Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Parametric Bayesian State Space Estimator for Negative Information

Simultaneous Localisation and Mapping (SLAM) is concerned with the development of filters to accurately and efficiently infer the state parameters (position, orientation,.) of an agent and aspects of its environment, commonly referred to as the map. A mapping system is necessary for the agent to achieve situatedness which is a precondition for planning and reasoning. In this work, we consider an agent who is given the task of finding a set of objects. The agent has limited perception and can only sense the presence of objects if a direct contact is made, as a result most of the sensing is negative information. In the absence of recurrent sightings or direct measurements of objects, there are no correlations from the measurement errors which can be exploited. This renders SLAM estimators, for which this fact is their backbone such as EKF-SLAM, ineffective. In addition for our setting, no assumptions are taken with respect to the marginals (beliefs) of both the agent and objects (map). From the loose assumptions we stipulate regarding the marginals and measurements, we adopt a histogram parametrisation. We introduce a Bayesian State Space Estimator (BSSE), which we name Measurement Likelihood Memory Filter (MLMF), in which the values of the joint distribution are not parametrised but instead we directly apply changes from the measurement integration step to the marginals. This is achieved by keeping track of the history of likelihood functions’ parameters. We demonstrate that the MLMF gives the same filtered marginals as a histogram filter and show two implementations: MLMF and scalable-MLMF which both have a linear space complexity. The original MLMF retains an exponential time complexity (although an order of magnitude smaller than the histogram filter) whilst the scalable-MLMF introduced independence assumption such to have a linear time complexity. We further quantitatively demonstrate the scalability of our algorithm with 25 beliefs having up to 10’000’000 states each. In an Active-SLAM setting we evaluate the impact that the size of the memory’s history has on the decision-theoretic process in a search scenario for a one-step look-ahead information gain planner. We report on both 1D and 2D experiments.
LRRK2 BAC transgenic rats develop progressive, L-DOPA-responsive motor impairment, and deficits in dopamine circuit function

Mutations in leucine-rich repeat kinase 2 (LRRK2) lead to late-onset, autosomal dominant Parkinson's disease, characterized by the degeneration of dopamine neurons of the substantia nigra pars compacta, a deficit in dopamine neurotransmission and the development of motor and non-motor symptoms. The most prevalent Parkinson's disease LRRK2 mutations are located in the kinase (G2019S) and GTPase (R1441C) encoding domains of LRRK2. To better understand the sequence of events that lead to progressive neurophysiological deficits in vulnerable neurons and circuits in Parkinson's disease, we have generated LRRK2 bacterial artificial chromosome transgenic rats expressing either G2019S or R1441C mutant, or wild-type LRRK2, from the complete human LRRK2 genomic locus, including endogenous promoter and regulatory regions. Aged (18–21 months) G2019S and R1441C mutant transgenic rats exhibit L-DOPA-responsive motor dysfunction, impaired striatal dopamine release as determined by fast-scan cyclic voltammetry, and cognitive deficits. In addition, in vivo recordings of identified substantia nigra pars compacta dopamine neurons in R1441C LRRK2 transgenic rats reveal an age-dependent reduction in burst firing, which likely results in further reductions to striatal dopamine release. These alterations to dopamine circuit function occur in the absence of neurodegeneration or abnormal protein accumulation within the substantia nigra pars compacta, suggesting that nigrostriatal dopamine dysfunction precedes detectable protein aggregation and cell death in the development of Parkinson's disease. In conclusion, our longitudinal deep-phenotyping provides novel insights into how the genetic burden arising from human mutant LRRK2 manifests as early pathophysiological changes to dopamine circuit function and highlights a potential model for testing Parkinson's therapeutics.
Isolation and characterisation of novel polymorphic microsatellite loci in Iberian painted frogs (Discoglossus galganoi and D. jeanneae), with data on cross-species amplification in Discoglossus and Latonia (Alytidae)

Mediterranean painted frogs (genus Discoglossus Otth, 1837) are distributed across western Europe, North Africa and some Mediterranean islands. Previous studies have focused on their phylogenetic relationships, but the taxonomic position of the Iberian taxa (D. galganoi and D. jeanneae) is still under debate. By using microsatellites, patterns and rates of admixture can be quantified. We report the characterisation of eighteen novel polymorphic microsatellite loci in Iberian painted frogs. These loci were also tested in all other species of Discoglossus and in the recently rediscovered and highly endangered relative Latonia nigriventer. Two to eleven loci amplified in these species, and the number of polymorphic loci ranged from zero (in Latonia) to eight (in D. scovazzi). The new markers will be useful in addressing questions related to the evolutionary history, population structure, and conservation of Iberian Discoglossus. They also have potential for use in the North African species D. scovazzi and D. pictus, the latter of which is an invasive species rapidly expanding its range in southeast France and northeast Spain.
Effects of lateral bone augmentation procedures on peri‐implant health or disease: A systematic review and meta‐analysis

OBJECTIVES This systematic review evaluated the evidence on the effect of the interventions aimed for lateral ridge augmentation (both simultaneously with implant placement or as a staged procedure) on peri-implant health or disease. METHODS A protocol was developed to answer the following PICO question: "In patients with horizontal alveolar ridge deficiencies (population), what is the effect of lateral bone augmentation procedures (intervention and comparison) on peri-implant health (outcome)? " Included studies were randomised controlled trials or controlled clinical trials with a follow-up of at least 12 months after implant loading. Meta-analyses were performed whenever possible, including subgroup analysis based on follow-up. RESULTS Twelve final publications from eight investigations were included. The results from the meta-analysis indicated that irrespective of the type of intervention, the inflammatory changes, based on bleeding on probing (%) were minimal, both at short- (n = 1; weighted mean difference [WMD] = -1.00; 95% CI [-14.04; 12.04]; p = .881) and long-term (n = 5; WMD = -5.63; 95% CI [-18.42; 7.16]; p = .881). When comparing different treatment modalities, no significant differences were observed (n = 6; WMD = -3.36; 95% CI [-12.49; 5.77]; p < .471). Similarly, changes in probing pocket depth and marginal bone levels were not significantly different among groups. The incidence of peri-implantitis was evaluated in three investigations and varied from 16% to 26% after a follow-up period of 6-8 years. CONCLUSIONS The results from this systematic review and meta-analysis have shown that lateral ridge augmentation procedures can maintain peri-implant health over time with low mucosal inflammatory changes and a relatively small incidence of peri-implant bone loss.
Understanding Short Texts through Semantic Enrichment and Hashing

Clustering short texts (such as news titles) by their meaning is a challenging task. The semantic hashing approach encodes the meaning of a text into a compact binary code. Thus, to tell if two texts have similar meanings, we only need to check if they have similar codes. The encoding is created by a deep neural network, which is trained on texts represented by word-count vectors (bag-of-word representation). Unfortunately, for short texts such as search queries, tweets, or news titles, such representations are insufficient to capture the underlying semantics. To cluster short texts by their meanings, we propose to add more semantic signals to short texts. Specifically, for each term in a short text, we obtain its concepts and co-occurring terms from a probabilistic knowledge base to enrich the short text. Furthermore, we introduce a simplified deep learning network consisting of a 3-layer stacked auto-encoders for semantic hashing. Comprehensive experiments show that, with more semantic signals, our simplified deep learning model is able to capture the semantics of short texts, which enables a variety of applications including short text retrieval, classification, and general purpose text processing.
Novel Wearable Electrodes Based on Conductive Chitosan Fabrics and Their Application in Smart Garments

Smart garments, which can capture electrocardiogram signals at any time or location, can alert others to the risk of heart attacks and prevent sudden cardiac death when people are sleeping, walking, or running. Novel wearable electrodes for smart garments based on conductive chitosan fabrics were fabricated by electroless plating of silver nanoparticles onto the surfaces of the fibers. The electrical resistance, which is related to the silver content of the composite fabrics, can be as low as 0.0332 ± 0.0041 Ω/sq due to the strong reactivity between amine groups and silver ions. After washing these fabrics eight times, the electrical resistance remained below 1 Ω/sq. The conductive chitosan fabrics were applied to smart garments as wearable electrodes to capture electrocardiogram signals of the human body in static state, jogging state, and running state, which showed good data acquisition ability and sensitivity.
Epithelial contribution to the profibrotic stiff microenvironment and myofibroblast population in lung fibrosis

Myofibroblasts are key effector cells in tissue stiffening and fibrosis progression, but the contribution of cells undergoing epithelial–mesenchymal transition (EMT) is unclear. Unlike EMT, myofibroblasts contribute to tissue stiffening through their contractility and expression of fibrillar collagens, which is associated with aberrant FAK (Y397) hyperactivation.
Biosynthesised Silver Nanoparticles from Fungi as Antimicrobial Agents for Endo-Perio Lesions – A Review

Even after rapid development in the treatment modalities and contemporary dental practice, still we encounter the failures due to endodontic, periodontal or combined lesions. Complete elimination of bacteria is the key for successful treatment. The emerging new microorganisms, increase in the Review Article Kiran et al.; ARRB, 10(6): 1-7, 2016; Article no.ARRB. 25756 2 number of resistant microbial strains and complexity of the tooth system demands the development of new treatment strategies to control the disease process and to achieve high success rate. Silver nanoparticles (AgNps) are considered to be the best antimicrobial agents because of their unique mode of action, broad spectrum of activity and biocompatibility. Concern has been raised on the toxicity of chemical agents used in AgNps synthesis. Compared with the synthetic methods, biological methods provide a novel idea for the production of nano-materials. Therefore, nanotechnology rather biosynthesized nanoparticles is the field of interest for research. This review article highlights the application of biosynthesised AgNps produced from fungi as antimicrobial agents for endodontic, periodontal or combined lesions.
Psychosocial Factors Related to Functional Restoration Treatment Completion and Return-to-Function for Patients With Chronic Disabling Occupational Musculoskeletal Disorders

Objective: The aim of this study was to identify demographic and psychosocial variables associated with successful completion of a functional restoration program and return-to-function within 3 months of treatment completion. Methods: Three hundred seven patients admitted to the functional restoration program were evaluated for completion status and 200 patients with valid data were assessed for 3-month return-to-function status following completion. Psychosocial and functional status was assessed at baseline. Results: Key factors associated with program completion included lower perceived disability, lower pain, lower functional impairment, and lower fear avoidance. Factors associated with 3-month return-to-function included lower perceived disability, lower depression, greater belief that pain is not associated with impairment, and higher quality of life. Conclusions: Psychosocial and functional factors contribute to both functional restoration completion and 3-month return-to-function outcomes.
Topic-specific analysis of search queries

The analysis of search engine logs is important in order to understand how users interact with a search engine. Conventional analysis of search engine log data looks at various metrics such as query and session length aggregated over the full data set. Here we segment the data according to a top-level ontology of web search and compute the metrics on a topic by topic basis. Our results show that although for a given metric, such as query length, the statistics of most classes are similar to the aggregate statistic; there are usually some outlier classes which exhibit deviant behavior.
A Semi-Lagrangian two-level preconditioned Newton-Krylov solver for constrained diffeomorphic image registration

We propose an efficient numerical algorithm for the solution of diffeomorphic image registration problems. We use a variational formulation constrained by a partial differential equation (PDE), where the constraints are a scalar transport equation. We use a pseudospectral discretization in space and second-order accurate semi-Lagrangian time stepping scheme for the transport equations. We solve for a stationary velocity field using a preconditioned, globalized, matrix-free Newton-Krylov scheme. We propose and test a two-level Hessian preconditioner. We consider two strategies for inverting the preconditioner on the coarse grid: a nested preconditioned conjugate gradient method (exact solve) and a nested Chebyshev iterative method (inexact solve) with a fixed number of iterations. We test the performance of our solver in different synthetic and real-world two-dimensional application scenarios. We study grid convergence and computational efficiency of our new scheme. We compare the performance of our solver against our initial implementation that uses the same spatial discretization but a standard, explicit, second-order Runge-Kutta scheme for the numerical time integration of the transport equations and a single-level preconditioner. Our improved scheme delivers significant speedups over our original implementation. As a highlight, we observe a 20× speedup for a two dimensional, real world multi-subject medical image registration problem.
Fatigue and fracture behaviour of aluminothermic rail welds under high axle load conditions

I ACKNOWLEDGEMENTS . III DECLARATION . V TABLE OF CONTENTS . VI LIST OF PUBLICATIONS . X LIST OF FIGURES . XI LIST OF TABLES . XX IV LIST OF NOTATIONS AND ACRONYMS . XXV CHAPTER
Genes as Cues of Relatedness and Social Evolution in Heterogeneous Environments

There are many situations where relatives interact while at the same time there is genetic polymorphism in traits influencing survival and reproduction. Examples include cheater-cooperator polymorphism and polymorphic microbial pathogens. Environmental heterogeneity, favoring different traits in nearby habitats, with dispersal between them, is one general reason to expect polymorphism. Currently, there is no formal framework of social evolution that encompasses genetic polymorphism. We develop such a framework, thus integrating theories of social evolution into the evolutionary ecology of heterogeneous environments. We allow for adaptively maintained genetic polymorphism by applying the concept of genetic cues. We analyze a model of social evolution in a two-habitat situation with limited dispersal between habitats, in which the average relatedness at the time of helping and other benefits of helping can differ between habitats. An important result from the analysis is that alleles at a polymorphic locus play the role of genetic cues, in the sense that the presence of a cue allele contains statistical information for an organism about its current environment, including information about relatedness. We show that epistatic modifiers of the cue polymorphism can evolve to make optimal use of the information in the genetic cue, in analogy with a Bayesian decision maker. Another important result is that the genetic linkage between a cue locus and modifier loci influences the evolutionary interest of modifiers, with tighter linkage leading to greater divergence between social traits induced by different cue alleles, and this can be understood in terms of genetic conflict.
Levels of neuroticism differentially predict individual scores in the depression and anxiety dimensions of the tripartite model: A multiwave longitudinal study

The associations between neuroticism and depression and anxiety symptoms remain unclear possibly due to the lack of specificity and covariation among common symptoms. The current study hypothesized that neuroticism acts as a vulnerability factor for general distress (GD) and specific depression and anxiety symptoms. We investigated this hypothesis using the "tripartite model" (a well-known dimensional model of anxiety and depression). A sample of 644 college students was recruited from Hunan, China. In the initial stage of this investigation, the students completed self-assessment forms to measure their levels of neuroticism and frequency of daily hassles. The students also reported on their levels of GD and specific depression and anxiety symptoms. The same self-assessment procedure was undertaken once a month for the next 6 months. The results of the hierarchical linear model suggest a significant interaction between neuroticism and the level of stress caused by exposure to increased levels of daily hassles, which can be used as a predictor of future levels of GD and specific anxiety symptoms. However, the same interaction was not a significant predictor of specific depression symptoms. The current findings suggest that neuroticism may be a risk factor for specific anxiety but not for specific depression symptoms in face of stress caused by daily hassles.
Evaluation of Modified Vector Space Representation Using ADFA-LD and ADFA-WD Datasets

Predicting anomalous behaviour of a running process using system call trace is a common practice among security community and it is still an active research area. It is a typical pattern recognition problem and can be dealt with machine learning algorithms. Standard system call datasets were employed to train these algorithms. However, advancements in operating systems made these datasets outdated and un-relevant. Australian Defence Force Academy Linux Dataset (ADFA-LD) and Australian Defence Force Academy Windows Dataset (ADFA-WD) are new generation system calls datasets that contain labelled system call traces for modern exploits and attacks on various applications. In this paper, we evaluate performance of Modified Vector Space Representation technique on ADFA-LD and ADFA-WD datasets using various classification algorithms. Our experimental results show that our method performs well and it helps accurately distinguishing process behaviour through system calls.
Dynamic Switch of Negative Feedback Regulation in Drosophila Akt–TOR Signaling

Akt represents a nodal point between the Insulin receptor and TOR signaling, and its activation by phosphorylation controls cell proliferation, cell size, and metabolism. The activity of Akt must be carefully balanced, as increased Akt signaling is frequently associated with cancer and as insufficient Akt signaling is linked to metabolic disease and diabetes mellitus. Using a genome-wide RNAi screen in Drosophila cells in culture, and in vivo analyses in the third instar wing imaginal disc, we studied the regulatory circuitries that define dAkt activation. We provide evidence that negative feedback regulation of dAkt occurs during normal Drosophila development in vivo. Whereas in cell culture dAkt is regulated by S6 Kinase (S6K)–dependent negative feedback, this feedback inhibition only plays a minor role in vivo. In contrast, dAkt activation under wild-type conditions is defined by feedback inhibition that depends on TOR Complex 1 (TORC1), but is S6K–independent. This feedback inhibition is switched from TORC1 to S6K only in the context of enhanced TORC1 activity, as triggered by mutations in tsc2. These results illustrate how the Akt–TOR pathway dynamically adapts the routing of negative feedback in response to the activity load of its signaling circuit in vivo.
Curcumin decreases astrocytic reaction after gliotoxic injury in the rat brainstem

ABSTRACT Recent studies have demonstrated that curcumin (Cur) has antioxidant, anti-inflammatory and anti-fibrotic effects. Ethidium bromide (EB) injections into the central nervous system (CNS) are known to induce local oligodendroglial and astrocytic loss, resulting in primary demyelination and neuroinflammation. Peripheral astrogliosis is seen around the injury site with increased immunoreactivity to glial fibrillary acidic protein (GFAP). This investigation aimed to evaluate the effect of Cur administration on astrocytic response following gliotoxic injury. Wistar rats were injected with EB into the cisterna pontis and treated, or not, with Cur (100 mg/kg/day, intraperitoneal route) during the experimental period. Brainstem sections were collected at 15, 21 and 31 days after EB injection and processed for GFAP immunohistochemical staining. Astrocytic reactivity was measured in a computerized system for image analysis. In Cur-treated rats, the GFAP-stained area around the lesion was significantly smaller in all periods after EB injection compared to untreated animals, showing that Cur reduces glial scar development following injury.
Mode Domain Spatial Active Noise Control Using Sparse Signal Representation

Active noise control (ANC) over a sizeable space requires a large number of reference and error microphones to satisfy the spatial Nyquist sampling criterion, which limits the feasibility of practical realization of such systems. This paper proposes a mode-domain feedforward ANC method to attenuate the noise field over a large space while reducing the number of microphones required. We adopt a sparse reference signal representation to precisely calculate the reference mode coefficients. The proposed system consists of circular reference and error microphone arrays, which capture the reference noise signal and residual error signal, respectively, and a circular loudspeaker array to drive the anti-noise signal. Experimental results indicate that above the spatial Nyquist frequency, our proposed method can perform well compared to a conventional methods. Moreover, the proposed method can even reduce the number of reference microphones while achieving better noise attenuation.
Zero-Cost Coercions for Program and Proof Reuse

We introduce the notion of identity coercions between non-indexed and indexed variants of inductive datatypes, such as lists and vectors. An identity coercion translates one type to another such that the coercion function definitionally reduces to the identity function. This allows us to reuse vector programs to derive list programs (and vice versa), without any runtime cost. This also allows us to reuse vector proofs to derive list proofs (and vice versa), without the cost of equational reasoning proof obligations. Our work is formalized in Cedille, a dependently typed programming language based on a type-annotated Curry-style type the- ory with implicit (or, erased) products (or, dependent functions), and relies crucially on erasure to introduce definitional equalities between underlying untyped terms.