Miracle-dz/UniFAR-FTUs huggingface dataset:
Linking cognitive integrity to working memory dynamics in the aging human brain

Aging is accompanied by a decline of multiple cognitive capacities, including working memory: the ability to maintain information online for the flexible control of behavior. Working memory involves stimulus-selective neural activity, persisting after stimulus presentation in widely distributed cortical areas. Here, we unraveled the mechanisms of working memory in healthy older adults and patients with mild cognitive impairment (MCI), a condition associated with increased risk of developing dementia. We studied a sample of 19 older adults diagnosed with MCI and 20 older healthy controls using a combination of model-based behavioral psychophysics, neuropsychological assessment, and magnetoencephalographic (MEG) recordings of brain activity. Twenty-one younger healthy adults were studied with model-based behavioral psychophysics only. All subjects performed a visuo-spatial delayed-match-to-sample working memory task under systematic manipulation of the temporal delay and the spatial distance between successively presented sample and test stimuli. We developed a computational model of the latent dynamics underlying task behavior and fit this to individual behavior. In the model, working memory representations diffused over time, a threshold was applied to produce a match/non-match decision about sample and test locations, and occasional lapses produced random decisions. For the older participants, we related the individual model parameters to a summary measure of individual cognitive integrity obtained from a large neuropsychological test battery, as well as to cortical MEG activity during the delay interval of the working memory task. For all groups, task accuracy decreased with delay duration and sample-test distance. When sample/test distances were small, older adults exhibited larger false alarm rates than younger adults. The behavioral effects were well captured by the model, which explained the age-related differences in terms of a deterioration of the quality of working memory representations, rather than differences in task strategy (i.e., threshold parameter). Task accuracy as well as the parameters governing behavioral stochasticity (diffusion noise and lapse rate combined) were correlated with overall cognitive integrity in the MCI group, but not in the older healthy controls. Individual task accuracy and stochasticity parameters (diffusion noise and lapse rate) were also correlated with stimulus-selective cortical activity during the delay interval, as assessed by decoding of the MEG signals, corroborating their validity as markers of cortical working memory mechanisms. Our findings provide insight into the mechanistic basis of aging-related changes in working memory maintenance and reveal a link between individual working memory dynamics and cognitive integrity in MCI.
Designed Endocytosis-Triggering Proteins mediate Targeted Degradation

Endocytosis and lysosomal trafficking of cell surface receptors can be triggered by interaction with endogenous ligands. Therapeutic approaches such as LYTAC1,2 and KineTAC3, have taken advantage of this to target specific proteins for degradation by fusing modified native ligands to target binding proteins. While powerful, these approaches can be limited by possible competition with the endogenous ligand(s), the requirement in some cases for chemical modification that limits genetic encodability and can complicate manufacturing, and more generally, there may not be natural ligands which stimulate endocytosis through a given receptor. Here we describe general protein design approaches for designing endocytosis triggering binding proteins (EndoTags) that overcome these challenges. We present EndoTags for the IGF-2R, ASGPR, Sortillin, and Transferrin receptors, and show that fusing these tags to proteins which bind to soluble or transmembrane protein leads to lysosomal trafficking and target degradation; as these receptors have different tissue distributions, the different EndoTags could enable targeting of degradation to different tissues. The modularity and genetic encodability of EndoTags enables AND gate control for higher specificity targeted degradation, and the localized secretion of degraders from engineered cells. The tunability and modularity of our genetically encodable EndoTags should contribute to deciphering the relationship between receptor engagement and cellular trafficking, and they have considerable therapeutic potential as targeted degradation inducers, signaling activators for endocytosis-dependent pathways, and cellular uptake inducers for targeted antibody drug and RNA conjugates.
Real-time GNSS spoofing detection in maritime code receivers

The paper presents an overview of methods for detecting the spoofing of GNSS open service code signals illustrated with the example of C/A GPS signals. GNSS signal spoofing is an attack method where a signal is transmitted that appears authentic but it induces the receiver under attack to compute an erroneous navigation solution, time, or both. Usage of commercially available satellite compasses and two antennas systems for the detection of such threat is described in detail
Artificial Intelligence Techniques Applied To Intrusion Detection

Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, that uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly, misuse and host based detection. Simple Fuzzy rules allow us to construct if-then rules that reflect common ways of describing security attacks. For host based intrusion detection we use neural-networks along with self organizing maps. Suspicious intrusions can be traced back to its original source path and any traffic from that particular source will be redirected back to them in future.
Adaptive Rigidification of Discrete Shells

We present a method to improve the computation time of thin shell simulations by using adaptive rigidification to reduce the number of degrees of freedom. Our method uses a discretization independent metric for bending rates, and we derive a membrane strain rate to curvature rate equivalence that permits the use of a common threshold. To improve accuracy, we enhance the elastification oracle by considering both membrane and bending deformation to determine when to rigidify or elastify. Furthermore, we explore different approaches that are compatible with the previous work on adaptive rigidifcation and enhance the accuracy of the elastification on new contacts without increasing the computational overhead. Additionally, we propose a scaling approach that reduces the conditioning issues that arise from mixing rigid and elastic bodies in the same model.
A Linear and Angular Momentum Conserving Hybrid Particle/Grid Iteration for Volumetric Elastic Contact

We present a momentum conserving hybrid particle/grid iteration for resolving volumetric elastic collision. Our hybrid method uses implicit time stepping with a Lagrangian finite element discretization of the volumetric elastic material together with impulse-based collision-correcting momentum updates designed to exactly conserve linear and angular momentum. We use a two-step process for collisions: first we use a novel grid-based approach that leverages the favorable collision resolution properties of Particle-In-Cell (PIC) techniques, then we finalize with a classical collision impulse strategy utilizing continuous collision detection. Our PIC approach uses Affine-Particle-In-Cell momentum transfers as collision preventing impulses together with novel perfectly momentum conserving boundary resampling and downsampling operators that prevent artifacts in portions of the boundary where the grid resolution is of disparate resolution. We combine this with a momentum conserving augury iteration to remove numerical cohesion and model sliding friction. Our collision strategy has the same continuous collision detection as traditional approaches, however our hybrid particle/grid iteration drastically reduces the number of iterations required. Lastly, we develop a novel symmetric positive semi-definite Rayleigh damping model that increases the convexity of the nonlinear systems associated with implicit time stepping. We demonstrate the robustness and efficiency of our approach in a number of collision intensive examples.
Transfer Learning across Graph Convolutional Networks: Methods, Theory, and Applications

Graph neural networks have been widely used for learning representations of nodes for many downstream tasks on graph data. Existing models were designed for the nodes on a single graph, which would not be able to utilize information across multiple graphs. The real world does have multiple graphs where the nodes are often partially aligned. For examples, knowledge graphs share a number of named entities though they may have different relation schema; collaboration networks on publications and awarded projects share some researcher nodes who are authors and investigators, respectively; people use multiple web services, shopping, tweeting, rating movies, and some may register the same e-mail account across the platforms. In this article, we propose partially aligned graph convolutional networks to learn node representations across the models. We provide multiple methods such as model sharing, regularization, and alignment reconstruction, as well as theoretical analysis to positively transfer knowledge across the set of partially aligned nodes. Extensive experiments on real-world knowledge graphs, collaboration networks, and bipartite rating graphs show the superior performance of our proposed methods on relation classification, link prediction, and item recommendation.
A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics

We present a generalized constitutive model for versatile physics simulation of inviscid fluids, Newtonian viscosity, hyperelasticity, viscoplasticity, elastoplasticity, and other physical effects that arise due to a mixture of these behaviors. The key ideas behind our formulation are the design of a generalized Kirchhoff stress tensor that can describe hyperelasticity, Newtonian viscosity and inviscid fluids, and the use of pre-projection and post-correction rules for simulating material behaviors that involve plasticity, including elastoplasticity and viscoplasticity. We show how our generalized Kirchhoff stress tensor can be coupled together into a generalized constitutive model that allows the simulation of diverse material behaviors by only changing parameter values. We present several side-by-side comparisons with physics simulations for specific constitutive models to show that our generalized model produces visually similar results. More notably, our formulation allows for inverse learning of unknown material properties directly from data using differentiable physics simulations. We present several 3D simulations to highlight the robustness of our method, even with multiple different materials. To the best of our knowledge, our approach is the first to recover the knowledge of unknown material properties without making explicit assumptions about the data.
NATIONAL TRAVEL SURVEY 1977-78: STATISTICAL ADJUSTMENTS AND FINAL RESULTS

Over the period July 1977 to June 1978, the Bureau of Transport Economics (BTE) carried out a general survey of non-urban travel in Australia. This survey was known as the National Travel Survey (NTS). The NTS was a postal survey eliciting voluntary responses from households throughout Australia. Such surveys have three primary sources of error- sampling bias, response errors and non-response bias. This paper examines all of these sources of error in the context of the NTS. Some of the more important and interesting results derived from the NTS are presented in this paper. The estimates given in the tables presented are estimates for the whole population, after adjustment for non-response bias. Some discussion and interpretation of the tables in this paper is included. During the course of the NTS it became apparent that, despite careful planning, there were several areas in the design of the NTS which resulted in some problems in processing and analysing the final results. These deficiencies in the NTS design are outlined in the paper to assist the design of any future survey along the same lines. Although it is by no means clear that all of these deficiencies could have been overcome, perhaps the main outcome of this review of the survey design was desirability of adequate pilot- testing of the complete operational approach to a survey, including not only the questionnaire format but the coding and administrative process as well. (TRRL)
Climate Change 2022 – Impacts, Adaptation and Vulnerability

The Working Group II contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) provides a comprehensive assessment of the scientific literature relevant to climate change impacts, adaptation and vulnerability. The report recognizes the interactions of climate, ecosystems and biodiversity, and human societies, and integrates across the natural, ecological, social and economic sciences. It emphasizes how efforts in adaptation and in reducing greenhouse gas emissions can come together in a process called climate resilient development, which enables a liveable future for biodiversity and humankind. The IPCC is the leading body for assessing climate change science. IPCC reports are produced in comprehensive, objective and transparent ways, ensuring they reflect the full range of views in the scientific literature. Novel elements include focused topical assessments, and an atlas presenting observed climate change impacts and future risks from global to regional scales. Available as Open Access on Cambridge Core.
Virtual Machines

Virtual machine monitors partition a single physical machine into multiple virtual ones. This can be useful for several important applications, such as running multiple isolated servers on a single machine, testing and debugging software, using possibly malicious software and building honeypots. The widely used IA-32 architecture does not natively support virtualisation. We compare several state-of-the-art methods used to circumvent this problem. Our comparison includes dynamic binary translation, paravirtualisation, previrtualisation, operating system level partitioning, application virtual machines and the recent “Virtual Machine Extensions” added to IA-32 by Intel. Our criteria for this comparison include performance, robustness, relationship with the host operating system and portability. We use the results of the comparison to determine which techniques are most suitable for which applications. We find that the new hardware virtualisation support looks very promising for many applications, but may not completely replace other methods.
The Carbon Cycle: Carbon Dioxide Emissions from Fossil Fuel Consumption and Cement Manufacture, 1751–1991, and an Estimate of Their Isotopic Composition and Latitudinal Distribution

This work briefly discusses four of the current research emphases at Oak Ridge National Laboratory regarding the emission of carbon dioxide (CO{sub 2}) from fossil fuel consumption, natural gas flaring and cement manufacture. These emphases include: (1) updating the 1950 to present time series of CO{sub 2} emissions from fossil fuel consumption and cement manufacture, (2) extending this time series back to 1751, (3) gridding the data at 1{sup 0} by 1{sup 0} resolution, and (4) estimating the isotopic signature of these emissions. In 1991, global emissions of CO{sub 2} from fossil fuel and cement increased 1.5% over 1990 levels to 6188 {times} 10{sup 6} metric tonnes C. The Kuwaiti oil fires can account for all of the increase. Recently published energy data (Etemad et al., 1991) allow extension of the CO emissions time series back to 1751. Preliminary examination shows good agreement with two other, but shorter, energy time series. A latitudinal distribution of carbon emissions is being completed. A southward shift in the major mass of CO{sub 2} emissions is occurring from European-North American latitudes towards central-southeast Asian latitudes, reflecting the growth of population and industrialization at these lower latitudes. The carbon isotopic signature of these emissions hasmore » been re-examined. The emissions of the last two decades are approximately 1{per_thousand} lighter than previously reported (Tans, 1981). This lightening of the emissions signature is due to fossil fuel gases and liquids, including a revision of their {delta}{sup 13}C isotopic signature and an increased production rate. « less
Detecting APT through graph anomaly detection

—Despite fruitful achievements made by unsupervised machine learning-based anomaly detection for network intrusion detection systems, they are still prone to the issue of high false alarm rates, and it is still difﬁcult to reach very high recalls. In 2020, Leichtnam et al. proposed Sec2graph, an unsupervised approach applied to security objects graphs that exhibited interesting results on single-step attacks. The graph representation and the embedding allowed for better detection since it creates qualitative features. In this paper, we present new experiments to assess the performances of this approach for detecting APT attacks. We achieve better detection performances than the original work’s baseline detection methods on the DAPT2020 dataset. This work is realised in the context of the Ph.D. thesis of Maxime Lanvin, which started in October 2021.
Detecting and disrupting criminal networks

Background: criminal networks are essentially defined as sets of actors that are connected by one or more ties. While many empirical studies of criminal networks have focused on the properties of actors (i.e. key-players), less attention is paid to the importance of ties in terms of tie-strength and its influence on emerging criminal networks. Aim: The aim of this study is to empirically study the associations between the structural, temporal and demographical dimensions of the strength of ties within organized criminal networks Method: We use a combination of social network-, statistical- and qualitative analysis to study the associations between three dimensions of tie-strength: the structural-, temporal-and demographical- dimension. Network-data: A criminal meso-network consisting of N= 5178 actors Results and conclusions: Our findings suggest that weak ties are important for the exchange of information particularly at a meso-level, but not exclusively due to a small-world effect. The majority of these ties are fluid, instrumental in nature, and often based on trust in a third party instead of between actors at both ends. ‘Freelance’ co-operation emerges out of the fluid pools of potential co-offenders. The strong ties in the network rely heavily on kinship and attachment to local neighborhoods. Different clusters of strong ties are associated with an overarching ‘old-boys’ network’ that remains more durable over time and forms a sustainable pool of criminal cooperation within the overall fluid criminal network.
2022 Nonassociative black holes in R-ﬂux deformed phase spaces and relativistic models of G. Perelman thermodynamics

This paper systematically explores new classes of black hole (BH) solutions in nonassociative and noncommutative gravity by focusing on features that generalize to higher dimensions. The theories we study are modelled on (co) tangent Lorentz bundles ( i.e. eight dimensional phase spaces) with a star product structure determined by R-ﬂux deformations in string theory. The nonassociative vacuum Einstein equations involve real and complex eﬀective sources with coeﬃcients which are proportional to the Planck and string constants or their products. We develop the anholonomic frame and connection deformation method, and prove that such systems of nonlinear partial diﬀerential equations can be decoupled and integrated in general form for a generic oﬀ-diagonal ansatz for symmetric and non-symmetric metrics as well as for generalized (non) linear connection structures. The coeﬃcients of such metrics may depend on all phase space coordinates (space-time coordinates plus energy-momentum). Conditions are given when the generating and integration functions and eﬀective sources deﬁne two classes of physically important exact and parametric solutions with R-ﬂux sources related via nonlinear symmetries to eﬀective cosmological constants: (1) 6D Tangherlini BHs, which are star product and R-ﬂux distorted to quasi-stationary conﬁgurations and 8D black ellipsoids (Bes) and BHs; (2) nonassocitative space-time and co-ﬁber space double BH and/or BE conﬁgurations generalizing Schwarzschild - de Sitter metrics. We argue that the concept of Bekenstein-Hawking entropy is applicable only for very special classes of nonassociative BHs encoding conventional horizons and (anti) de Sitter conﬁgurations. Finally, we show how analogs of the relativistic G. Perelman W-entropy and related geometric thermodynamic variables can be deﬁned and computed for general classes of oﬀ-diagonal solutions nonassociative R-ﬂux deformations.
ACMFNN: Design of an augmented convolutional model for intelligent cross-domain malware localization via forensic neural networks

: Classification of malwares from spatial & temporal data patterns requires efficient design of deep learning models. These models deploy methods for feature extraction, feature selection, classification & post-processing to perform this task. A wide variety of high-efficiency malware analysis models are proposed by researchers, and most of them are application-specific, thus cannot be scaled to multiple domains. Out of these, only a few of these models are targeted towards identification of malware locations. In order to improve malware detection scalability, and localization performance, this text proposes a novel augmented convolutional model (ACM) for intelligent cross-domain malware analysis via forensic neural networks (FNNs). The FNNs are designed as an integration of multiple augmented convolutional models, which include different optimizers & feature extraction units. In this design, each of these units are customized to improve their feature extraction & selection capabilities, which assists in improving classification performance. Results of classification are given to an ACM layer, which performs feature augmentation to localize malware positions in input data. The proposed model was evaluated on multiple malware datasets, including Electro RAT, Pegasus, SkyGoFree, Viking Horde, Bat Skull, Yesmile, Wirenet, Jigsaw, Satana, Tapaoux, etc. It was observed that the proposed model was able to classify these malwares with an average accuracy of 98.5%, which makes it useful for real-time malware analysis. The model was also able to achieve an average localization accuracy of 79.6% across these datasets, thereby assisting forensic experts to obtain an approximate estimate of malware locations in input data streams. This performance was compared with some of the recently proposed malware detection models, and it was observed that the proposed ACMFNN method has 8% better precision, 6.5% better recall, and 9.4% better classification accuracy when compared with these methods on the same dataset. Due to augmented convolutional model, it was observed that the proposed approach had 15% better localization accuracy, 19% better localization precision, and 14% better localization recall when compared with these methods. Thereby indicating that the propose model is applicable for a wide variety of malware detection & localization application deployments.