Miracle-dz/UniFAR-FTUs huggingface dataset:
Octree-Based Hierarchical 3D Pathfinding Optimization of Three-Dimensional Pathfinding

Despite the fact that the problem of pathfinding exists for quite a while, most of the related work is focused on methods that are applicable only in 2D environment. However, there is a demand for methods that can provide a solution for pathfinding tasks in 3D working space. These tasks mostly come from area of robotics and game development, where it is often required to get a solution in real-time. The main problem, besides the lack of appropriate methods, is that in 3D environment the working space size increases greatly in comparison with 2D case, and it becomes harder to construct a precise representation of this space while maintaining low size of search graph. To overcome this, methods that lower graph size, whether explicitly or implicitly, are used. This paper provides a brief review on some of existing approaches, comparison of their effectiveness and presentation a new method of real-time pathfinding in 3D environment that can be helpful in real-time calculations (i.e. game development area). This brief review will be referred to when comparing proposed method with existing ones. A proposed method combines advantages of using octree structure as search graph (such as sparce free space representation) and hierarchical path planning (effective reduction of graph size by clustering). Thus, an appreciable speed-up is achieved.
Examining the Influence of Product Innovation on Social Media Platforms: Evidence from Instagram

Grounding our discussion in social penetration and resource allocation theories, we propose and test the influence of introducing a new product, namely Instagram Story, on the usage and content creation behavior on the Instagram platform. Employing a quasi-experimental setting, we investigate the influence of introducing this new product on user engagement and content generation. Our findings identify the significant influence of Instagram Story on the user engagement with the original product i.e. Instagram Post and the overall resource expenditure by content providers. Through our study we delineate several important implications for both research and practice. Specifically, our study provides significant insights for platform owners and content providers to better design and implement new products on social media platforms.
Behavioral simulation for smart contracts

While smart contracts have the potential to revolutionize many important applications like banking, trade, and supply-chain, their reliable deployment begs for rigorous formal verification. Since most smart contracts are not annotated with formal specifications, general verification of functional properties is impeded. In this work, we propose an automated approach to verify unannotated smart contracts against specifications ascribed to a few manually-annotated contracts. In particular, we propose a notion of behavioral refinement, which implies inheritance of functional properties. Furthermore, we propose an automated approach to inductive proof, by synthesizing simulation relations on the states of related contracts. Empirically, we demonstrate that behavioral simulations can be synthesized automatically for several ubiquitous classes like tokens, auctions, and escrow, thus enabling the verification of unannotated contracts against functional specifications.
Circulating fibrocytes can identify people with a poor prognosis in pulmonary fibrosis

Rationale: Circulating fibrocytes are elevated in idiopathic pulmonary fibrosis, but the relationship between fibrocyte level with lung function decline and clinical outcomes remains controversial. Objective: To provide evidence for the utility of circulating fibrocyte levels as a prognostic biomarker in pulmonary fibrosis. Methods: We tested associations between circulating fibrocyte levels, mortality, disease progression and longitudinal lung function in a well-defined prospective observational study of pulmonary fibrosis (PROFILE; NCT01134822). A subset of recruited participants had blood samples processed for fibrocyte measurement, with flow cytometry based on CD45 and collagen-I gating. Associations were tested using univariate and multivariate generalised linear models. Mortality data were combined with an independent study in a weighted-pooled analysis. Measurements and Main Results: In 119 participants with idiopathic pulmonary fibrosis and non-specific interstitial pneumonia, circulating fibrocytes were elevated but not significantly above healthy controls. In multivariate analyses, each increment in circulating fibrocyte was associated with a 4% increased risk of overall mortality (Hazard Ratio 1.04 95% CI 1.00-1.09). A weighted-pooled analysis of idiopathic pulmonary fibrosis alone supported these observations in a larger sample of 162 participants, a 5% rise in fibrocytes was associated with a 24% increase in adjusted mortality risk (Hazard Ratio 1.24 95% CI 1.06-1.46). We found no association of fibrocytes with lung function or disease progression. Conclusions: In a large prospective clinical cohort of pulmonary fibrosis, circulating fibrocytes can be isolated and increased levels are associated with increased mortality but do not associate with disease related decline in lung function.
Efficient Direct Agent Interaction in Optimistic Distributed Multi-Agent-System Simulations

Agent-to-agent communications is an important operation in multi-agent systems and their simulation. Given the data-centric nature of agent-simulations, direct agent-to-agent communication is generally an orthogonal operation to accessing shared data in the simulation. In distributed multi-agent-system simulations in particular, implementing direct agent-to-agent communication may impose serious performance degradation due to potentially large communication and synchronization overheads. In this paper, we propose an efficient agent-to-agent communication method in the context of optimistic distributed simulation of multi-agent systems. An implementation of the proposed method is demonstrated and quantitatively evaluated through its integration into the PDES-MAS simulation kernel.
Towards Preprocessing Guidelines for Neural Network Embedding of Customer Behavior in Digital Retail

Shopping transactions in digital retailing platforms enable retailers to understand customers' needs for providing personalized experiences. Researchers started modeling transaction data through neural network embedding, which enables unsupervised learning of contextual similarities between attributes in shopping transactions. However, every study brings different approaches for embedding customer's transactions, and clear preprocessing guidelines are missing. This paper reviews the recent literature of neural embedding for customer behavior and brings three main contributions. First, we provide a set of guidelines for preprocessing and modeling consumer transaction data to learn neural network embeddings. Second, it is introduced a multi-task Long Short-Term Memory Network to evaluate the guidelines proposed through the task of purchase behavior prediction. Third, we present a multi-contextual visualization of customer behavior embeddings, and its usefulness for purchase prediction and fraud detection applications. Results achieved illustrate accuracies above 40%, 60%, and 80% for predicting the next days, hours, and products purchased for some customers in a dataset composed of online grocery shopping transactions.
VersaTouch: A Versatile Plug-and-Play System that Enables Touch Interactions on Everyday Passive Surfaces

We present VersaTouch, a portable, plug-and-play system that uses active acoustic sensing to track fine-grained touch locations as well as touch force of multiple fingers on everyday surfaces without having to permanently instrument them or do extensive calibration. Our system is versatile in multiple aspects. First, with simple calibration, VersaTouch can be arranged in arbitrary layouts in order to fit into crowded surfaces while retaining its accuracy. Second, various modalities of touch input, such as distance and position, can be supported depending on the number of sensors used to suit the interaction scenario. Third, VersaTouch can sense multi-finger touch, touch force, as well as identify the touch source. Last, VersaTouch is capable of providing vibrotactile feedback to fingertips through the same actuators used for touch sensing. We conducted a series of studies and demonstrated that VersaTouch was able to track finger touch using various layouts with average error from 9.62mm to 14.25mm on different surfaces within a circular area of 400mm diameter centred around the sensors, as well as detect touch force. Finally, we discuss the interaction design space and interaction techniques enabled by VersaTouch.
Flagellar photoresponses of Chlamydomonas cells held on micropipettes: II. Change in flagellar beat pattern

Chlamydomonas cells immobilized on micropipettes were filmed at high-speed (500 f/s) while photostimulated either by one continuous light stimulus or by pulsed light in the frequency range of cell rotation (1 or 2 Hz). Two kinds of photophobic step-up and two kinds of photophobic step-down beat frequency changes without a reversal of flagellar beat were observed after frame-by-frame evaluation of 141 records. So far, a single step-up response, the “shock” response, has been considered the only photophobic response. However, the present results show that the cells always responded to step-up as well as to step-down light stimuli. Either a decrease of beat frequency by step-up was combined with an increase by step-down (type I), or an increase by step-up was combined with a decrease by step-down (type II). Whether type I or type II was observed depended on the preirradiation of the cells. All four responses are blue-light responses with a lag-time of ∼40 ms. Nothing can be said about the photoreceptor site. Regardless, these responses cannot be the basic mechanism for phototaxis, as assumed till now, because the flagella remain synchronized during the flagella beat frequency changes. Even if they are uncoupled before and after stimulation, both flagella respond in the same sense, i.e., either both increase or both decrease their beat frequency. The behavioral relevance of these responses for Chlamydomonas is not yet clear.
Image Approach to Speech Recognition on CNN

In this paper has been discussed about speech recognition using spectrogram images and deep convolution neural network(CNN) of Uzbek spoken digits. Spectrogram images from speech signal were generated and it were used for deep CNN training. Presented CNN model contains 3 convolution layers and 2 fully connected layers that discriminative features can be divided and estimated of spectrogram images by those layers. In current research period, dataset of Uzbek spoken digits were made and in based on presented CNN model they were trained. Testing results shows that, proposed approach for Uzbek spoken digits classified 100% accuracy.
BEHAVIOURAL EFFECT OF VARIOUS BINARY AND TERNARY SEX PHEROMONE BLENDS ON THE ASIAN CORN BORER (OSTRINZA FURNACALZS)

Abstract The response of individual male Asian corn borer moth was observed in a wind tunnel to various binary and ternary blends and dosages of synthetic sex pheromones, tetradecyl acetate. (E)‐12‐te‐tradecenyl acetate and (Z)‐12‐tetradecenyl acetate. Lures containing two components (Z+E) blends elicit the highest behavioral responses at ratios in the scope of 37. 5% E to 62. 5% E and there was no significant difference in this scope according to source contact step. The highest responses to the lures of three components are observed at ratio 1 : 5 : 4 (14 : Ac/Z/E), which have no significant difference to the best lures of two components. But dose‐response shows that the ternary blend has a wider response scope than the binary blend. The ternary blend can elicit behavioral response at dosage even as low as Ing, but the binary blend can not at the same dosage. The highest response scope is 100 to 1 000 ng for the binary blend and 100 to 10 000 ng for the ternary blend.
Benefits in Privacy Research: A Literature Review, Status Quo and Future Research Directions

Research in the stream of privacy considers benefits as an important and central concept. Benefits are the positive outcomes that individuals will experience when disclosing information. However, we see that only few papers theorize benefits in their research and thereby the operationalization and understanding of benefits is mixed, unstandardized and seems to follow no specific order. Based on that observation, we aim to provide a first step towards a standardized usage of benefits in privacy research studies, by summarizing existing benefits in privacy research and categorizing them. To do so, we base on the theory of perceived value. This theory uses five dimensions (emotional value, social value for myself, social value for others, monetary value as well as utility value), which we use to categorize different benefits. The results confirm our initial observations that indeed different constructs of benefits are used even in the same research context without justification. Implications among others refer to the recommendation to use the same term for the same construct and to consider all five dimensions of benefits in privacy research studies.
Using Graph Representations for Semantic Information Extraction from Chinese Patents

This paper proposes a graph representation approach to automatically extract semantic information from Chinese patents, which can be used to provide semantic support for text-content based patent intelligent analysis. Two graph models are devised using graph representations, i.e., a keyword based text graph model and a dependency tree based text graph model. The first graph model is constructed by computing the similarities between two keywords, while the second graph model is constructed by extracting syntactic relations from text sentences. In the case study a frequent subgraph mining algorithm is utilized to discover frequent subgraph patterns based on the above two models, and such patterns were further used as features to build text classifiers for the purpose of testing the expressivity and effectiveness of the proposed graph models. The experimental results proves the validation of the proposed graph representation methods.
Naming Anonymous Processes with Test-and-Set Registers

A system of anonymous processes that have no names assigned to them is considered in both synchronous and asynchronous communication models. The processes are fault free and can only communicate using test-and-set (TAS) registers. The aim of the paper is to assign unique names to all processes using a distributed algorithm. The naming of anonymous processes is studied in eight new problem models based on two categories; the number of TAS registers available, and the knowledge of the number of processes. In this paper, two distributed naming algorithms are developed that can assign unique names to anonymous processes. One is deterministic and the other is randomized. The developed algorithms are optimal in time complexity and namespace size. The Sequential Lookup algorithm, which is a deterministic algorithm, has a time complexity of 0(n2) steps, whereas the Random Lookup algorithm, which is a randomized algorithm, has a time complexity of 0(n log n) steps. Proof of the correctness of each naming algorithm is presented for all categories of the problem model where the number of processes is known. The Random Lookup algorithm has a better time complexity compared to the Sequential Lookup algorithm due to the use of randomness in accessing TAS registers.
Epigenetic and transcriptional regulation of osteoclastogenesis in the pathogenesis of skeletal diseases: a protocol for a systematic review

Osteoclastogenesis (OCG) is a multi-stage process that involves formation of activated osteoclasts from bone marrow macrophages. The progression of each stage of OCG is governed by a set of transcription factors and gene regulators, which are genetically and epigenetically regulated at both transcription and post-transcription levels. Epigenetic changes are used to denote interactions between genetic material and environment leading to phenotypic alterations that can be inherited, without any variations in DNA sequence. Epigenetic and transcription regulatory events have profound effects on osteoclast formation and activation; these have been implicated in numerous disorders including osteoporosis, osteopetrosis, rheumatoid arthritis, spondyloarthritis, gout and bone metastasis. We aim to conduct a systematic review to assess possible relationship between key epigenetic and transcriptional regulators of osteoclastogenesis, and their role in specific bone disorders. This is a protocol for the proposed review. Keywords: osteoclasts; osteoclastogenesis; epigenetics; transcription; bone; skeletal disorders; methylation; histone proteins; micro RNA
Autonomic Power Management in Speculative Simulation Runtime Environments

While transitioning to exascale systems, it has become clear that power management plays a fundamental role to support a viable utilization of the underlying hardware, also performance-wise. To meet power restrictions imposed by future exascale supercomputers, runtime environments will be required to enforce self-tuning schemes to run dynamic workloads under an imposed power cap. Literature results show that, for a wide class of multi-threaded applications, tuning both the degree of parallelism and frequency/voltage of cores allows a more effective use of the budget, compared to techniques that use only one of these mechanisms in isolation. In this paper, we explore the issues associated with applying these techniques on speculative Time-Warp based simulation runtime environments. We discuss how the differences in two antithetical Time Warp-based simulation environments impact the obtained results. Our assessment confirms that the performance gains achieved through a proper allocation of the power budget can be significant. We also identify the research challenges that would make these form of self-tuning more broadly applicable.
Demonstrating the Consequences of Learning Missingness Patterns in Early Warning Systems for Preventative Health Care: A Novel Simulation and Solution

When using tree-based methods to develop predictive analytics and early warning systems for preventive healthcare, it is important to use an appropriate imputation method to prevent learning the missingness pattern. To demonstrate this, we developed a novel simulation that generated synthetic electronic health record data using a variational autoencoder with a custom loss function, which took into account the high missing rate of electronic health data. We showed that when tree-based methods learn missingness patterns (correlated with adverse events) in electronic health record data, this leads to decreased performance if the system is used in a new setting that has different missingness patterns. Performance is worst in this scenario when the missing rate between those with and without an adverse event is the greatest. We found that randomized and Bayesian regression imputation methods mitigate the issue of learning the missingness pattern for tree-based methods. We used this information to build a novel early warning system for predicting patient deterioration in general wards and telemetry units: PICTURE (Predicting Intensive Care Transfers and other UnfoReseen Events). To develop, tune, and test PICTURE, we used labs and vital signs from electronic health records of adult patients over four years (n=133,089 encounters). We analyzed primary outcomes of unplanned intensive care unit transfer, emergency vasoactive medication administration, cardiac arrest, and death. We compared PICTURE with existing early warning systems and logistic regression at multiple levels of granularity. When analyzing PICTURE on the testing set using all observations within a hospital encounter (event rate = 3.4%), PICTURE had an area under the receiver operating characteristic curve (AUROC) of 0.83 and an adjusted (event rate = 4%) area under the precision-recall curve (AUPR) of 0.27, while the next best-tested method-regularized logistic regression-had an AUROC of 0.80 and an adjusted AUPR of 0.22. To ensure system interpretability, we applied a state-of-the-art prediction explainer that provided a ranked list of features contributing most to the prediction. Though it is currently difficult to compare machine learning-based early warning systems, a rudimentary comparison with published scores demonstrated that PICTURE is on par with state-of-the-art machine learning systems. To facilitate more robust comparisons and development of early warning systems in the future, we have released our variational autoencoder's code and weights so researchers can (a) test their models on data similar to our institution and (b) make their own synthetic datasets.
Towards scalable backscatter sensor mesh with decodable relay and distributed excitation

Backscatter communication, in which data is conveyed through reflecting excitation signals, has been advocated as a promising green technology for Internet of Things (IoT). Existing backscatter solutions however are mostly centralized, relying on a single excitation source, typically within one hop. Though recent works have demonstrated the viability of multi-hop backscatter, the excitation signal remains centralized, which attenuates quickly and fundamentally limits the communication scope. For long-range and high-quality communication, distributed excitations are expected and also naturally available as ambient signals (WiFi, BLE, cellular, FM, light, sound, etc.), albeit not being explored for boosting nearby tags for relaying. Given the existence of distributed excitation, a relay tag has to be decodable, i.e., be able to first decode its previous hop's information and then backscatter to the next hop with a boost from a nearby excitation whenever possible. In this paper, we present DecRel, a decodable tag relay solution towards a backscatter sensor mesh for universal and scalable deployment with distributed excitation. DecRel is also an innovative wireless sensor architecture for simultaneous sensing and relay. It incorporates a relay path that uses envelope detection for decoding, and a sensing path that converts its own sensor data into a baseband for amplitude-demodulation by the next hop tag's relay path. The two paths then backscatter their respective data to different frequencies to avoid interference. We have built a working DecRel tag prototype using FPGA, discrete components, and off-the-shelf analog devices. Our experiments show superior performance of DecRel as compared with the state-of-the-art non-decodable tag relay: specifically, a digital baseband's multi-hop throughput of up to 40Kbps (200x improvement), an analog baseband's equivalent multi-hop throughput of up to 768Kbps (3000x improvement), and a tag-to-tag distance of up to 4.8m (10x improvement) with a hop count of up to 6. DecRel tag consumes 337. 9μW of power using IC design.