Miracle-dz/UniFAR-FTUs huggingface dataset:
University of Birmingham Branching time active inference

Active inference is a state-of-the-art framework for modelling the brain that explains a wide range of mechanisms such as habit formation, dopaminergic discharge and curiosity. However, recent implementations suffer from an exponential (space and time) complexity class when computing the prior over all the possible policies up to the time horizon. Fountas et al. (2020) used Monte Carlo tree search to address this problem, leading to very good results in two different tasks. Additionally, Champion et al. (2021a) proposed a tree search approach based on (temporal) structure learning. This was enabled by the development of a variational message passing approach to active inference (Champion, Bowman, Grześ, 2021), which enables compositional construction of Bayesian networks for active inference. However, this message passing tree search approach, which we call branching-time active inference (BTAI), has never been tested empirically. In this paper, we present an experimental study of the approach (Champion, Grześ, Bowman, 2021) in the context of a maze solving agent. In this context, we show that both improved prior preferences and deeper search help mitigate the vulnerability to local minima. Then, we compare BTAI to standard active inference (AcI) on a graph navigation task. We show that for small graphs, both BTAI and AcI successfully solve the task. For larger graphs, AcI exhibits an exponential (space) complexity class, making the approach intractable. However, BTAI explores the space of policies more efficiently, successfully scaling to larger graphs. Then, BTAI was compared to the POMCP algorithm (Silver and Veness, 2010) on the frozen lake environment. The experiments suggest that BTAI and the POMCP algorithm accumulate a similar amount of reward. Also, we describe when BTAI receives more rewards than the POMCP agent, and when the opposite is true. Finally, we compared BTAI to the approach of Fountas et al. (2020) on the dSprites dataset, and we discussed the pros and cons of each approach. © 2022TheAuthor
Proceedings of the Workshop on Data Exploration for Interactive Surfaces (DEXIS 2015)

By design, interactive tabletops and surfaces provide numerous opportunities for data visualization and analysis. In information visualization, scientific visualization, and visual analytics, useful insights primarily emerge from \emph{interactive} data exploration. Nevertheless, interaction research in these domains has largely focused on mouse-based interactions in the past, with little research on how interactive data exploration can benefit from interactive surfaces. These proceedings represent the results of the DEXIS 2015 Workshop on Data Exploration for Interactive Surfaces. It was held in conjunction with the ACM International Conference on Tabletops and Interactive Surfaces (ITS) in Funchal on the island of Madeira, Portugal, on November 15, 2015.
Nerve Meningioma: Case Necrotizing Neurosarcoidosis Masquerading

Summary: Isolated neurosarcoidosis involving the optic nerve meninges is extremely rare and is often indistinguishable from a meningioma in its anatomic site and MR imaging presentation. Characteristic findings include enhanced perineural encasement and thickening of the affected optic nerve on contrast-enhanced T1-weighted cranial MR imaging studies. We present the case report of a patient with isolated necrotizing neurosarcoidosis of the left optic nerve, with clinical and MR imaging findings strongly suggestive of a preoperative diagnosis of a meningioma.
Hidrotermal Alterasyona Bağlı Element Kirliliği: Canca (Gümüşhane - Türkiye)

: In this study it is aimed to investigate possible elemental pollution in hydrothermal alteration areas. For this purpose, the element contents of 288 soil samples collected from the Canca (Gümüşhane -Turkey) hydrothermal alteration area were compared with the accepted average values (background values) and examined with different pollution parameters. As a result of the study, it was determined
Characteristics of underwater noise near the west roadstead of the port of Vladivostok

The characteristics of underwater noise registered near the west roadstead of the port of Vladivostok, near Vladivostok Aquarium is investigated. The traffic intensity across roadstead near this area is up to 20 large tonnage vessels per day. Studies were carried out using a freely drifting vertical array of omnidirectional hydrophones, connected with recording equipment placed on a boat, drifting along with the receiving system. Studies were carried out in the dry windless weather. Depth in the study area was 20-30 m, a sea bottom - stone, sand and limestone. Sea State was calm, flow velocity does not exceed 0.5 knots. The results of estimation of average spectral noise density, perceived by a single hydrophone, auto and mutually-spectrums for two hydrophones is described. The results are presented for cases when the distance between the receiving system and moving or anchored vessel was over 2 miles. The results of changing characteristics of underwater noise when at 0.2 miles from the receiving system .
VET: Autonomous Vehicular Credential Verification using Trajectory and Motion Vectors

There has been significant progress in autonomous vehicles: autonomous automobiles, unmanned aerial vehicles, and many more are improving our quality of life and making it safer. However, this also opens up a new attack paradigm: now, an adversary can take control of these autonomous systems to cause life-threatening scenarios. It becomes possible due to the broadcast nature of wireless communication, which connects autonomous vehicles in an ad-hoc network. Traditional crypto-algorithms alone cannot tackle the problem as the crypto-credentials can be compromised or even issued to adversarial parties. We propose VET: a framework that verifies the veracity of the crypto-credentials by authen-ticating them against physical trajectory and motion vectors (TMVs). The verifier implements a location and motion-based authentication to verify the crypto-credentials based on the acceptability of claimed TMVs against randomly estimated TMVs. This prevents any adversary from re-motely injecting spoofed messages when it is not physically present. We formally analyze the correctness and robustness of VET using matching conversations. Finally, we attest to the findings of the theoretical analysis using an experimentally analyzed VET on the USRP platform. Our experiments show that VET has 97% true positives when operating without an adversary. Also, VET can detect advanced remote adversaries with 99.9% who is capable of manipulating signals with absolute channel knowledge.
2015). Processing the image gradient field using a topographic primal sketch approach. International

The spatial derivatives of the image intensity provide topographic information that may be used to identify and segment objects. The accurate computation of the derivatives is often hampered in medical images by the presence of noise and a limited resolution. This paper focuses on accurate computation of spatial derivatives and their subsequent use to process an image gradient ﬁeld directly, from which an image with improved characteristics can be reconstructed. The improvements include noise reduction, contrast enhancement, thinning object contours and the preservation of edges. Processing the gradient ﬁeld directly instead of the image is shown to have numerous beneﬁts. The approach is developed such that the steps are modular, allowing the overall method to be improved and possibly tailored to di ﬀ erent applications. As presented, the approach relies on a topographic representation and primal sketch of an image. Comparisons with existing image processing methods on a synthetic image and di ﬀ erent medical images show improved results and accuracy in segmentation. Here the focus is on objects with low spatial resolution, which is often the case in medical images. The methods developed show the importance of improved accuracy in derivative calculation, and the potential in processing the image gradient ﬁeld directly.
Review of Research on Children with Autism Spectrum Disorder and the Use of Technology

This article presents the results of a review of research on children with autism spectrum disorders (ASD) and the use of technology. The review included 255 peer-reviewed research articles published between 2000 and 2010, archived in the ACM, IEEE, EBSCO, and ERIC databases. Content analysis was a method for analyzing articles. The results of the review indicated a clear growth in the number of research articles published during the last decade that addressed the issue of technologies for children with autism. Natural settings and field experiments were a typical methodology of the research, which reflected a strong link between academia and the field. On the other hand, the development of technologies took place separately from the user and children with ASD who participated in research activities in the latter stages of research and development. The results also indicated a strong research focus on the identification and provision of solutions to the problems typically related to ASD. Since the review only considered research focusing on problems related to ASD, future research should focus on the emergence of the capabilities and strengths of children with these disorders.
Why the first colonisation of the Australian region is the earliest evidence of modern human behaviour

The first colonisation of the Greater Australian region, Sahul, is the oldest evidence for the expression of behaviour that is distinctively human. This colonisation is central to an understanding of human evolution because it provides certainties where other events provide only ambiguities. In this paper we expand on our previous outlines (e.g. Davidson and Noble 1989; Nobel and Davidson 1991) of a case that the first passage of people from the Greater Southeast Asian landmass, Sunda, to Sahul represents the earliest documentation of the evolutionary emergence of language. Our case stands opposed to an argument for an early emergence, which rests on the appearance on endocasts of Homo habilis of novel features in the region of Broca's area (Tobias 1987; Falk 1983). We define what we mean by language in due course and briefly set out why it is distinctively human, why it is necessary for the first colonisation of Australia, and also fundamental to understanding behaviours that characterise the evolutionary emergence of human diversity. The first colonisation of Sahul, perhaps as much as 53,000 years ago (Roberts, Jones and Smith 1991), involved the use of a boat, and was part of a pattern of Pleistocene boat use undocumented elsewhere until the end of the Pleistocene (Cherry 1990). It also involved only humans fully modern in their anatomy, as no remains of other hominid fossils have been found in Australia (Brown 1990). Finally, the early archaeology of the Australian region provides evidence as old as or older than elsewhere for the sorts of symbolic activities (Morse in press) distinctive of the behaviour of modern humans. Claims that the beginnings of these activities can be seen as glimmerings in the earlier record are under sustained attack (Chase and Dibble 1987; Davidson 1991; d'Errico 1991; White in press), and do not seem likely to survive.
Conservation Agriculture for combating land degradation in Central Asia: a synthesis

This manuscript reviews scientific findings on agricultural systems, associated land degradation and selected remedies such as Conservation Agricultural (CA) practices to counterbalance these. In particular, this review addresses the research findings onCA practices conducted in the rainfed and irrigated systems in Central Asia. The arid and semi-arid croplands in this region are vulnerable to different types of soil and environmental degradation, and particularly to degradation caused by intensive tillage, irrigation water mismanagement, and cropping practices, especially in the Aral Sea Basin. Overall, the evidence shows that various CA elements, such as permanent beds, seems to be technically suitable for the major cropping systems and despite the heterogeneous conditions in the region. CA practices can contribute to combating on-going land degradation. No-till seeding along with the maintenance of a permanent soil coverage e.g. by residue retention, reduces wind and water erosion, increases water infiltration and storage which can reduce crop water stress, improve soil quality and increase soil organic matter. Further, CA practices can lead to similar or even higher crop yields while reducing production resource needs and costs considerably, including fuel, seeds, agrochemicals, water and labour. Nevertheless, the growing research evidence on the productivity, economic and environmental benefits that can be harnessed with CA, still is from a limited number of studies and hence more research at local scale is needed.
Target Based Resource Allocation and Scheduling Algorithm for Systematic Workflow in Cloud

: Cloud computing is the latest emerging technology that provision service in distributed system supports to investigate its benefits on executing scientific scheduling workflow. However, the major goal of cloud computing is to allocate resource to user truly needed based on pay for use. Furthermore, deadline scheduling workflow is the most challenging problem in cloud. This paper proposes a system that includes minimize cost of the execution, resource provisioning, and deadline constrained scheduling scientific workflows on Infrastructure as a Service (IaaS) clouds. We use the concept of an algorithm based on the particle swarm optimization (PSO), deadline schedule generation. These algorithms minimize the overall workflow execution cost while meeting deadline constraints. The results show that our approach increase likelihood of deadlines being met and reduces the execution cost as compare to current state-of-the-art algorithms.
Guide to Geographical Indications: Linking Products and Their Origins (Summary)

Geographical Indications present significant opportunities for differentiating products or services that are uniquely related to their geographic origin. While they can offer many positive economic, social, cultural, and even environmental benefits, they can also be problematic and therefore caution is warranted when pursuing them. The publication distills the relevant lessons that could apply, particularly to developing countries, from a review of more than 200 documents and a number of original Case Studies. It presents a groundwork to better understand the costs and the benefits of undertaking Geographical Indications by outlining the basic processes, covering the pros and cons of different legal instruments, and offering insights into the important factors of success. It reviews and presents current data on the key issues of global GIs such as: economic results, public and private benefits; and market relevance.
CasesMicrovascular Permeability: Report of Two Blood Volume, Blood Flow, and Brain Tumors: Quantitative Assessment of Dynamic, Contrast-Enhanced CT of Human

Summary: We present two patients with metastatic brain tumors who underwent dynamic contrast-enhanced CT that yielded estimates of blood volume, blood flow, and microvascular permeability. For one patient, these CT-based quantifications were compared with contrast-enhanced MR-based assessments of fractional blood volume and permeability. Regional blood volume heterogeneity patterns were consistent between both CT-and MR imaging-based mapping. Permeability variations across the tumor, however, were less consistent, perhaps because of differences between ionic and non-ionic contrast agents. The advantages of dynamic contrast-enhanced CT in comparison with dynamic, contrast-enhanced MR imaging for microvascular quantifications are its availability and lower costs, applicability in the presence of MR imaging contra-indications, and potentially more accurate analyses. The disadvantages are its limited anatomic coverage, radiation exposure, and the need for injection of a contrast agent.
Modelling Local Patterns of Child Mortality Risk. A Bayesian Spatio-Temporal Analysis.

Background : At the international scale the number of child deaths is still high; however, this figure is susceptible to be reduced implementing proper spatially-targeted health public policies. Due to its alarming rate in comparison to North American standards, child mortality is a particular health concern in Mexico. Despite this fact, there remains a dearth of studies that address the spatial-temporal identification of child mortality in Mexico. The aims of this study are i) to model the evolution of child mortality risk at the municipality level in Greater Mexico City, (ii) to identify municipalities with high, medium and low risk over time, and (iii) using municipality trends, to ascertain potential high-risk municipalities. Methods : In order to control for space-time patterns of data, the study performs a Bayesian spatio-temporal analysis. This methodology allows to model the geographical variation of child mortality risk across municipalities within the studied time span. Results : The analysis shows that most of the high-risk municipalities are in the north and west areas of Greater Mexico City, although there coexist some in the east; some of them presenting an increasing child mortality risk trend. The outcomes highlight some municipalities currently presenting a medium risk, but that, given their trend, are likely to become high risk after the studied period. Finally, the likelihood of child mortality risk illustrates an overall decreasing tendency throughout the 7-year studied period. Conclusions : The identification of high-risk municipalities and risk trends may provide a useful input to policy-makers seeking to reduce the incidence of child mortality. The results provide evidence to support geographical targeting for policy interventions.
Her-Short communication: A comparative analysis of recombinant chymosins

The first step in cheesemaking is the milk clotting process, in which κ-caseinolytic enzymes contribute to micelle precipitation. The best enzyme for this purpose is chymosin because of its high degree of specificity toward κ-casein. Although recombinant bovine chymosin is the most frequently used chymosin in the industry, new sources of recombinant chymosin, such as goat, camel, or buffalo, are now available. The pres-ent work represents a comparative study of 4 different recombinant chymosins (goat and buffalo chymosins expressed in Pichia pastoris , and bovine and camel chymosin expressed in Aspergillus niger ). Recombinant goat chymosin exhibited the best catalytic efficiency compared with the buffalo, bovine, or camel recombinant enzymes. Moreover, recombinant goat chymosin exhibited the best specific proteolytic activity, a wider pH range of action, and a lower glycosylation degree than the other 3 enzymes. In conclusion, we propose that recombinant goat chymosin represents a serious alternative to recombinant bovine chymosin for use in the cheesemaking industry.
, analytic continuation with respect to n

We speak about the part of integrable system theory dealing with conformal theory and W -algebras (ordinary and deformed). Some new approaches to ﬁnding Bethe equations that describe the spectrum of Hamiltonians of these quantum integrable systems are developed. The derivation of the Bethe equations is based on the technique of shuﬄe algebras arising in quantum group theory.
Indoor-Outdoor Scene Recognition Method based on Adaboost-PNN

In response to the challenges of low accuracy and instability in indoor-outdoor seamless positioning technology for scene recognition, this paper proposes a high-precision indoor-outdoor scene recognition method based on an adaptive boosting algorithm (Adaboost-PNN). The method utilizes the built-in magnetic sensor, Global Navigation Satellite System (GNSS) module, and WiFi module of a smartphone to collect training data. Based on the different features exhibited by the number of satellites, signal-to-noise ratio, geomagnetic intensity, and WiFi signal strength in indoor and outdoor environments, the data features are input into the adaptive boosting algorithm to train the indoor-outdoor scene recognition model. Comprehensive testing results demonstrate that the indoor-outdoor scene recognition model based on the adaptive boosting algorithm achieves a recognition accuracy of 96.3% in different scenes, effectively distinguishing between indoor and outdoor environments. Moreover, compared to traditional recognition algorithms such as SVM, KNN, and PNN, the proposed method shows an improvement of 2%-5% in accuracy.
Effect of pH and gel electrolyte on safe charge injection and electrode degradation of platinum electrodes

Platinum (Pt) is a widespread electrode material choice for neural interfaces and electrochemical biosensors, due to its supposed electrochemical inertness. However, faradaic reactions can take place at Pt electrodes, including Pt oxide formation and reduction. Repeated redox cycles of Pt can lead to Pt dissolution, which may harm the tissue and significantly reduce electrode lifetime. In this study, we investigated how the electrolyte may influence Pt dissolution mechanisms during current pulsing. Two electrolyte characteristics were considered: pH and gelation. We confirmed that empirically reported tissue damage thresholds correlate with Pt oxide formation and reduction. Varying electrolyte pH occasioned a shift in recorded potentials, however, damage thresholds correlated with the same mechanisms for all pH values. The similar behaviour observed for pH values in the central range (4 ≤ pH ≤ 10) can be explained by variations of local pH at the electrode surface. Gel electrolytes behaved comparably to solutions, which was confirmed by statistical similarity tests. This study extends the knowledge about platinum electrochemistry and shows the necessity to carefully choose the stimulation protocol and the electrolyte to avoid platinum dissolution and tissue damage.
Binary-SE-ResNet Model Research for Binary Classification of Chest X-Ray

In 2019, the outbreak of the novel coronavirus swept across the globe. With the increase in the number of infections, the demand for automated pneumonia detection gradually increased. This study mainly focused on the binary classification problem of pneumonia images and proposed a Binary-SE-ResNet that utilizes both the original image features and LBP image features extracted through the SE-ResNet34 model. The extracted features are then concatenated and input into an XGBoost model, which is optimized by a Bayesian optimizer to select the best model for the final classification. Considering the characteristics of the dataset, this study employed CLAHE for image enhancement during the preprocessing stage to improve contrast and noise reduction. Ultimately, the proposed model achieved an accuracy of 97.7% in the binary classification of pneumonia, which is a 0.9% improvement compared to the best single model. This demonstrates that the approach of extracting features from different feature maps and utilizing ensemble learning can better capture features and achieve improved classification performance.
EIS based detection of Microcystin-LR in Water using Stencil Printed Carbon Electrodes Decorated with Cysteamine Capped Gold Nanoparticles

Algal and cyanobacterial blooms that release toxins are proliferating rapidly as a result of the eutrophication of water bodies on a global scale. When the most common variant of the toxin, microcystin-LR, is found in amounts above the World Health Organization’s guideline it poses a major threat to human health worldwide. In this paper, we describe the development of an immunosensor by fabricating a stencil printed conductive carbon based interdigitated microelectrodes decorated with cysteamine capped gold nanoparticles. By using the electrochemical impedance spectroscopy, we could detect the concentration of 0.1 μg/L which is below the World Health Organization’s recommended limit.