Miracle-dz/UniFAR-FTUs huggingface dataset:
Boundary First Flattening

A conformal flattening maps a curved surface to the plane without distorting angles—such maps have become a fundamental building block for problems in geometry processing, numerical simulation, and computational design. Yet existing methods provide little direct control over the shape of the flattened domain, or else demand expensive nonlinear optimization. Boundary first flattening (BFF) is a linear method for conformal parameterization that is faster than traditional linear methods, yet provides control and quality comparable to sophisticated nonlinear schemes. The key insight is that the boundary data for many conformal mapping problems can be efficiently constructed via the Cherrier formula together with a pair of Poincaré-Steklov operators; once the boundary is known, the map can be easily extended over the rest of the domain. Since computation demands only a single factorization of the real Laplace matrix, the amortized cost is about 50× less than any previously published technique for boundary-controlled conformal flattening. As a result, BFF opens the door to real-time editing or fast optimization of high-resolution maps, with direct control over boundary length or angle. We show how this method can be used to construct maps with sharp corners, cone singularities, minimal area distortion, and uniformization over the unit disk; we also demonstrate for the first time how a surface can be conformally flattened directly onto any given target shape.
Not All Seeds Are Equal: Measuring the Quality of Text Mining Seeds

Open-class semantic lexicon induction is of great interest for current knowledge harvesting algorithms. We propose a general framework that uses patterns in bootstrapping fashion to learn open-class semantic lexicons for different kinds of relations. These patterns require seeds. To estimate the goodness (the potential yield) of new seeds, we introduce a regression model that considers the connectivity behavior of the seed during bootstrapping. The generalized regression model is evaluated on six different kinds of relations with over 10000 different seeds for English and Spanish patterns. Our approach reaches robust performance of 90% correlation coefficient with 15% error rate for any of the patterns when predicting the goodness of seeds.
JPEG compression history estimation for color images

We routinely encounter digital color images that were previously compressed using the Joint Photographic Experts Group (JPEG) standard. En route to the image's current representation, the previous JPEG compression's various settings-termed its JPEG compression history (CH)-are often discarded after the JPEG decompression step. Given a JPEG-decompressed color image, this paper aims to estimate its lost JPEG CH. We observe that the previous JPEG compression's quantization step introduces a lattice structure in the discrete cosine transform (DCT) domain. This paper proposes two approaches that exploit this structure to solve the JPEG Compression History Estimation (CHEst) problem. First, we design a statistical dictionary-based CHEst algorithm that tests the various CHs in a dictionary and selects the maximum a posteriori estimate. Second, for cases where the DCT coefficients closely conform to a 3-D parallelepiped lattice, we design a blind lattice-based CHEst algorithm. The blind algorithm exploits the fact that the JPEG CH is encoded in the nearly orthogonal bases for the 3-D lattice and employs novel lattice algorithms and recent results on nearly orthogonal lattice bases to estimate the CH. Both algorithms provide robust JPEG CHEst performance in practice. Simulations demonstrate that JPEG CHEst can be useful in JPEG recompression; the estimated CH allows us to recompress a JPEG-decompressed image with minimal distortion (large signal-to-noise-ratio) and simultaneously achieve a small file-size.
Investigation of Roughness Algorithms Applied to JRC Profiles for Assessment of Weathering

Although JRC profiles are commonly used for shear strength assessment of rock joints, they can also serve as a base model for weathering assessment. In this study, JRC profiles were characterized using eight promising roughness algorithms from diverse fields such as geomorphology, rock mechanics and signal processing. Roughness indices of digitized JRC profiles were assessed at three different sampling intervals to observe how scale impacts their correlation with JRC. Plots of JRC versus roughness indices exhibit one of two general shapes: either a generally (but sometimes monotonically increasing) curve with occasional small peaks at JRC 6-8 and JRC 12-14 or a bimodal shape that peaks consistently at JRC 10-12 and JRC 14-16 followed by a sudden drop in roughness. Z2 and Mean Absolute Angle show the highest linearity of the eight algorithms. Z2 has been used numerous times to assess JRC profiles but Mean Absolute Angle is a new and promising roughness algorithm. Standard Deviation (σ) and Signal Energy (Es) demonstrate remarkable consistency between sampling intervals but their relative non-linearity with JRC suggests they are insufficient in quantifying roughness. A new JRC relationship, based on Z2 and intercept of the log-log plot of Z2 versus sampling interval, is presented. roughness is an arbitrary measure; it is scale dependent and for its use in quantifying weathering, there is no single satisfactory scale [13]. As a precursor to weathering quantification using surface roughness, eight promising roughness algorithms will be assessed using the joint roughness profiles. Fig. 1. Surface roughness of three weathered limestone
ADSCs on PLLA/PCL Hybrid Nanoscaffold and Gelatin Modification: Cytocompatibility and Mechanical Properties

Background Development of tissue engineering and regenerative medicine has led to designing scaffolds and their modification to provide a better microenvironment which mimics the natural niche of the cells. Gelatin surface modification was applied to improve scaffold flexibility and cytocompatibility. Methods PLLA/PCL aligned fibrous scaffold was fabricated using electrospinning method. ADSCs were seeded after O2 plasma treatment and gelatin coating of the scaffolds. The morphological and mechanical properties of blends were assessed by Scanning Electron Microscopy (SEM), tensile test and ATR-FTIR. The cells proliferation was evaluated by MTT assay. Results Based on the results, it is supposed that gelatin coating is a brilliant method of surface modification which significantly increases the mechanical properties of scaffold without any changes on the construction or on the direction of nanofibers which conducts cell's elongation. MTT analysis exhibited that ADSCs attachment, viability and proliferation significantly (p < 0.05) increased after gelatin treatment. Conclusion Gelatin surface modification is a highly beneficial method to improve cytocompatibility, flexibility and mechanical features of the scaffolds which doesn't affect the nanofibers construction. Proliferation of Adipose Derived Stem Cells (ADSCs) as a remarkable source of stem cells was investigated for the first time on PLLA/PCL hybrid scaffold.
Membrane-bound molecules in rat cerebral cortex regulate thalamic innervation

During development of the thalamocortical projection, afferent fibers from the thalamus reach the cortex at a time when their target cells have just been generated but have not yet migrated to their final position. Thalamic axons begin to invade the cortex only shortly before their target layer 4 is formed. The mechanisms responsible for the innervation and termination of thalamic fibers in the cortex are not known. Here we show that the growth of thalamic axons in vitro is influenced by the age of cortical explants. Cortical explants of early embryonic stages were not invaded by thalamic explants, whereas thalamic fibers entered explants from postnatal cortices and terminated properly in their target layer 4 in vitro. Outgrowth assays on cortical cell membranes prepared at different developmental stages revealed that the growth of thalamic axons is selectively influenced by growth- promoting molecules that are up-regulated during development. Moreover, experiments with postnatal cortical membranes isolated from distinct layers revealed that the growth of thalamic axons is selectively reduced on membranes prepared from layer 4. These results provide evidence that membrane-bound molecules in the cortex are involved in both the regulation of thalamic innervation into the cortical layers and their termination in the correct target layer.
Network I/O Path Analysis in the Kernel-Based Virtual Machine Environment through Tracing

Virtualization is experiencing a resurgence of interest for diverse uses including server consolidation and shared hosting. Unfortunately, the overhead of virtual machine network I/O has became the main factor that affects the performance of the virtual machine. Meanwhile, few tools are currently available to help debug performance problems in virtual machine environments. Therefore, it is especially significant to analyze Virtual Machine network I/O path and then improve the performance. Firstly, we analyze the Kernel-based Virtual Machine (KVM) and its network I/O architecture. Then the tracing tool SystemTap and tracing technology in virtual machine are described. This paper presents in detail the ingressing path and the corresponding overhead in the environment of KVM through tracing. The bottlenecks that affect the performance of network virtualization in KVM are also summarized to make future optimizing work easier.
The Random Buffer Tree : A Randomized Technique for I/O-efficient Algorithms

In this paper, we present a probabilistic self-balancing dictionary data structure for massive data sets, and prove expected amortized I/O-optimal bounds on the dictionary operations. We show how to use the structure as an I/O-optimal priority queue. The data structure, which we call as the random buffer tree, abstracts the properties of the random treap and the buffer tree and has the same expected I/O-bounds as the buffer tree.
High-resolution depth maps based on TOF-stereo fusion

The combination of range sensors with color cameras can be very useful for robot navigation, semantic perception, manipulation, and telepresence. Several methods of combining range- and color-data have been investigated and successfully used in various robotic applications. Most of these systems suffer from the problems of noise in the range-data and resolution mismatch between the range sensor and the color cameras, since the resolution of current range sensors is much less than the resolution of color cameras. High-resolution depth maps can be obtained using stereo matching, but this often fails to construct accurate depth maps of weakly/repetitively textured scenes, or if the scene exhibits complex self-occlusions. Range sensors provide coarse depth information regardless of presence/absence of texture. The use of a calibrated system, composed of a time-of-flight (TOF) camera and of a stereoscopic camera pair, allows data fusion thus overcoming the weaknesses of both individual sensors. We propose a novel TOF-stereo fusion method based on an efficient seed-growing algorithm which uses the TOF data projected onto the stereo image pair as an initial set of correspondences. These initial “seeds” are then propagated based on a Bayesian model which combines an image similarity score with rough depth priors computed from the low-resolution range data. The overall result is a dense and accurate depth map at the resolution of the color cameras at hand. We show that the proposed algorithm outperforms 2D image-based stereo algorithms and that the results are of higher resolution than off-the-shelf color-range sensors, e.g., Kinect. Moreover, the algorithm potentially exhibits real-time performance on a single CPU.
Ethical issues raised in using online tele-mental health (OTH): a review of studies

Given the current COVID-19 pandemic, utilizing technology in online platforms has been a primary method internationally for upholding the health care system during this world health crisis. Online tele-mental health (OTH) offers exciting new breaks for mental health practitioners. However, initiation of OTH has also come with a host of trials, particularly on ethical standards. The goal of this narrative review aims to highlight the raised ethical issues among studies applying the modality of OTH which is a timely resource for mental health professionals who have been forced to move into the practice of online consultations and sessions. Using the standard key points of the mental health practitioners’ codes of ethics, applying both the American Psychological Association (APA) and the code of ethics provided by the Psychological Association of the Philippines (PAP) is used during the review. Results show that there are problems and uncertainties for mental health providers striving to fulfill the shift in technology. In this study, the five of the most critical ethical issues found in OTH encounters include issues of competency, issues in the interruptions of psychological services in using technology, issues in integrity, issues in privacy and confidentiality, and themes that are considered accessory issues in OTH. The recommendations provided adheres to lift ethical issues raised in the review.
Approximating minimum bounded degree spanning trees to within one of optimal

In the <sc>Minimum Bounded Degree Spanning Tree</sc> problem, we aregiven an undirected graph with a degree upper bound B<sub>v</sub> on eachvertex v, and the task is to find a spanning tree of minimumcost which satisfies all the degree bounds. Let OPT be the costof an optimal solution to this problem. In this paper, we presenta polynomial time algorithm which returns a spanning tree T ofcost at most OPT and d<sub>T</sub>(v) ≤ B<sub>v</sub>+1 for all v, where d<sub>T</sub>(v) denotes the degree of v in T. This generalizes aresult of Furer and Raghavachari [8] to weighted graphs, andsettles a 15-year-old conjecture of Goemans [10] affirmatively. The algorithm generalizes when each vertex v hasa degree lower bound A<sub>v</sub> and a degree upper bound B<sub>v</sub>, andreturns a spanning tree with cost at most OPT and A<sub>v</sub> - 1 ≤d<sub>T</sub>(v) ≤ B<sub>v</sub> + 1 for all v. This is essentially the bestpossible. The main technique used is an extension of the iterativerounding method introduced by Jain [12] for the design ofapproximation algorithms.
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate

Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative opinions. The new model has an explicit parameter called quality factor to model the natural behavior of people turning negative to a product due to product defects. Our model incorporates negativity bias (negative opinions usually dominate over positive opinions) commonly acknowledged in the social psychology literature. The model maintains some nice properties such as submodularity, which allows a greedy approximation algorithm for maximizing positive influence within a ratio of 1-1/e. We define a quality sensitivity ratio (qs-ratio) of influence graphs and show a tight bound of T(vn/k) on the qs-ratio, where n is the number of nodes in the network and k is the number of seeds selected, which indicates that seed selection is sensitive to the quality factor for general graphs. We design an efficient algorithm to compute influence in tree structures, which is nontrivial due to the negativity bias in the model. We use this algorithm as the core to build a heuristic algorithm for influence maximization for general graphs. Through simulations, we show that our heuristic algorithm has matching influence with a standard greedy approximation algorithm while being orders of magnitude faster.
Exploring Gene Expression Data with Class Scores

We address a commonly asked question about gene expression data sets: "What functional classes of genes are most interesting in the data? " In the methods we present, expression data is partitioned into classes based on existing annotation schemes. Each class is then given three separately derived "interest" scores. The first score is based on an assessment of the statistical significance of gene expression changes experienced by members of the class, in the context of the experimental design. The second is based on the co-expression of genes in the class. The third score is based on the learnability of the classification. We show that all three methods reveal significant classes in each of three different gene expression data sets. Many classes are identified by one method but not the others, indicating that the methods are complementary. The classes identified are in many cases of clear relevance to the experiment. Our results suggest that these class scoring methods are useful tools for exploring gene expression data.
CPW-Fed Stub-Loaded Slot Dipole Antenna Design for Dual-Band Operation

A novel uniplanar slot dipole antenna fed by a coplanar waveguide (CPW) is proposed for dual-band operation. The frequency ratio between the first spurious and fundamental modes of the slot dipole antenna can be conveniently adjusted with the use of four slot stubs introduced on its two arms. Furthermore, the radiation patterns at the first spurious mode are modified by adding four parasitic slots along its two arms to resemble those of the fundamental mode. Under the assistance of the slot stubs together with the parasitic slots, another resonant mode can be generated and merged with the first spurious mode, and therefore improve the bandwidth significantly. An antenna prototype was fabricated and measured to validate the design concept. The measured results show that dual-band operation with 10-dB impedance bandwidths of 2.99–3.83 GHz (24.6%) and 5.21–6.89 GHz (27.8%) has been obtained. The antenna has stable broadside and bidirectional radiation patterns with low crosspolarization components over the two operating bands.
Assessment of Adaptive Self-Learning-Based BLDC Motor Energy Management Controller in Electric Vehicles Under Real-World Driving Conditions for Performance Characteristics

The superior performance of an electric vehicles (EVs) is dependent on the related energy management controller (EMC). The current study devoted to develop various EMCs such PID, intelligent, hybrid and supervisory strategy to enhance the performance of EVs under real-time driving conditions. Also, the work integrates the various novel methodologies to develop EV model, efficiency maps and real-time driving cycle (DC). In this instance, a mathematical model of an EV with BLDC motor is developed using MATLAB/Simulink. Further, the efficiency maps for the motor and controller with different EMC’s are generated using the innovative experimental approach. Then, the developed efficiency maps are incorporated into model-in-loop (MIL)-based EV test platform to analyze the performance of various EMCs. Additionally, to validate the EV model, a real time DC has been developed for different types of road conditions, including urban, rural, and highway. Subsequently, the developed DC is integrated with MIL-based EV test platform for analysis of energy consumption and battery discharge behavior under real-time conditions. From the results, the proposed supervisory controller (68.4%) exhibits minimal SOC drop than the PID (21.5%), intelligent (44.9%) and hybrid (59.1%) controllers. As well, the energy consumption (EC) of the various EMCs is 85.63, 60.14, 44.67 and 33.4 Wh/km. In the case of regenerative efficiency of the developed EMCs under real-time driving conditions are −27.73, −41.64, −58.28 and −77.6 Wh respectively. The overall outcome of this work demonstrates that the proposed supervisory controller achieves a considerable improvement in battery consumption as well as a reduction in EC as compared to PID, intelligent, and hybrid controllers.
A review of numerical methods for red blood cell flow simulation

In this review, we provide an overview of the simulation techniques employed for modelling the flow of red blood cells (RBCs) in blood plasma. The scope of this review omits the fluid modelling aspect while focusing on other key components in the RBC–plasma model such as (1) describing the RBC deformation with shell-based and spring-based RBC models, (2) constitutive models for RBC aggregation based on bridging theory and depletion theory and (3) additional strategies required for completing the RBC–plasma flow model. These include topics such as modelling fluid–structure interaction with the immersed boundary method and boundary integral method, and updating the variations in multiphase fluid property through the employment of index field methods. Lastly, we summarily discuss the current state and aims of RBC modelling and suggest some research directions for the further development of this field of modelling.
Joint Design of Groupwise STBC and SIC Based Receiver

In this letter, we propose a simple groupwise space-time block code (GSTBC) which can be easily applied to a large number of transmit antennas and be effectively decoded by a low complexity successive interference cancellation (SIC) based receiver. The proposed GSTBC and SIC based receiver are jointly designed and the diversity repetition in GSTBC is used to induce the dimension expansion that can be exploited by the SIC based receiver to suppress interfering signals as well as to obtain diversity gain. Our proposed scheme provides a near maximum likelihood (ML) performance while keeping a reasonably low complexity at the receiver.
Surgical Management of Idiopathic Normal-pressure Hydrocephalus

OBJECTIVE:To develop evidence-based guidelines for surgical management of idiopathic normal-pressure hydrocephalus (INPH). Compared with the diagnostic phase, the surgical management of INPH has received less scientific attention. The quality of much of the literature concerning the surgical management has been limited by many factors. These include retrospective analysis, small patient numbers, analysis of a mixed NPH population, and sometimes a lack of detail as to what type of shunt system was used. Many earlier studies predated our current understanding of the hydrodynamics of cerebrospinal fluid shunts, and therefore, the conclusions drawn may no longer be valid. METHODS:A MEDLINE and PubMed search from 1966 to the present was conducted using the following key terms: normal-pressure hydrocephalus and idiopathic adult-onset hydrocephalus. Only English-language literature in peer-reviewed journals was reviewed. The search was further limited to articles that described the method of treatment and outcome selectively for INPH patients. Finally, only studies that included 20 or more INPH patients were considered with respect to formulating the recommendations in these Guidelines (27 articles). RESULTS:For practical reasons, it is important to identify probable shunt responders diagnosed with INPH. If the patient is an acceptable candidate for anesthesia, then an INPH-specific risk-benefit analysis should be determined. In general, patients exhibiting negligible symptoms may not be suitable candidates for surgical management, given the known risks and complications associated with shunting INPH. The choice of valve type and setting should be based on empirical reasoning and a basic understanding of shunt hydrodynamics. The most conservative choice is a valve incorporating an antisiphon device, with the understanding that underdrainage (despite a low opening pressure) may occur in a small percentage of patients because of the antisiphon device. On the basis of retrospective studies, the use of an adjustable valve seems to be beneficial in the management of INPH. CONCLUSION:The treatment of INPH should not be considered lightly, given the seriousness of the potential complications. Within these limitations and the available evidence, guidelines for surgical management were developed.