization of the internal thoracic artery conduits lowers the risk of superficial and deep sternal wound infection in patients undergoing CABG compared to the pedicled technique. However, skeletonization of internal thoracic artery conduits prolongs the operation time and requires more surgical skills.
Stanford’s Multi-Pass Sieve Coreference Resolution System at the CoNLL-2011 Shared Task

This paper details the coreference resolution system submitted by Stanford at the CoNLL-2011 shared task. Our system is a collection of deterministic coreference resolution models that incorporate lexical, syntactic, semantic, and discourse information. All these models use global document-level information by sharing mention attributes, such as gender and number, across mentions in the same cluster. We participated in both the open and closed tracks and submitted results using both predicted and gold mentions. Our system was ranked ﬁrst in both tracks, with a score of 57.8 in the closed track and 58.3 in the open track.