Miracle-dz/UniFAR-FTUs huggingface dataset:
StyleCam: interactive stylized 3D navigation using integrated spatial & temporal controls

This paper describes StyleCam, an approach for authoring 3D viewing experiences that incorporate stylistic elements that are not available in typical 3D viewers. A key aspect of StyleCam is that it allows the author to significantly tailor what the user sees and when they see it. The resulting viewing experience can approach the visual richness and pacing of highly authored visual content such as television commercials or feature films. At the same time, StyleCam allows for a satisfying level of interactivity while avoiding the problems inherent in using unconstrained camera models. The main components of StyleCam are camera surfaces which spatially constrain the viewing camera; animation clips that allow for visually appealing transitions between different camera surfaces; and a simple, unified, interaction technique that permits the user to seamlessly and continuously move between spatial-control of the camera and temporal-control of the animated transitions. Further, the user's focus of attention is always kept on the content, and not on extraneous interface widgets. In addition to describing the conceptual model of StyleCam, its current implementation, and an example authored experience, we also present the results of an evaluation involving real users.
Connectome : A MATLAB toolbox for mapping and imaging of brain

We have developed a MATLAB-based toolbox, eConnectome (electrophysiological connectome), for mapping and imaging functional connectivity at both the scalp and cortical levels from the electroencephalogram (EEG), as well as from the electrocorticogram (ECoG). Graphical user interfaces were designed for interactive and intuitive use of the toolbox. Major functions of eConnectome include EEG/ECoG preprocessing, scalp spatial mapping, cortical source estimation, connectivity analysis, and visualization. Granger causality measures such as directed transfer function and adaptive directed transfer function were implemented to estimate the directional interactions of brain functional networks,
The Power of Social Media for Marketing Activities to Boost Brand Love of Local Products

It has become increasingly difficult for local brands to compete with global brands, as most foreign brands are perceived as better than local brands. As such, local companies were highly suggested to change their public perception through appropriate marketing communications. Social media for business purposes can be optimized through social media marketing activities (SMMA) to shape positive customer attitudes and behaviors. This study aimed to examine the influence of SMMA on customer love for local brands and the effect of SMMA on increasing brand awareness and image as two essential antecedents for creating brand love. This research design used a survey and involved 200 customers and social media visitors of local brands. Data was analyzed using the WarpPLS software. The findings showed that SMMA could increase customer love for local brands through brand awareness and brand image. Research also found that SMMA can directly influence brand love. The influence of SMMA on brand awareness was more powerful than brand image. From a customer perception perspective, brand awareness and brand image were important conditions for strengthening brand love. The implication of this research is that local product companies should design and manage their marketing activities in social media based on the important criteria from the customer’s perspective. Keywords: social media marketing activities, brand awareness, brand image, brand love, local product
Gradient-Based Quantitative Image Reconstruction in Ultrasound-Modulated Optical Tomography: First Harmonic Measurement Type in a Linearised Diffusion Formulation

Ultrasound-modulated optical tomography is an emerging biomedical imaging modality which uses the spatially localised acoustically-driven modulation of coherent light as a probe of the structure and optical properties of biological tissues. In this work we begin by providing an overview of forward modelling methods, before deriving a linearised diffusion-style model which calculates the first-harmonic modulated flux measured on the boundary of a given domain. We derive and examine the correlation measurement density functions of the model which describe the sensitivity of the modality to perturbations in the optical parameters of interest. Finally, we employ said functions in the development of an adjoint-assisted gradient based image reconstruction method, which ameliorates the computational burden and memory requirements of a traditional Newton-based optimisation approach. We validate our work by performing reconstructions of optical absorption and scattering in two- and three-dimensions using simulated measurements with 1% proportional Gaussian noise, and demonstrate the successful recovery of the parameters to within ±5% of their true values when the resolution of the ultrasound raster probing the domain is sufficient to delineate perturbing inclusions.
Radical trachelectomy versus radical hysterectomy for the treatment of early cervical cancer: a systematic review

Objective. To assess the efficacy and safety of radical trachelectomy (RT) and radical hysterectomy (RH) for patients with early cervical cancer. Design. Systematic review with meta‐analysis. Population. Women who had early cervical cancer. Methods. Prospective controlled clinical trials comparing RT with RH were identified using a predefined search strategy. Recurrence, five‐year recurrence‐free survival rate, five‐year overall survival rate, postoperative mortality, intraoperative and postoperative complications between the two operations were compared by using the methods provided by the Cochrane Handbook for Systematic Reviews of Interventions. Results. Three controlled clinical trials involving 587 participants were included. Meta‐analysis showed that there was no significant difference between the two groups in recurrence rate [1.38; 95% confidence interval (CI) 0.58–3.28, p=0.47], five‐year recurrence‐free survival rate (1.17; 95% CI 0.54–2.53, p=0.69), five‐year overall survival rate (0.86; 95% CI 0.30–2.43, p=0.78), postoperative mortality (1.14; 95% CI 0.42–3.11, p=0.80), intraoperative complications (1.66; 95% CI 0.11–25.28, p=0.72), postoperative complications (0.52; 95% CI 0.11–2.48, p=0.41), blood transfusion (0.29; 95% CI 0.06–1.36, p=0.12) and number of harvested lymph nodes. However, RT, compared with RH, reduced blood loss and shortened duration to normal urine residual volume and postoperative hospital stay. Moreover, RT may achieve to normal conception rates, while RH makes patients sterile. Conclusions. Radical trachelectomy has similar efficacy and safety to RH as the surgical treatment for early cervical cancer. Moreover, it reduced blood loss and shortened the duration to normal urine residual volumes and postoperative hospital stay. Radical trachelectomy can be used to treat early stage cervical cancer as an alternative operation for patients who wish to preserve fertility.
Precise and compact modular procedure summaries for heap manipulating programs

We present a strictly bottom-up, summary-based, and precise heap analysis targeted for program verification that performs strong updates to heap locations at call sites. We first present a theory of heap decompositions that forms the basis of our approach; we then describe a full analysis algorithm that is fully symbolic and efficient. We demonstrate the precision and scalability of our approach for verification of real C and C++ programs.
Adaptive Dynamic Inversion via Time-Scale Separation

This paper presents an adaptive dynamic inversion method for uncertain nonaffine-in-control systems. The adaptive dynamic inversion controller is defined as a solution of a fast dynamical equation, which achieves time-scale separation between a state predictor and the controller dynamics. Lyapunov based adaptive laws ensure that the state predictor tracks the actual nonlinear system with bounded errors. As a result, the system state tracks the reference model with bounded errors. Benefits of the proposed design method are demonstrated using Van der Pol dynamics with nonlinear control input
Defending mechanisms against false-data injection attacks in the power system state estimation

Accurate state estimation is of critical importance to maintain normal operating conditions of smart power grids. To ensure the integrity of state estimation, the current power grid systems use bad data detection (BDD) system to filter faulty measurements caused by device malfunctions or malicious attacks, etc. However, recent research shows that carefully synthesized false-data injection attacks can bypass the BDD system and thus introduce arbitrary errors to state estimates. In this paper, we investigate defending mechanisms against false-data injection attacks. Through protecting carefully selected meter measurements, no false-data injection attack can be launched to compromise any set of state estimates. We first derive a necessary and sufficient condition to select the protection measurements and analyze the properties of the optimal solution which protects the state estimates with minimum number of measurements. Based on the arguments, both optimal and reduced-complexity suboptimal algorithms are proposed. In particular, the suboptimal algorithm allows the system operator to protect the state estimates in arbitrary sequence according to their relative importance. Analysis and experiments show that it significantly reduces computational complexity, while yielding negligible performance degradation compared with the optimal algorithm. Our results here may find applications in the security upgrade projects of our current large-scale electrical power system towards smart power grids.
Transition-based RRT for path planning in continuous cost spaces

This paper presents a new method called Transition-based RRT (T-RRT) for path planning in continuous cost spaces. It combines the exploration strength of the RRT algorithm that rapidly grow random trees toward unexplored regions of the space, with the efficiency of stochastic optimization methods that use transition tests to accept or to reject a new potential state. This planner also relies on the notion of minimal work path that gives a quantitative way to compare path costs. The method also integrates self tuning of a parameter controlling its exploratory behavior. It yields to solution paths that efficiently follow low cost valleys and the saddle points of the cost space. Simulation results show that the method can be applied to a large set of applications including terrain costmap motions or planning low cost motions for free flying or articulated robots.
Single-spin stochastic optical reconstruction microscopy

Significance Mastering nanoscopic quantum systems promises applications in quantum information processing (QIP) and nanoscale metrology. However, the spatial density of individually addressable systems is not only limited by their size, but rather by the scale needed for control and readout. This affects upscaling of QIP devices and limits nanoscale sensors to be scanning probe devices with serial data acquisition. Here, we simultaneously apply parallel nanoscale microscopy and spin resonance methods on ensembles of optically addressable spins. The latter have been applied as qubits and nanoscale quantum sensors on the single level. In contrast, our method allows parallel individual addressing inside ensembles of qubits/sensors with nanometer distances. This promises a vast speedup for sensing applications compared with scanning probe methods. We experimentally demonstrate precision addressing of single-quantum emitters by combined optical microscopy and spin resonance techniques. To this end, we use nitrogen vacancy (NV) color centers in diamond confined within a few ten nanometers as individually resolvable quantum systems. By developing a stochastic optical reconstruction microscopy (STORM) technique for NV centers, we are able to simultaneously perform sub–diffraction-limit imaging and optically detected spin resonance (ODMR) measurements on NV spins. This allows the assignment of spin resonance spectra to individual NV center locations with nanometer-scale resolution and thus further improves spatial discrimination. For example, we resolved formerly indistinguishable emitters by their spectra. Furthermore, ODMR spectra contain metrology information allowing for sub–diffraction-limit sensing of, for instance, magnetic or electric fields with inherently parallel data acquisition. As an example, we have detected nuclear spins with nanometer-scale precision. Finally, we give prospects of how this technique can evolve into a fully parallel quantum sensor for nanometer resolution imaging of delocalized quantum correlations.
Analysis and prospects of automobile intelligent assisted driving characteristics based on FPGA technology

This article provides a comprehensive exploration of the pivotal role that field-programmable gate arrays (FPGAs) play in the advancement of autonomous driving technology. FPGAs, which made their debut in the early 1980s, have emerged as a crucial component in this field, owing to their robust parallel processing capabilities, real-time data analysis capabilities, and exceptional customizability. With the ever-increasing demand for autonomous driving solutions, the adoption of FPGAs has become indispensable in meeting the requirements for high-speed data processing and instantaneous response times. Within these pages, we delve into the significant role that FPGAs assume in elevating intelligent driving systems, offering a deep dive into this subject through meticulous case studies and technical insights. This article casts a spotlight on several compelling instances where FPGAs shine, notably in adaptive cruise systems, obstacle recognition, automatic emergency braking, and intelligent parking assistance, achieved through seamless integration with YOLO technology. These real-world examples serve to underscore the pivotal role that FPGAs play in ensuring road safety and propelling technological advancement in the realm of autonomous driving.
Proceedings of the 5th Symposium on Operating Systems Design and Implementation Resource Overbooking and Application Profiling in Shared Hosting Platforms

In this paper, we present techniques for provisioning CPU and network resources in shared hosting platforms running potentially antagonistic third-party applications. The primary contribution of our work is to demonstrate the feasibility and benefits of overbooking resources in shared platforms, to maximize the platform yield: the revenue generated by the available resources. We do this by first deriving an accurate estimate of application resource needs by profiling applications on dedicated nodes, and then using these profiles to guide the placement of application components onto shared nodes. By overbooking cluster resources in a controlled fashion , our platform can provide performance guarantees to applications even when overbooked, and combine these techniques with commonly used QoS resource allocation mechanisms to provide application isolation and performance guarantees at run-time. When compared to provi-sioning based on the worst-case, the efficiency (and consequently revenue) benefits from controlled overbooking of resources can be dramatic. Specifically, experiments on our Linux cluster implementation indicate that overbook-ing resources by as little as 1% can increase the utilization of the cluster by a factor of two, and a 5% overbook-ing yields a 300-500% improvement, while still providing useful resource guarantees to applications.
A High-Resolution Single Nucleotide Polymorphism Genetic Map of the Mouse Genome

High-resolution genetic maps are required for mapping complex traits and for the study of recombination. We report the highest density genetic map yet created for any organism, except humans. Using more than 10,000 single nucleotide polymorphisms evenly spaced across the mouse genome, we have constructed genetic maps for both outbred and inbred mice, and separately for males and females. Recombination rates are highly correlated in outbred and inbred mice, but show relatively low correlation between males and females. Differences between male and female recombination maps and the sequence features associated with recombination are strikingly similar to those observed in humans. Genetic maps are available from http://gscan.well. ox.ac. uk/#genetic_map and as supporting information to this publication.
A microphysical parameterization of aqSOA and sulfate formation in clouds

Sulfate and secondary organic aerosol (cloud aqSOA) can be chemically formed in cloud water. Model implementation of these processes represents a computational burden due to the large number of microphysical and chemical parameters. Chemical mechanisms have been condensed by reducing the number of chemical parameters. Here an alternative is presented to reduce the number of microphysical parameters (number of cloud droplet size classes). In‐cloud mass formation is surface and volume dependent due to surface‐limited oxidant uptake and/or size‐dependent pH. Box and parcel model simulations show that using the effective cloud droplet diameter (proportional to total volume‐to‐surface ratio) reproduces sulfate and aqSOA formation rates within ≤30% as compared to full droplet distributions; other single diameters lead to much greater deviations. This single‐class approach reduces computing time significantly and can be included in models when total liquid water content and effective diameter are available.
Land cover and land use change

Climate can affect and be affected by changes in land cover (the physical features that cover the land such as trees or pavement) and land use (human management and activities on land, such as mining or recreation). A forest, for instance, would likely include tree cover but could also include areas of recent tree removals currently covered by open grass areas. Land cover and use are inherently coupled: changes in land-use practices can change land cover, and land cover enables specific land uses. Understanding how land cover, use, condition, and management vary in space and time is challenging.
Incremental Support Vector Learning: Analysis, Implementation and Applications

Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen.
Screening for Depression Among Pregnant and Postpartum Women

The purpose of this study was to examine the screening of depression among pregnant and postpartum participants in a community-based program. This cross-sectional study used archival data from 98 women participating in a community-based visiting nurse program in a midwestern U.S. city. Depression screening was accomplished using the Center for Epidemiologic Studies Depression scale (CES-D) and Edinburgh Postnatal Depression scale (EPDS); both instruments ask respondents to answer questions regarding their mood during the past week. The CES-D identified more pregnant and postpartum women as depressed than did the EPDS. A standard regression analysis using previous pregnancies, history of depression, married versus nonmarried, presence of support, and breast-feeding as predictor variables did not produce statistically significant findings for predicting depression among the pregnant and postpartum women in this study. This finding underscores the value of brief depression screening instruments for nurses working with pregnant and postpartum women.
Ageing Voices: The Effect of Changes in Voice Parameters on ASR Performance

With ageing, human voices undergo several changes which are typically characterized by increased hoarseness and changes in articulation patterns. In this study, we have examined the effect on Automatic Speech Recognition (ASR) and found that the Word Error Rates (WER) on older voices is 10% absolute higher compared to those of adult voices. Subsequently, we compared several voice source parameters including fundamental frequency, jitter, shimmer, harmonicity, and cepstral peak prominence of adult and older males. Several of these parameters show statistically significant difference for the two groups. However, artificially increasing jitter and shimmer measures do not effect the ASR accuracies significantly. Artificially lowering the fundamental frequency degrades the ASR performance marginally but this drop in performance can be overcome to some extent using Vocal Tract Length Normalisation (VTLN). Overall, we observe that the changes in the voice source parameters do not have a significant impact on ASR performance. Comparison of the likelihood scores of all the phonemes for the two age groups show that there is a systematic mismatch in the acoustic space of the two age groups. Comparison of the phoneme recognition rates show that mid vowels, nasals, and phonemes that depend on the ability to create constrictions with tongue tip for articulation are more affected by ageing than other phonemes.
Analysis of transcriptomic and proteomic profiles demonstrates improved Madin–Darby canine kidney cell function in a renal microfluidic biochip

We have evaluated the influence of the microfluidic environment on renal cell functionality. For that purpose, we performed a time lapse transcriptomic and proteomic analysis in which we compared gene and protein expressions of Madin–Darby canine kidney cells after 24 h and 96 h of culture in both microfluidic biochips and plates. The transcriptomic and proteomic integration revealed that the ion transporters involved in calcium, phosphate, and sodium homoeostasis and several genes involved in H+ transporters and pH regulation were up‐regulated in microfluidic biochips. Concerning drug metabolism, we found Phase I (CYP P450), Phase II enzymes (GST), various multidrug resistance genes (MRP), and Phase III transporters (SLC) were also up‐regulated in the biochips. Furthermore, the study shows that those inductions were correlated with the induction of the Ahr and Nrf‐2 dependent pathways, which results in a global cytoprotective response induced by the microenvironment. However, there was no apoptosis situation or cell death in the biochips. Microfluidic biochips may thus provide an important insight into exploring xenobiotic injury and transport modifications in this type of bioartificial microfluidic kidney. Finally, the investigation demonstrated that combining the transcriptomic and proteomic analyses obtained from a cell “on chip” culture would provide a pertinent new tool in the mechanistic interpretation of cellular mechanisms for predicting kidney cell toxicity and renal clearance in vitro. © 2011 American Institute of Chemical Engineers Biotechnol. Prog. , 2012