Miracle-dz/UniFAR-FTUs huggingface dataset:
Iterative Global Similarity Points: A Robust Coarse-to-Fine Integration Solution for Pairwise 3D Point Cloud Registration

In this paper, we propose a coarse-to-fine integration solution inspired by the classical ICP algorithm, to pairwise 3D point cloud registration with two improvements of hybrid metric spaces (e.g., BSC feature and Euclidean geometry spaces) and globally optimal correspondences matching. First, we detect the keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to encode their local features. Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the globally optimal correspondences through optimizing the energy function by the Kuhn-Munkres algorithm and then calculate the transformation based on the correspondences. Finally, we iteratively refine the transformation between two point clouds by conducting optimal correspondences matching and transformation calculation in a mutually reinforcing manner, to achieve the coarse-to-fine registration under an unified framework. The proposed method is evaluated and compared to several state-of-the-art methods on selected challenging datasets with repetitive, symmetric and incomplete structures. Comprehensive experiments demonstrate that the proposed IGSP algorithm obtains good performance and outperforms the state-of-the-art methods in terms of both rotation and translation errors.
A Cooperative Quality-Aware Service Access System for Social Internet of Vehicles

Because of the enormous potential to guarantee road safety and improve driving experience, social Internet of Vehicle (SIoV) is becoming a hot research topic in both academic and industrial circles. As the ever-increasing variety, quantity, and intelligence of on-board equipment, along with the ever-growing demand for service quality of automobiles, the way to provide users with a range of security-related and user-oriented vehicular applications has become significant. This paper concentrates on the design of a service access system in SIoVs, which focuses on a reliability assurance strategy and quality optimization method. First, in lieu of the instability of vehicular devices, a dynamic access service evaluation scheme is investigated, which explores the potential relevance of vehicles by constructing their social relationships. Next, this work studies a trajectory-based interaction time prediction algorithm to cope with an unstable network topology and high rate of disconnection in SIoVs. At last, a cooperative quality-aware system model is proposed for service access in SIoVs. Simulation results demonstrate the effectiveness of the proposed scheme.
Time-Critical Reasoning: Representations and Application

We review the problem of time-critical action and discuss a reformulation that shifts knowledge acquisition from the assessment of complex temporal probabilistic dependencies to the direct assessment of time-dependent utilities over key outcomes of interest. We dwell on a class of decision problems characterized by the centrality of diagnosing and reacting in a timely manner to pathological processes. We motivate key ideas in the context of trauma-care triage and transportation decisions.
Game Theoretic Analysis for Joint Sponsored and Edge Caching Content Service Market

With a sponsored content scheme in a wireless network, a sponsored content service provider can pay to a network operator on behalf of the mobile users/subscribers to lower down the network subscription fees at the reasonable cost in terms of receiving some amount of advertisements. As such, content providers, network operators and mobile users are all actively motivated to participate in the sponsored content ecosystem. Meanwhile, in 5G cellular networks, caching technique is employed to improve content service quality, which stores potentially popular contents on edge networks nodes to serve mobile users. In this work, we propose the joint sponsored and edge caching content service market model. We investigate an interplay between the sponsored content service provider and the edge caching content service provider under the non-cooperative game framework. Furthermore, a three-stage Stackelberg game is formulated to model the interactions among the network operator, content service provider, and mobile users. Sub-game perfect equilibrium in each stage is analyzed by backward induction. The existence of Stackelberg equilibrium is validated by employing the bilevel optimization programming. Based on the game properties, we propose a sub-gradient based iterative algorithm, which ensures to converge to the Stackelberg equilibrium.
eDoctor: machine learning and the future of medicine

Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer‐aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.
The functional morphology of the musculature of squid (Loliginidae) arms and tentacles

The arms and tentacles of squid (Family Loliginidae: Sepioteuthis sepioidea (Blainville), Loligo pealei (LeSueur), Loligo plei (Blainville), Loliguncula brevis (Blainville)) do not possess the hardened skeletal elements or fluid‐filled cavities that typically provide skeletal support in other animals. Instead, these appendages are made up almost entirely of muscle. It is suggested here that the musculature serves as both the effector of movement and as the skeletal support system itself.
DIY absolute tele-colorimeter using a camera-projector system

Image-based reflectance measurement setups lower costs and increase the speed of reflectance acquisition. Unfortunately, consumer camera sensors are designed to produce aesthetically pleasing images, rather than faithfully capture the colors of a scene. We present a novel approach for colorimetric camera characterization, which exploits a commonly available projector as controllable light source, and accurately relates the camera sensor response to the known reflected radiance. The characterized camera can be effectively used as a 2D tele-colorimeter, suitable for image-based reflectance measurements, spectral prefiltering and spectral up-sampling for rendering, and to improve color accuracy in HDR imaging. We demonstrate our method in the context of radiometric compensation. Coupled with a gamut-mapping technique, it allows to seamlessly project images on almost any surface, including non-flat, colored or even textured ones.
Modified Seven-Level Pack U-Cell Inverter for Photovoltaic Applications

This paper proposes a modified conation of single-phase pack U-cell (PUC) multilevel inverter in which the output voltage has higher amplitude than the maximum dc link value used in the topology as a boost operation. The introduced inverter generates seven-level ac voltage at the output using two dc links and six semiconductor switches. Comparing to cascaded H-bridge and neutral point clamp multilevel inverters, the introduced multilevel inverter produces more voltage levels using less components. The proposed inverter is used in photovoltaic (PV) system where the green power comes from two separate PV panels connected to the dc links through dc–dc converters to draw the maximum power. Due to boost operation of this inverter, two different PV panels can combine and send their powers to the grid. Simulations and experimental tests are conducted to investigate the good dynamic performance of the inverter in grid-connected PV system.
Histone H2A‐H2B binding by Pol α in the eukaryotic replisome contributes to the maintenance of repressive chromatin

The eukaryotic replisome disassembles parental chromatin at DNA replication forks, but then plays a poorly understood role in the re‐deposition of the displaced histone complexes onto nascent DNA. Here, we show that yeast DNA polymerase α contains a histone‐binding motif that is conserved in human Pol α and is specific for histones H2A and H2B. Mutation of this motif in budding yeast cells does not affect DNA synthesis, but instead abrogates gene silencing at telomeres and mating‐type loci. Similar phenotypes are produced not only by mutations that displace Pol α from the replisome, but also by mutation of the previously identified histone‐binding motif in the CMG helicase subunit Mcm2, the human orthologue of which was shown to bind to histones H3 and H4. We show that chromatin‐derived histone complexes can be bound simultaneously by Mcm2, Pol α and the histone chaperone FACT that is also a replisome component. These findings indicate that replisome assembly unites multiple histone‐binding activities, which jointly process parental histones to help preserve silent chromatin during the process of chromosome duplication.
Joint Structured Models for Extraction from Overlapping Sources

We consider the problem of jointly training structured models for extraction from sources whose instances enjoy partial overlap. This has important applications like user-driven ad-hoc information extraction on the web. Such applications present new challenges in terms of the number of sources and their arbitrary pattern of overlap not seen by earlier collective training schemes applied on two sources. We present an agreement-based learning framework and alternatives within it to trade-off tractability, robustness to noise, and extent of agreement. We provide a principled scheme to discover low-noise agreement sets in unlabeled data across the sources. Through extensive experiments over 58 real datasets, we establish that our method of additively rewarding agreement over maximal segments of text provides the best trade-offs, and also scores over alternatives such as collective inference, staged training, and multi-view learning.
A Physiological Model-Based Study of Flow-Mediated Dilation in Peripheral Arteries Using Finger Photoplethysmogram Signal

Flow-mediated dilation measurement in the brachial artery using ultrasound imaging (FMD-US) is a common noninvasive procedure for endothelial function evaluation in the peripheral arteries. As FMD-US is operator-dependent and involves onerous equipment, its use has been mostly confined to research settings. In this paper, we propose to use the more accessible finger photoplethysmogram signal in conjunction with the FMD test (FMD-PPG) as a surrogate method. To this end, a tube-load physiological model of the upper arterial path in the arm is developed. Signals acquired from young and elderly subjects (N=20) are then investigated using model parameter estimation by the genetic algorithm. Our results show that the estimated parameter values for the elderly group in baseline are significantly different from that in the young group (p<0.05), and the parameter changes in cuff release interval are consistent with existing literature. Therefore, the presented FMD-PPG may hold a great potential in non-invasively assessing the endothelial function.
Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and Semidefinite Programming Synchronization

We consider the classic problem of establishing a statistical ranking of a set of n items given a set of inconsistent and incomplete pairwise comparisons between such items. Instantiations of this problem occur in numerous applications in data analysis (e.g., ranking teams in sports data), computer vision, and machine learning. We formulate the above problem of ranking with incomplete noisy information as an instance of the group synchronization problem over the group SO(2) of planar rotations, whose usefulness has been demonstrated in numerous applications in recent years. Its least squares solution can be approximated by either a spectral or a semidefinite programming (SDP) relaxation, followed by a rounding procedure. We perform extensive numerical simulations on both synthetic and real-world data sets, showing that our proposed method compares favorably to other algorithms from the recent literature. Existing theoretical guarantees on the group synchronization problem imply lower bounds on the largest amount of noise permissible in the ranking data while still achieving exact recovery. We propose a similar synchronization-based algorithm for the rank-aggregation problem, which integrates in a globally consistent ranking pairwise comparisons given by different rating systems on the same set of items. We also discuss the problem of semi-supervised ranking when there is available information on the ground truth rank of a subset of players, and propose an algorithm based on SDP which recovers the ranks of the remaining players. Finally, synchronization-based ranking, combined with a spectral technique for the densest subgraph problem, allows one to extract locally-consistent partial rankings, in other words, to identify the rank of a small subset of players whose pairwise comparisons are less noisy than the rest of the data, which other methods are not able to identify.
Nano-Cu Sintering Paste for High Power Devices Die Attach Applications

A nano-Cu pressureless sinterable paste was developed for joint formation applications. The material was further engineered in both chemistry and process for enhanced oxidation resistance. This oxidation resistance capability allowed the paste exhibited a shelf life of 6 months when stored at -10°C. The paste was insensitive to either sintering atmosphere or surface finish type, and consistently exhibited a joint shear strength around 13 MPa. Nano-Ag pastes were observed to be sensitive to surface finishes, and performed particularly poorly for OSP-OSP system. The shear strength of nano-Cu was sensitive to sintering temperature and time, with shear strength increased from 220°C/15 min, 220°C/30 min, to 240°C/15 min. The joint shear strength deteriorated with increasing TCT (-40°C/150°C) cycle number. After 3000 cycles, the ranking of shear strength can be shown below: Nano-Cu > Ag-N > Ag-H2 > Ag-H1 > Ag-K > Ag-D. The high Pb joint performed the best. The microstructure of Nano-Cu joint formed at 240°C/15 min sintering condition indicated a continuous sintering progress with increasing TCT cycles, with porosity diminishing and smooth area increasing. This strongly suggests that either a higher temperature or a longer time than 240°C/15 min is desired for improved sintering extent
A 31.8–40.8GHz Continuously Wide-Tuning VCO Based on Class-B Oscillator using Single Varactor and Inductor

A 31.8-40.8GHz continuously voltage-tuning LC-tank CMOS differential VCO is presented that has been designed by using only a pair of varactors and single symmetric inductor available from library, and adopting class-B oscillator. The push-pull complementary class-B oscillator is applied in the VCO to isolate LC-tank resonator from external and internal loads, and to produce more negative resistance, and thus resulting in wide frequency tuning range. The VCO shows 24.8% tuning range, −93.5dBc/Hz phase noise at 1MHz offset, and −14dBm output power with 50Ω load. The VCO is manufactured using 40nm CMOS process with consuming 20mW in oscillator circuit.
Consensus-Based Smart Grid State Estimation Algorithm

The distribution power subsystems are usually interconnected to each other, so the design of the interconnected optimal filtering algorithm for distributed state estimation is a challenging task. Driven by this motivation, this paper proposes a novel consensus filter based dynamic state estimation algorithm with its convergence analysis for modern power systems. The novelty of the scheme is that the algorithm is designed based on the mean squared error and semidefinite programming approaches. Specifically, the optimal local gain is computed after minimizing the mean squared error between the true and estimated states. The consensus gain is determined by a convex optimization process with a given suboptimal local gain. Furthermore, the convergence of the proposed scheme is analyzed after stacking all the estimation error dynamics. The Laplacian operator is used to represent the interconnected filter structure as a compact error dynamic for deriving the convergence condition of the algorithm. The developed approach is verified by using the renewable microgrid. It shows that the distributed scheme being explored is effective as it takes only 0.00004 seconds to properly estimate the system states and does not need to transmit the remote sensing signals to the central estimator.
Statistical modeling and visualization of localized prostate cancer

In this paper, a statistically significant master model of localized prostate cancer is developed with pathologically- proven surgical specimens to spatially guide specific points in the biopsy technique for a higher rate of prostate cancer detection and the best possible representation of tumor grade and extension. Based on 200 surgical specimens of the prostates, we have developed a surface reconstruction technique to interactively visualize in the clinically significant objects of interest such as the prostate capsule, urethra, seminal vesicles, ejaculatory ducts and the different carcinomas, for each of these cases. In order to investigate the complex disease pattern including the tumor distribution, volume, and multicentricity, we created a statistically significant master model of localized prostate cancer by fusing these reconstructed computer models together, followed by a quantitative formulation of the 3D finite mixture distribution. Based on the reconstructed prostate capsule and internal structures, we have developed a technique to align all surgical specimens through elastic matching. By labeling the voxels of localized prostate cancer by '1' and the voxels of other internal structures by '0', we can generate a 3D binary image of the prostate that is simply a mutually exclusive random sampling of the underlying distribution f cancer to gram of localized prostate cancer characteristics. In order to quantify the key parameters such as distribution, multicentricity, and volume, we used a finite generalized Gaussian mixture to model the histogram, and estimate the parameter values through information theoretical criteria and a probabilistic self-organizing mixture. Utilizing minimally-immersive and stereoscopic interactive visualization, an augmented reality can be developed to allow the physician to virtually hold the master model in one hand and use the dominant hand to probe data values and perform a simulated needle biopsy. An adaptive self- organizing vector quantization method is developed to determine the optimal locations of selective biopsies where maximum likelihood of cancer detection and the best possible representation of tumor grade and extension can be achieved theoretically, thus allowing a comprehensive analysis of pathological information. The preliminary results show that a statistical pattern of localized prostate cancer exists, and a better understanding of disease patterns associated with tumor volume, distribution, and multicentricity of prostate carcinoma can be obtained from the computerized master model.
Trust and Social Engineering in Human Robot Interaction: Will a Robot Make You Disclose Sensitive Information, Conform to Its Recommendations or Gamble?

Robots such as information security and overtrust in them are gaining increasing relevance. This research aims at giving an insight into how trust toward robots could be exploited for the purpose of social engineering. Drawing on Mitnick's model, a well-known social engineering framework, an interactive scenario with the humanoid robot iCub was designed to emulate a social engineering attack. At first, iCub attempted to collect the kind of personal information usually gathered by social engineers by asking a series of private questions. Then, the robot tried to develop trust and rapport with participants by offering reliable clues during a treasure hunt game. At the end of the treasure hunt, the robot tried to exploit the gained trust in order to make participants gamble the money they won. The results show that people tend to build rapport with and trust toward the robot, resulting in the disclosure of sensitive information, conformation to its suggestions and gambling.
A calculus for costed computations

We develop a version of the pi-calculus, picost, where channels are interpreted as resources which have costs associated with them. Code runs under the financial responsibility of owners; they must pay to use resources, but may profit by providing them. We provide a proof methodology for processes described in picost based on bisimulations. The underlying behavioural theory is justified via a contextual characterisation. We also demonstrate its usefulness via examples.
Environmentally induced changes to brain morphology predict cognitive performance

The relationship between the size and structure of a species' brain and its cognitive capacity has long interested scientists. Generally, this work relates interspecific variation in brain anatomy with performance on a variety of cognitive tasks. However, brains are known to show considerable short-term plasticity in response to a range of social, ecological and environmental factors. Despite this, we have a remarkably poor understanding of how this impacts on an animal's cognitive performance. Here, we non-invasively manipulated the relative size of brain regions associated with processing visual and chemical information in fish (the optic tectum and olfactory bulbs, respectively). We then tested performance in a cognitive task in which information from the two sensory modalities was in conflict. Although the fish could effectively use both visual and chemical information if presented in isolation, when they received cues from both modalities simultaneously, those with a relatively better developed optic tectum showed a greater reliance on visual information, while individuals with relatively better developed olfactory bulbs showed a greater reliance on chemical information. These results suggest that short-term changes in brain structure, possibly resulting from an attempt to minimize the costs of developing unnecessary but energetically expensive brain regions, may have marked effects on cognitive performance. This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.