Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconstructive Paradigms: A Problem-Solving Approach in Complex Tissue Defects

The field of plastic surgery is continuously evolving, with faster-emerging technologies and therapeutic approaches, leading to the necessity of establishing novel protocols and solving models. Surgical decision-making in reconstructive surgery is significantly impacted by various factors, including the etiopathology of the defect, the need to restore form and function, the patient’s characteristics, compliance and expectations, and the surgeon’s expertise. A broad surgical armamentarium is currently available, comprising well-established surgical procedures, as well as emerging techniques and technologies. Reconstructive surgery paradigms guide therapeutic strategies in order to reduce morbidity, mortality and risks while maximizing safety, patient satisfaction and properly restoring form and function. The paradigms provide researchers with formulation and solving models for each unique problem, assembling complex entities composed of theoretical, practical, methodological and instrumental elements.
Overburden movement characteristics of top-coal caving mining in multi-seam areas

The fully mechanized top-coal caving mining method was used to mine the 15 m thick No. 3–5 seam at a depth of 450 m in Tongxin mine, located in the Datong area, China. The seam was overlain with strong strata that are hard to cave and four partially mined coal seams. Excessive mine roadway closure and severe ground conditions at the longwall face and the tailgate were induced by periodic weighting and remnant coal pillars left in the four mined coal seams above. These ground conditions were studied using physical modelling and microseismic monitoring. The results indicated that hard rock strata above the coal seam did not cave readily, causing minor and major face loading events. The ground conditions deteriorated when mining was undertaken below the remnant pillars left in the four upper Jurassic seams. In addition, the physical model showed overlying longwall goaf collapse reaching the upper goafs and causing global movement in the strata. The data from the physical model and the underground microseismic survey presented here agreed closely with the visual observations and reported events that occurred at the underground longwall face in Tongxin coal mine. Overall, this study demonstrated the mechanisms of the overburden strata movement, longwall weighting occurrences and deteriorating ground conditions along the longwall face and the tailgate roadway when mining was undertaken under the overlapped Jurassic remnant coal pillars. Methods to minimize these bad ground conditions were suggested, using stress relief hydraulic fracturing methods and blasting, such as pre-splitting and softening of coal pillars and roof strata in key places. A design of small coal pillars <3–5 m in size were recommended for overlying seams to minimize stress concentrations in seams below.
READS: A Random Walk Approach for Efficient and Accurate Dynamic SimRank

Similarity among entities in graphs plays a key role in data analysis and mining. SimRank is a widely used and popular measurement to evaluate the similarity among the vertices. In real-life applications, graphs do not only grow in size, requiring fast and precise SimRank computation for large graphs, but also change and evolve continuously over time, demanding an efficient maintenance process to handle dynamic updates. In this paper, we propose a random walk based indexing scheme to compute SimRank efficiently and accurately over large dynamic graphs. We show that our algorithm outperforms the state-of-the-art static and dynamic SimRank algorithms.
The action dimension of right‐angled Artin groups

The action dimension of a discrete group Γ is the smallest dimension of a contractible manifold that admits a proper action of Γ . Associated to any flag complex L there is a right‐angled Artin group, AL . We compute the action dimension of AL for many L . Our calculations come close to confirming the conjecture that if an ℓ2 ‐Betti number of AL in degree l is nonzero, then the action dimension of AL is ⩾2l .
AOI transition trees

The analysis of transitions between areas of interest (AOIs) in eye tracking data provides insight into visual reading strategies followed by participants. We present a new approach to investigate eye tracking data of multiple participants, recorded from video stimuli. Our new transition trees summarize sequence patterns of all participants over complete videos. Shot boundary information from the video is used to divide the dynamic eye tracking information into time spans of similar semantics. AOI transitions within such a time span are modeled as a tree and visualized by an extended icicle plot that shows transition patterns and frequencies of transitions. Thumbnails represent AOIs in the visualization and allow for an interpretation of AOIs and transitions between them without detailed knowledge of the video stimulus. A sequence of several shots is visualized by connecting the respective icicle plots with curved links that indicate the correspondence of AOIs. We compare the technique with other approaches that visualize AOI transitions. With our approach, common transition patterns in eye tracking data recorded for several participants can be identified easily. In our use case, we demonstrate the scalability of our approach concerning the number of participants and investigate a video data set with the transition tree visualization.
Persistent Homology — a Survey

Persistent homology is an algebraic tool for measuring topological features of shapes and functions. It casts the multi-scale organization we frequently observe in nature into a mathematical formalism. Here we give a record of the short history of persistent homology and present its basic concepts. Besides the mathematics we focus on algorithms and mention the various connections to applications, including to biomolecules, biological networks, data analysis, and geometric modeling.
Design of Ambulatory Blood Pressure Monitoring for IOT-Based Hypertension Patients

Ambulatory blood pressure monitoring or ABPM is a non-invasive method to determine the average blood pressure for at least 24 hours, not only when medical checkup. ABPM is often found in cardiac examinations and monitoring of catlab preoperative patients. This study aims to analyze the performance of the ABPM tool that can measure blood pressure continuously with a specified time interval connected to IoT so that can make it easier to get test results. The contribution of this research is a 24-hour monitoring system with delivery via IoT. The experiment was conducted 10 times with Prosim comparison at each point to assess the level of reading accuracy and effectiveness of IoT viewers. At 120/80 mmHg systole accuracy 98.42%, diastole 97.25%. While at 150/100 mmHg systole accuracy is 99.67%, Diastole is 98.1%. At 200/160 mmHg point Systole accuracy 98.35%, Diastole 98.25%. The SPSS test states that the reading data collection is acceptable and has an average commensurate with the test. The difference in viewer time on the TFT and IoT layers is 3.8 seconds and the test data value is 0% loss. The results from making this module, concluding by utilizing the sensor MPX5050 obtained sufficient accuracy, the use of ESP32 as a microcontroller processes the sensor readings which will be converted into systole-diastole values and displays on IoT so that it can slightly help analyze the patient's condition, and this module can read the simulator tool well at pressures of 120/80 mmHg, 150/100 mmHg, and 200/160 mmHg. The device showed good accuracy and reliability in measuring blood pressure at different levels compared to a vital signs simulator. The device can be used for 24-hour monitoring of hypertension patients and provide useful information for diagnosis and treatment.
Self Organizing Semantic Topologies in Peer Database Systems

A semantic topology is a peer overlay network connected via semantic links, constructed using schema mappings and used for peer querying. The large-scale and dynamic environments of P2P networks dictate the use of automatic schema matching, which was shown to carry with it a degree of uncertainty, entailing uncertainty amplification over transitive mappings. Therefore, peers prefer network topologies that improve their ability to answer queries effectively, by reducing uncertainty. We introduce a model for a peer database management system that manages the inherent uncertainty of automatic schema matching, the amplification of this uncertainty over transitive mappings, and its impact on query processing. We then investigate a dynamic topology setting where peers can change their neighbor set selection. Given this setting, we focus on the following two research questions: (1) Can we efficiently identify topologies that reduce the uncertainty in the network? (2) Can such topologies self organize? We discuss algorithmic solutions both in an offline and in an online setting. The online heuristics are compared using empirical analysis, analyzing topology change process convergence and the quality of the end result.
The potential pattern of circulating lymphocytes TH1/TH2 is not altered after multiple injuries.

HYPOTHESIS A shift in the balance of helper T cells type 1 (T(H)1) toward type 2 (T(H)2) has been suggested as a possible mechanism for impaired immune responses after severe trauma. We suggest that major injuries (polytrauma) induce an alteration in the pattern of T(H)1/T(H)2 cells. DESIGN, SETTING, AND PATIENTS A prospective study of 35 polytraumatized patients (Injury Severity Score >16) admitted to a trauma intensive care unit at a level I trauma center (university hospital). INTERVENTIONS Blood samples were collected from patients at various times during their stay in the intensive care unit and from age- and sex-matched healthy individuals. MAIN OUTCOME MEASURES Serial determinations (n = 81) of intracellular interleukin (IL)-2 (T(H)1 cells) and IL-4 (T(H)2 cells) in stimulated CD3(+) T cells from patients with polytrauma twice a week during their stay in the intensive care unit accompanied by determination of the cell activation marker CD69 using 3-color flow cytometry. In parallel, the release of IL-2 and IL-4 from stimulated peripheral blood mononuclear cells and systemic plasma IL-4 levels were analyzed by conventional enzyme-linked immunosorbant assay. Healthy donors (n = 53) served as the control group. Data were related to outcome, Injury Severity Scores, and time after trauma. RESULTS Expression of the cell activation marker CD69 was similar in stimulated lymphocytes from patients and healthy donors. There were no significant posttraumatic alterations in numbers of CD3(+) cells stained for intracellular IL-2 or IL-4, except for a minor decrease in IL-2(+) cells during the first week after trauma. Subgroups with high (>24) and lower (<25) Injury Severity Scores or survivors and nonsurvivors revealed no differences in intracellular cytokine staining. In contrast, patients revealed a highly significant decrease in the number of CD3(+) T cells. Mean systemic IL-4 levels did not differ in patients compared with healthy donors. Release of IL-2 and IL-4 from peripheral blood mononuclear cell fractions stimulated with phorbolmyristateacetate and ionomycin was significantly increased in patients with trauma but not from those stimulated with toxic shock syndrome toxin-1. CONCLUSIONS Patients with multiple injuries have no significant alteration in the ratio of circulating T(H)1/T(H)2 cells. Thus, our results suggest pathomechanisms in posttraumatic T-cell suppression apart from alterations in the T(H)1/T(H)2 pattern.
Growth of nanocrystals and thin films at the water–oil interface

The use of the water–oil interface provides significant advantages in the synthesis of inorganic nanostructures. Employing the water–toluene interface, luminescent CdS nanocrystals have been obtained at a relatively modest temperature of 35°C. The diameters of the particulates can be varied between 1.0 and 5.0 nm. In addition, we have devised a new method for transferring thin films at the water–toluene interface onto solid substrates. Using this method, thin films consisting of Au and Ag nanocrystals spread over very large areas (square centimetres) are obtained in a single step. These films are directly usable as ingredients of functional devices. We show this by constructing a working amine sensor based on films of Au nanocrystals. The materials obtained have been characterized by X-ray diffraction, scanning and transmission electron microscopy, absorption and emission spectroscopy and charge transport measurements.
Color eigen-subband features for endoscopy image classification

This paper presents a new image feature extraction approach in the wavelet domain. We incorporate color-channel information of the LAB color space into the feature extraction process by computing variances from decorrelated detail subbands of the stationary wavelet transform. We evaluate our approach on a medical image classification problem using a k-nearest neighbor classifier and sequential forward feature selection. our experimental results, which include a comparative study to the popular color wavelet energy correlation signatures show that we can produce highly discriminative feature sets in terms of leave-one-out classification accuracy.
Joint instance and feature importance re-weighting for person reidentification

Person reidentification refers to the task of recognizing the same person under different non-overlapping camera views. Presently, person reidentification based on metric learning is proved to be effective among various techniques, which exploits the labeled data to learn a subspace that maximizes the inter-person divergence while minimizes the intra-person divergence. However, these methods fail to take the different impacts of various instances and local features into account. To address this issue, we propose to learn a projection matrix such that the importance of different instances and local features are re-weighted jointly. We also come up with a simplified formulation of the proposed algorithm, thus it can be solved by the efficient UDFS optimization algorithm. Extensive experiments on the VIPeR and iLIDS datasets demonstrate the effectiveness and efficiency of our algorithm.
Approaches of wireless TCP enhancement and a new proposal based on congestion coherence

TCP is known to have poor performance over unreliable wireless links where packet losses due to transmission errors are misinterpreted as indications of network congestion. TCP enhancements proposed in the literature differ in their signalling and data recovery mechanisms, applicable network configurations, traffic scenarios and locations where required changes are made. In this paper we categorize existing enhancements into several approaches. Motivated by these criteria, we propose a new enhancement that requires only local changes, but applies to a broad range of network and traffic configurations. Comparison with existing algorithm show this new enhancement achieves excellent performance.
Plant Initiation Factor 3 Subunit Composition Resembles Mammalian Initiation Factor 3 and Has a Novel Subunit*

Eukaryotic initiation factor 3 (eIF3) is a multisubunit complex that is required for binding of mRNA to 40 S ribosomal subunits, stabilization of ternary complex binding to 40 S subunits, and dissociation of 40 and 60 S subunits. These functions and the complex nature of eIF3 suggest multiple interactions with many components of the translational machinery. Recently, the subunits of mammalian and Saccharomyces cerevisiae eIF3 were identified, and substantial differences in the subunit composition of mammalian and S. cerevisiae were observed. Mammalian eIF3 consists of 11 nonidentical subunits, whereas S. cerevisiaeeIF3 consists of up to eight nonidentical subunits. Only five of the subunits of mammalian and S. cerevisiae are shared in common, and these five subunits comprise a “core” complex inS. cerevisiae. eIF3 from wheat consists of at least 10 subunits, but their relationship to either the mammalian or S. cerevisiae eIF3 subunits is unknown. Peptide sequences derived from purified wheat eIF3 subunits were used to correlate each subunit with mammalian and/or S. cerevisiae subunits. The peptide sequences were also used to identify Arabidopsis thalianacDNAs for each of the eIF3 subunits. We report seven new cDNAs for A. thaliana eIF3 subunits. A. thaliana eIF3 was purified and characterized to confirm that the subunit composition and activity of wheat and A. thaliana eIF3 were similar. We report that plant eIF3 closely resembles the subunit composition of mammalian eIF3, having 10 out of 11 subunits in common. Further, we find a novel subunit in the plant eIF3 complex not present in either mammalian or S. cerevisiae eIF3. These results suggest that plant and mammalian eIF3 evolved similarly, whereas S. cerevisiae has diverged.
Low complexity erasure insertion in RS-coded SFH spread-spectrum communications with partial-band interference and Nakagami-m fading

We propose two novel low-complexity, low-delay erasure insertion schemes, namely, the output threshold test (OTT) and joint maximum output and ratio threshold test (MO-RTT). The employment of the OTT and MO-RTT is beneficial in the context of the "errors-and-erasures" Reed-Solomon (RS) decoding in a slow frequency-hopping spread-spectrum (SFH/SS) system using M-ary frequency-shift keying (MFSK). The statistics of the erasure insertion related decision variables associated with the OTT, MO-RTT as well as with the ratio threshold test (RTT) are investigated, when the channel of each frequency-hopping (FH) slot is modeled as flat Nakagami-m fading. The transmitted signals also experience both additive white Gaussian noise (AWGN) as well as partial-band Gaussian interference (PBGI). The properties of these erasure insertion schemes are investigated with the aid of their statistics. The performance of the proposed erasure insertion schemes and that of the erasure insertion scheme using the RTT is investigated and compared in the context of RS coded SFH/SS systems using MFSK. Furthermore, the performance of the RS coded SFH/SS systems is also compared both with and without side-information concerning the PBGI.
Hydrologic Landscape Classification to Estimate Bristol Bay, Alaska Watershed Hydrology

Hydrologic landscapes (HLs) have proven to be a useful tool for broad scale assessment and classification of landscapes across the United States as they help organize larger geographical areas into areas of similar hydrologic characteristics. We developed a HL classification for the Bristol Bay watershed of southwest Alaska that incorporates indices of annual climate and seasonality, terrain, geology, and the influences of large lakes and glaciers. A HL classification is particularly useful in this large watershed because of its hydrologic and landscape variability, important salmon fishery, variety of environmental and potential anthropogenic stressors, and lack of widespread hydrologic data. Following creation of Bristol Bay basin‐wide HL classes, we compared the HL distributions within watersheds grouped by two calculated runoff parameters derived from available long‐term streamflow records and found HL distributions within these groups provided predictive insight on hydrologic behavior. Using these developed runoff groups, we estimated expected hydrologic behavior in watersheds across the larger Bristol Bay watershed that lacked gauged streamflow records. The HL approach provides a scientific basis for estimating the first‐order hydrologic behavior of watersheds and landscapes that lack detailed hydrologic information.
Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation

Estimating 3D pose from monocular images is a highly ambiguous problem. Physical constraints can be exploited to restrict the space of feasible configurations. In this paper we propose an approach to constraining the prediction of a discriminative predictor. We first show that the mean prediction of a Gaussian process implicitly satisfies linear constraints if those constraints are satisfied by the training examples. We then show how, by performing a change of variables, a GP can be forced to satisfy quadratic constraints. As evidenced by the experiments, our method outperforms state-of-the-art approaches on the tasks of rigid and non-rigid pose estimation.
Analysis of an emotional speech corpus in Hebrew based on objective criteria

To build a corpus of emotion in spontaneous speech, subjects were asked to recall an emotional event. Physiologic evaluations were then applied to validate the emotional content. This evaluation consisted of measuring several physiological parameters. Forty subjects were examined, each expressing five basic emotions (anger, fear, joy, sadness and disgust). Subjects who responded within the set criterion of the physiological evaluation were considered to represent the said emotion. This was followed by computer analysis in order to determine a set of criteria that could represent each emotion. The method employed was based on the analysis of the signal over sliding windows and extracting a representative feature set. At each instantce, the distance of the measured feature set from a set of reference points is calculated, and used to compute a fuzzy membership index for each emotion, which distance we called “emotional index”. We present initial results of this analysis: the characteristics of various emotions are presented, and we discuss the extent to which they are common across different individuals. Our initial results show that when tested on a Hebrew emotional speech corpus the proposed method yields reliable results.
Maximum Entropy Models for Named Entity Recognition

In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional non-annotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy.
The Dominance Tournament Method of Monitoring Progress in Coevolution

In competitive coevolution, the goal is to establish an “arms race” that will lead to increasingly sophisticated strategies. The existing methods for monitoring progress in coevolution are designed to demonstrate that the arms race indeed occurred. However, two issues remain: (1) How can progress be monitored efficiently so that every generation champion does not need to be compared to every other generation champion? (2) How can a monitoring method determine whether strictly more sophisticated strategies are discovered as the evolution progresses? We introduce a new method for tracking progress, the dominance tournament, which provides an answer to both questions. The dominance tournament shows how different coevolution runs continue to innovate for different periods of time, reveals the precise generation in each run where stagnation occurs, and identifies the best individuals found during the runs. Such differences are difficult to detect using standard techniques but are clearly distinguished in a dominance tournament, which makes this method a highly useful tool in understanding progress in coevolution.
The Challenges of Predicting Structural Performance in Fires

This paper describes the challenges of predicting structural performance in fires, with a view to the past, the present and the future. Despite the huge advances in computational capability for both fire modelling and structural analysis, it remains very difficult to give accurate predictions of structural performance in a single building with a known fire. Most designers grapple with a known building and an unknown possible fire (or fires), and code writers valiantly attempt to give rules or guidelines for whole classes of unknown buildings which could be exposed to any kind of unknown fires.