Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Robust Depth-Pressure Estimation Algorithm for a Vision-Based Breast Self-Examination Guidance System.

In the case of breast cancer, as with most cancers, early detection can significantly improve a person's chances of survival. This makes it important for there to be an effective and accessible means of regularly checking for manifestations of the disease. A vision-based guidance system (VBGS) for breast self-examination (BSE) is one way to improve a person's ability to detect the cancerous systems. In response to this need, this study sought to develop a depth-pressure estimation algorithm for the proposed VBGS. A large number of BSE videos were used to train the model, and these samples were segmented according to breast size, which was found to be a differentiation factor in the depth-pressure estimation. The result was an algorithm that was applicable for universal use. In addition to these, several feature extraction schemes were tested with the objective of making the algorithm functional on average technology. It was found that Law's Textures Histogram and Local Binary Pattern Global Histogram were the most effective in estimating pressure using visual data. Moreover, combinations of the two schemes further improved the accuracy of the model in estimation. The resulting algorithm was thereby fit to be used by the average consumer.
A characterisation of system-wide propagation in the malware landscape

System-wide propagation is frequently observed in malware, and there are several resources, like blog posts and similar, that detail some of the techniques used. However, there is currently no thorough study on the subject at large, and the full extent of system-wide malware propagation remains unknown. In this paper, we perform a systematic study on many real-world samples to comprehensively characterise system-wide propagation within the malware landscape and the goal is to use detailed and precise analyses to derive high-level views. We achieve this by collecting a diverse set of malware samples, analyse them in our Minerva malware analysis framework and then extract vast amounts of statistics about the results. We use these results to provide an in-depth discussion centred on four main research questions.
Combined Task and Action Learning from Human Demonstrations for Mobile Manipulation Applications

Learning from demonstrations is a promising paradigm for transferring knowledge to robots. However, learning mobile manipulation tasks directly from a human teacher is a complex problem as it requires learning models of both the overall task goal and of the underlying actions. Additionally, learning from a small number of demonstrations often introduces ambiguity with respect to the intention of the teacher, making it challenging to commit to one model for generalizing the task to new settings. In this paper, we present an approach to learning flexible mobile manipulation action models and task goal representations from teacher demonstrations. Our action models enable the robot to consider different likely outcomes of each action and to generate feasible trajectories for achieving them. Accordingly, we leverage a probabilistic framework based on Monte Carlo tree search to compute sequences of feasible actions imitating the teacher intention in new settings without requiring the teacher to specify an explicit goal state. We demonstrate the effectiveness of our approach in complex tasks carried out in real-world settings.
The power of $d$-thinning in load balancing

In the classical balls-and-bins model, $m$ balls are allocated into $n$ bins one by one uniformly at random. In this note, we consider the $d$-thinning variant of this model, in which the process is regulated in an on-line fashion as follows. For each ball, after a random allocation bin has been generated, an overseer may decide, based on all previous history, whether to accept or reject this allocation. However, one of every $d$ consecutive suggested allocations must be accepted. The \emph{maximum load} of the model is the number of balls in the most loaded bin. We show that after $\Theta(n)$ balls have been allocated, the least maximum load achievable with high probability is $(d+o(1))(d\log n/\log\log n)^{1/d}$. This is in contrast with the related two-choice model, in which the number of alternative choices offered to the overseer merely affects the achievable maximum load by a multiplicative constant.
Optimization of Split Keyboard Design for Touchscreen Devices

ABSTRACT While many large touchscreen devices have a split keyboard option to enable two-thumb typing, there are significant differences in the design which affect users’ text entry rate. A mixed-integer programming model is developed to optimize key-to-thumb assignment with the objective of minimizing the expected time to type a character. Computer simulations are conducted to determine the optimal key dimension under different values of the Fitts’ law’s slope coefficient, typing error rate, and alternate-hand advantage phenomenon. The results show that text entry rate and the optimal key-to-thumb assignment depend on key dimensions, user’s speed-accuracy profile, and the level of alternate-hand advantage. The optimal keyboard is proposed. To validate the analytical findings, an empirical study is conducted with eighteen users and six different keyboards in terms of key dimensions and typing zone. Empirical results report between 7% and 18% improvement in the text entry rate over the other split keyboards tested.
Investigation of 13C Isotopic Fractionation of CCH in Two Starless Cores: L1521B and L134N

We carried out observations of CCH and its two 13C isotopologues, 13CCH and C13CH, in the 84–88 GHz band toward two starless cores, L1521B and L134N (L183), using the Nobeyama 45 m radio telescope. We detected C13CH with a signal-to-noise ratio of 4, whereas no line of 13CCH was detected in either of the dark clouds. The column densities of the normal species were derived to be (1.66 ± 0.18) × 1014 cm−2 and (7.3 ± 0.9) × 1013 cm−2 (1σ) in L1521B and L134N, respectively. The column density ratios of N(C13CH)/N(13CCH) were calculated to be >1.1 and >1.4 in L1521B and L134N, respectively. The characteristic that 13CCH is less abundant than C13CH is likely common for dark clouds. Moreover, we find that the 12C/13C ratios of CCH are much higher than those of HC3N in L1521B by more than a factor of 2, as well as in Taurus Molecular Cloud-1 (TMC-1). In L134N, the differences in the 12C/13C ratios between CCH and HC3N seem to be smaller than those in L1521B and TMC-1. We discuss the origins of the 13C isotopic fractionation of CCH and investigate possible routes that cause the significantly high 12C/13C ratio of CCH especially in young dark clouds, with the help of chemical simulations. The high 12C/13C ratios of CCH seem to be caused by reactions between hydrocarbons (e.g., CCH, C2H2, l-C3H and c-C3H) and C+.
A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues

Wireless networks have been widely deployed for many Internet-of-Things (IoT) applications, like smart cities and precision agriculture. Low Power Wide Area Networking (LPWAN) is an emerging IoT networking paradigm to meet three key requirements of IoT applications, i.e., low cost, large scale deployment and high energy efficiency. Among all available LPWAN technologies, LoRa networking has attracted much attention from both academia and industry, since it specifies an open standard and allows us to build autonomous LPWAN networks without any third-party infrastructure. Many LoRa networks have been developed recently, e.g., managing solar plants in Carson City, Nevada, USA and power monitoring in Lyon and Grenoble, France. However, there are still many research challenges to develop practical LoRa networks, e.g., link coordination, resource allocation, reliable transmissions and security. This article provides a comprehensive survey on LoRa networks, including the technical challenges of deploying LoRa networks and recent solutions. Based on our detailed analysis of current solutions, some open issues of LoRa networking are discussed. The goal of this survey paper is to inspire more works on improving the performance of LoRa networks and enabling more practical deployments.
All twist and no bend makes raft edges splay: Spontaneous curvature of domain edges in colloidal membranes

Geometric frustration of colloidal membrane textures drives spontaneous edge curvature and nonconvex shapes of raft domains. Using theory and experiments, we study the interface between two immiscible domains in a colloidal membrane composed of rigid rods of different lengths. Geometric considerations of rigid rod packing imply that a domain of sufficiently short rods in a background membrane of long rods is more susceptible to twist than the inverse structure, a long-rod domain in a short-rod membrane. The midplane tilt at the interdomain edge forces splay, which, in turn, manifests as spontaneous edge curvature with energetics controlled by the length asymmetry of constituent rods. A thermodynamic model of such tilt-curvature coupling at interdomain edges explains a number of experimental observations, including annularly shaped long-rod domains, and a nonmonotonic dependence of edge twist on domain radius. Our work shows how coupling between orientational and compositional degrees of freedom in two-dimensional fluids gives rise to complex shapes of fluid domains, analogous to shape transitions in 3D fluid vesicles.
Don’t Just Scratch the Surface: Enhancing Word Representations for Korean with Hanja

We propose a simple yet effective approach for improving Korean word representations using additional linguistic annotation (i.e. Hanja). We employ cross-lingual transfer learning in training word representations by leveraging the fact that Hanja is closely related to Chinese. We evaluate the intrinsic quality of representations learned through our approach using the word analogy and similarity tests. In addition, we demonstrate their effectiveness on several downstream tasks, including a novel Korean news headline generation task.
Tvarak: Software-managed hardware offload for DAX NVM storage redundancy

Tvarak efficiently implements system-level redundancy for direct-access (DAX) NVM storage. Production storage systems complement device-level ECC (which covers media errors) with system-checksums and cross-device parity. This system-level redundancy enables detection of and recovery from data corruption due to device firmware bugs (e.g., reading data from the wrong physical location). Direct access to NVM penalizes software-only implementations of system-level redundancy, forcing a choice between lack of data protection or significant performance penalties. Offloading the update and verification of system-level redundancy to Tvarak, a hardware controller co-located with the last-level cache, enables efficient protection of data from such bugs in memory controller and NVM DIMM firmware. Simulation-based evaluation with seven data-intensive applications shows Tvarak's performance and energy efficiency. For example, Tvarak reduces Redis set-only performance by only 3%, compared to 50% reduction for a state-of-the-art software-only approach.
Matrix embedding method in match for session-based recommendation

Session based model is widely used in recommend system. It use the user click sequence as input of a Recurrent Neural Network (RNN), and get the output of the RNN network as the vector embedding of the session, and use the inner product of the vector embedding of session and the vector embedding of the next item as the score that is the metric of the interest to the next item. This method can be used for the "match" stage for the recommendation system whose item number is very big by using some index method like KD-Tree or Ball-Tree and etc. But this method repudiate the variousness of the interest of user in a session. We generated the model to modify the vector embedding of session to a symmetric matrix embedding, that is equivalent to a quadratic form on the vector space of items. The score is builded as the value of the vector embedding of next item under the quadratic form. The eigenvectors of the symmetric matrix embedding corresponding to the positive eigenvalues are conjectured to represent the interests of user in the session. This method can be used for the "match" stage also. The experiments show that this method is better than the method of vector embedding.
Generator evaluator-selector net: a modular approach for panoptic segmentation

In machine learning and other fields, suggesting a good solution to a problem is usually a harder task than evaluating the quality of such a solution. This asymmetry is the basis for a large number of selection oriented methods that use a generator system to guess a set of solutions and an evaluator system to rank and select the best solutions. This work examines the use of this approach to the problem of image segmentation. The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects and distinct regions in the image and an evaluator net that chooses the best segments to be merged into the segmentation map. The result is a trial and error evolutionary approach in which a generator that guesses segments with low average accuracy, but with wide variability, can still produce good results when coupled with an accurate evaluator. Generating and evaluating each segment separately is essential in this case since it demands exponentially fewer guesses compared to a system that guesses and evaluates the full segmentation map in each try. Another form of modularity used in this system is separating the segmentation and classification into independent neural nets. This allows the segmentation to be class agnostic and hence capable of segmenting unfamiliar categories that were not part of the training set. The method was examined on the COCO Panoptic segmentation benchmark and gave competitive results to the standard semantic segmentation and instance segmentation methods.
A Two-zone Model for Blazar Emission: Implications for TXS 0506+056 and the Neutrino Event IceCube-170922A

A high-energy muon neutrino event, IceCube-170922A, was recently discovered in both spatial and temporal coincidence with a gamma-ray flare of the blazar TXS 0506+056. It has been shown with standard one-zone models that neutrinos can be produced in the blazar jet via hadronic interactions, but with a flux that is mostly limited by the X-ray data. In this work, we explore the neutrino production from TXS 0506+056 by invoking two physically distinct emission zones in the jet, with an inner blob inside of or close to the broad-line region (BLR) and an outer one well beyond the BLR. Using the Doppler-boosted radiation of the BLR as the target photon field, the inner zone accounts for the neutrino and gamma-ray emission via pγ interactions and inverse Compton scattering, respectively, while the outer zone produces the optical and X-ray emission via synchrotron and synchrotron self-Compton processes. The different conditions of the two zones allow us to suppress the X-ray emission from the electromagnetic cascade, and set a much higher upper limit on the muon neutrino flux (i.e., ∼10−11 erg cm−2 s−1) than in one-zone models. We compare our scenario in detail with one-zone models discussed in the literature, and argue that differentiating between such scenarios will become possible with next-generation neutrino telescopes, such as IceCube-Gen2.
IoT System for Air Pollutants Assessment in Underground Infrastructures

This paper describes an IoT system capable of capturing information about hazardous working environments and analyzes the health risks associated with increased air pollution. The case study regards the underground transportation systems, which are key components in commuting networks of large cities, providing fast and affordable transport for urban communities. First, a risk analysis of the categories of people working in this space or commuting through the city using the subway was performed. Furthermore, the situation in other similar environments and the main sources of pollution was analyzed. By designing and implementing a WSN system could be managed to gather air quality data and process the sensors measurements. The experimental results consist of a predictive model of PMs emissions which can aid in mitigating air pollution.
Robust Importance-Weighted Cross-Validation Under Sample Selection Bias

Cross-validation under sample selection bias can, in principle, be done by importance-weighting the empirical risk. However, the importance-weighted risk estimator produces suboptimal hyperparameter estimates in problem settings where large weights arise with high probability. We study its sampling variance as a function of the training data distribution and introduce a control variate to increase its robustness to problematically large weights.
No-Reference Light Field Image Quality Assessment Based on Micro-Lens Image

Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality depends on both angular consistency and spatial quality. However, few existing LF-IQA methods concentrate on effects caused by angular inconsistency. Especially, no-reference methods lack effective utilization of 2D angular information. In this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can simultaneously record the angular information in both horizontal and vertical directions. Since the MLI contains 2D angular information, we propose a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI). Specifically, we first utilize Global Entropy Distribution (GED) and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and then pool them together to measure angular consistency. In addition, the information entropy of SubAperture Image (SAI) is adopted to measure spatial quality. Extensive experimental results show that LF-QMLI achieves the state-of-the-art performance.
Campana points of bounded height on vector group compactifications

We initiate a systematic quantitative study of subsets of rational points that are integral with respect to a weighted boundary divisor on Fano orbifolds. We call the points in these sets Campana points. Earlier work of Campana and subsequently Abramovich shows that there are several reasonable competing definitions for Campana points. We use a version that delineates well different types of behavior of points as the weights on the boundary divisor vary. This prompts a Manin‐type conjecture on Fano orbifolds for sets of Campana points that satisfy a klt (Kawamata log terminal) condition. By importing work of Chambert‐Loir and Tschinkel to our setup, we prove a log version of Manin's conjecture for klt Campana points on equivariant compactifications of vector groups.
Convergence of the conjugate gradient method with unbounded operators

In the framework of inverse linear problems on infinite-dimensional Hilbert space, we prove the convergence of the conjugate gradient iterates to an exact solution to the inverse problem in the most general case where the self-adjoint, non-negative operator is unbounded and with minimal, technically unavoidable assumptions on the initial guess of the iterative algorithm. The convergence is proved to always hold in the Hilbert space norm (error convergence), as well as at other levels of regularity (energy norm, residual, etc.) depending on the regularity of the iterates. We also discuss, both analytically and through a selection of numerical tests, the main features and differences of our convergence result as compared to the case, already available in the literature, where the operator is bounded.
A Morpho-Syntactically Informed LSTM-CRF Model for Named Entity Recognition

We propose a morphologically informed model for named entity recognition, which is based on LSTM-CRF architecture and combines word embeddings, Bi-LSTM character embeddings, part-of-speech (POS) tags, and morphological information. While previous work has focused on learning from raw word input, using word and character embeddings only, we show that for morphologically rich languages, such as Bulgarian, access to POS information contributes more to the performance gains than the detailed morphological information. Thus, we show that named entity recognition needs only coarse-grained POS tags, but at the same time it can benefit from simultaneously using some POS information of different granularity. Our evaluation results over a standard dataset show sizeable improvements over the state-of-the-art for Bulgarian NER.
Physics Informed Data Driven model for Flood Prediction: Application of Deep Learning in prediction of urban flood development

Flash floods in urban areas occur with increasing frequency. Detecting these floods would greatlyhelp alleviate human and economic losses. However, current flood prediction methods are eithertoo slow or too simplified to capture the flood development in details. Using Deep Neural Networks,this work aims at boosting the computational speed of a physics-based 2-D urban flood predictionmethod, governed by the Shallow Water Equation (SWE). Convolutional Neural Networks(CNN)and conditional Generative Adversarial Neural Networks(cGANs) are applied to extract the dy-namics of flood from the data simulated by a Partial Differential Equation(PDE) solver. Theperformance of the data-driven model is evaluated in terms of Mean Squared Error(MSE) andPeak Signal to Noise Ratio(PSNR). The deep learning-based, data-driven flood prediction modelis shown to be able to provide precise real-time predictions of flood development
Copulas and Preserver Problems

Preserver problems concern the characterization of operators on general spaces that leave invariant some categories of subsets or ratios. The most known in the mathematical literature are those of linear preserver problems (LPP) which date back to the ninth century. Here, we treat the preserver problem in the recent and emerging field of copulas. Precisely, we prove that \emph{copula property} is preserved uniquely under increasing transformations.
A New Small-World IoT Routing Mechanism Based on Cayley Graphs

An increasing number of low-power Internet of Things (IoT) devices will be widely deployed in the near future. Considering the short-range communication of low-power devices, multihop transmissions will become an important transmission mechanism in IoT networks. It is a crucial for low-power devices to transmit data over long distances via multihop in a low-delay and reliable way. The small-world characteristics of networks indicate that the network has an advantage of a small average shortest-path length (ASL) and a high average clustering coefficient (ACC). In this article, a new IoT routing mechanism considering small-world characteristics is proposed to reduce the delay and improve the reliability. The ASL and ACC are derived for the performance analysis of small-world characteristics in IoT networks based on Cayley graphs. Besides, the reliability and delay models are proposed for small-world IoT based on Cayley graphs (SWITCH). The simulation results demonstrate that SWITCH has lower delay and better reliability than that of conventional nearest neighboring routing (NNR). Moreover, the maximum delay of SWITCH is reduced by 50.6% compared with that by NNR.