Miracle-dz/UniFAR-FTUs huggingface dataset:
One-stage partial vertebrectomy, titanium mesh implantation and pedicle screw fixation in the treatment of thoracolumbar burst fractures through a posterior approach

OBJECTIVE: To analyze the clinical results of a partial vertebrectomy with titanium mesh implantation and pedicle screw fixation using a posterior approach to reconstruct the spine in the treatment of thoracolumbar burst fractures. METHOD: From January 2006 to August 2008, 20 patients with severe thoracolumbar fractures were treated.For vertebral bodies associated with one injured intervertebral disk, subtotal vertebrectomy surgery and single-segment fusion were performed. For vertebral bodies with two injured adjacent intervertebral disks, partial vertebrectomy surgery and two-segment fusion were performed. RESULTS: All 20 patients were followed up for 12 to 24 months (average of 18 months). There were no complications such as wound infections, hemopneumothorax or abdominal infections in any of the patients. The neurological status of all of the patients was improved by at least one American Spinal Injury Association grade by the last follow-up. The anterior vertebral body height was an average of 50.77% before surgery, 88.51% after surgery and 87.86% at the last follow up; the sagittal Cobb angle was improved, on average, from 26.15° to 5.39° and was 5.90° at the last follow up. The percentage of spinal stenosis was improved, on average, from 26.07% to 4.93%° and was 6.15% at the last follow up. There were significant differences in the anterior vertebral body height pre- and post-surgery and in the sagittal Cobb angle and the percentage of spinal stenosis (p<0) in all patients. CONCLUSIONS: This surgical procedure is simple and can accomplish decompression, reduction, fixation and fusion of the spine in one stage. This approach could be widely used in orthopedics.
Experimental Evaluation of Network Telemetry Anonymization for Cloud Based Security Analysis

Network telemetry data is considered a gold mine for researchers for performing traffic analysis, QoS, security forensics, malware spread etc. Cloud Consumers could benefit from variety of Cloud based security services if such telemetry data is made available. But there is a compelling need for ensuring privacy of sensitive fields before data is shared with any Cloud provider. In this paper we provide thorough experimental evaluation of data anonymization techniques. We explore the viability of onion layered encryption techniques for practical security forensics on anonymized data. We provide results of such experiments and our analysis of the same. Our major observation is that onion layered techniques do not scale for more advanced analytic use cases.
Forecasting Chaotic Stock Market Data using Time Series Data Mining

An important financial subject that has attracted researchers’ attention for many years is forecasting stock return. Many researchers have contributed in this area of chaotic forecast in their ways. Among them data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, instead of a single aspects of stock market, traders need to use various aspects’ forecasting to gain multiple signals and more information about the future of the markets. Aspects of Lyapunov, Entropy and Variance (ALEV) provide an approach for mining large stocks of time series data. This paper proposes a novel method for forecasting chaotic behavior of stock market’s opening, high, low and closing price with time series data mining. The outcome of this study tries to help the investors in the stock market to decide the better timing for buying or selling stocks based on the knowledge extracted from the historical prices of such stocks. General Terms Chaotic data mining, time series based data forecasting.
Rough Sets Based Hybrid Intelligent Fault Diagnosis for Precision Test Turntable

This paper is concerned with fault diagnosis for the precision test turntable (PTT). Using rough set theory combine with neural network, a forward greedy reduce algorithm based on rough set is presented to pre-process the raw fault information. By calculating the dependence and significance of the condition, the core attributes are gained and finally the reduction of the raw fault information is obtained. The worst case of computational complexity of reduction and the total computational times of the algorithm are presented. The reduced decision table will be used by the neural network as the training samples. Rough set method can effectively decrease the dimension of the information space. In this algorithm, the training samples for the neural network can be reduced dramatically, and the training time of the network is decreased. The method can detect the composed faults while keeping good robustness, and can reduce the false alarm rate and the missing alarm rate of the fault diagnosis system effectively.
Assessment of Atmospheric Correction Methods for Sentinel-2 MSI Images Applied to Amazon Floodplain Lakes

Satellite data provide the only viable means for extensive monitoring of remote and large freshwater systems, such as the Amazon floodplain lakes. However, an accurate atmospheric correction is required to retrieve water constituents based on surface water reflectance ( R W ). In this paper, we assessed three atmospheric correction methods (Second Simulation of a Satellite Signal in the Solar Spectrum (6SV), ACOLITE and Sen2Cor) applied to an image acquired by the MultiSpectral Instrument (MSI) on-board of the European Space Agency’s Sentinel-2A platform using concurrent in-situ measurements over four Amazon floodplain lakes in Brazil. In addition, we evaluated the correction of forest adjacency effects based on the linear spectral unmixing model, and performed a temporal evaluation of atmospheric constituents from Multi-Angle Implementation of Atmospheric Correction (MAIAC) products. The validation of MAIAC aerosol optical depth (AOD) indicated satisfactory retrievals over the Amazon region, with a correlation coefficient (R) of ~0.7 and 0.85 for Terra and Aqua products, respectively. The seasonal distribution of the cloud cover and AOD revealed a contrast between the first and second half of the year in the study area. Furthermore, simulation of top-of-atmosphere (TOA) reflectance showed a critical contribution of atmospheric effects (>50%) to all spectral bands, especially the deep blue (92%–96%) and blue (84%–92%) bands. The atmospheric correction results of the visible bands illustrate the limitation of the methods over dark lakes ( R W < 1%), and better match of the R W shape compared with in-situ measurements over turbid lakes, although the accuracy varied depending on the spectral bands and methods. Particularly above 705 nm, R W was highly affected by Amazon forest adjacency, and the proposed adjacency effect correction minimized the spectral distortions in R W (RMSE < 0.006). Finally, an extensive validation of the methods is required for distinct inland water types and atmospheric conditions.
Maximizing the Impact of e-Therapy and Serious Gaming: Time for a Paradigm Shift

Internet interventions for mental health, including serious games, online programs, and apps, hold promise for increasing access to evidence-based treatments and prevention. Many such interventions have been shown to be effective and acceptable in trials; however, uptake and adherence outside of trials is seldom reported, and where it is, adherence at least, generally appears to be underwhelming. In response, an international Collaboration On Maximizing the impact of E-Therapy and Serious Gaming (COMETS) was formed. In this perspectives’ paper, we call for a paradigm shift to increase the impact of internet interventions toward the ultimate goal of improved population mental health. We propose four pillars for change: (1) increased focus on user-centered approaches, including both user-centered design of programs and greater individualization within programs, with the latter perhaps utilizing increased modularization; (2) Increased emphasis on engagement utilizing processes such as gaming, gamification, telepresence, and persuasive technology; (3) Increased collaboration in program development, testing, and data sharing, across both sectors and regions, in order to achieve higher quality, more sustainable outcomes with greater reach; and (4) Rapid testing and implementation, including the measurement of reach, engagement, and effectiveness, and timely implementation. We suggest it is time for researchers, clinicians, developers, and end-users to collaborate on these aspects in order to maximize the impact of e-therapies and serious gaming.
OBSTACLE-AVOIDING PATH PLANNING FOR HIGH VELOCITY WHEELED MOBILE ROBOTS

Abstract This paper presents a new motion planning algorithm for wheeled mobile robots in presence of known static obstacles, especially well-suited for high velocity situations. It takes into account several conditions traditionally attached to smooth path planning, i.e. paths with continuous derivative and upper-bounded curvature. It makes use of a global path planner which exploits polynomial G3 curves characteristics.
Activity of avibactam against Enterobacter cloacae producing an extended-spectrum class C β-lactamase enzyme.

BACKGROUND Extended-spectrum AmpC (ESAC) β-lactamase enzymes, which are either chromosomally encoded or plasmid encoded, have minor structural changes that broaden their substrate hydrolysis profile. The derepressed AmpC enzyme found once in Enterobacter cloacae CHE was shown to contain a six residue deletion in the H-10 helix in close proximity to the active site. Avibactam is a non-β-lactam inhibitor of Ambler class A, class C and some class D β-lactamases that is in clinical development with several β-lactam agents. It has been shown to inhibit AmpC enzymes, but its microbiological activity against isolates carrying different ESAC enzymes is less well understood. METHODS MICs were determined using the broth microdilution technique. RT-PCR analyses were performed to measure the level of ampC expression and whole genome sequencing was performed to enable sequence-based analyses. RESULTS Structural analyses of avibactam bound to a representative AmpC β-lactamase suggested that the H-10 helix deletion would impact the potency of the inhibitor. Under standard conditions, the ceftazidime/avibactam and ceftaroline/avibactam MIC values for E. cloacae CHE were 64 and 4 mg/L, respectively, representing a significant decrease in susceptibility over control E. cloacae isolates. However, use of higher avibactam concentrations restored the susceptibility of E. cloacae CHE in a dose-dependent manner. Comparison with other E. cloacae isolates carrying derepressed AmpC enzymes suggested that this difference in inhibition by avibactam was unrelated to the level of AmpC being produced. CONCLUSIONS The E. cloacae CHE ESAC enzyme is inhibited less efficiently by avibactam than other E. cloacae AmpC proteins due to a subtle rearrangement of the binding site. Although the variants are not commonly observed, the different ESAC enzymes may be inhibited to varied extents by avibactam.
Efficient Linear Discriminant Analysis Using a Fast Quadratic Programming Algorithm

An algorithm is proposed for performing linear discriminant analysis using a single-layered feedforward network. The algorithm follows successive steepest descent directions with respect to the perceptron cost function, taking care not to increase the number of misclassified patterns. The algorithm has no free parameters and therefore no heuristics are involved in its application. Its efficiency in terms of speed of convergence is demonstrated in a number of pattern classification problems.
Plasma Simulation on Networks of Workstations using the Bulk-Synchronous Parallel Model

Computationally intensive applications with frequent communication and synchronization require careful design for eecient execution on networks of workstations. We describe a Bulk-Synchronous Processing (BSP) model implementation of a plasma simulation and use of BSP analysis techniques for tuning the program for arbitrary architectures. In addition, we compare the performance of the BSP implementation with a version using MPI. Our results indicate that the BSP model, serving as a basis for an eecient implementation, compares favorably with MPI.
Maximizing the resonant displacement of piezoelectric beams

The paper presents a linear, lumped-parameter, one-degree- of-freedom model that describes the first mode dynamic response of a piezoelectrically-actuated cantilevered beam. The original system consists of two identical, symmetrically disposed piezoelectric patch actuators bonded to a uniform beam with a tip mass. Specifically, the first mode of the system is described by an equivalent mass-spring-damper system where the equivalent mass, stiffness, damping, and force are functions of the parameters defining the piezoelectrically-actuated system. Experimental data is used to verify model damping and displacement. The model is then utilized to demonstrate that the dynamic response of the system depends upon four independent non-dimensional geometric variables. Numerical simulations are performed to graphically demonstrate this dependence and to indicate an optimal configuration for achieving a maximum deflection at the beam's free end during resonant excitation.
The Wealth of Wealthholders John Ameriks

This paper introduces the Vanguard Research Initiative (VRI), a new panel survey of wealthholders designed to yield high-quality measurements of a large sample of older Americans who arrive at retirement with significant financial assets. The VRI links survey data with a variety of administrative data from Vanguard. The survey features an account-by-account approach to asset measurement and a real-time feedback and correction mechanism that are shown to be highly successful in eliciting accurate measures of wealth. Specifically, the VRI data reflect unbiased and precise estimates of wealth when compared to administrative account data. The VRI sample has characteristics similar to populations meeting analogous wealth and Internet access eligibility conditions in the Health and Retirement Study (HRS) and Survey of Consumer Finances (SCF). To illustrate the value of the VRI, the paper shows that the relationship between wealth and expected retirement date is very different in the VRI than in the HRS and SCF— mainly because those surveys have so few observations where wealth levels are high enough to finance substantial consumption during retirement. John Ameriks The Vanguard Group, Inc. PO Box 2600 Valley Forge PA 19482-2600 john_ameriks@vanguard.com Andrew Caplin Department of Economics New York University New York, NY 10012 and NBER andrew.caplin@nyu.edu Minjoon Lee Department of Economics and Survey Research Center University of Michigan Ann Arbor, MI 48109-1248 minjoon@umich.edu Matthew D. Shapiro Department of Economics and Survey Research Center University of Michigan Ann Arbor, MI 48109-1248 and NBER shapiro@umich.edu Christopher Tonetti Graduate School of Business Stanford University Stanford CA, 94305-7298 tonetti@stanford.edu
Automatic Caudate Segmentation by Hybrid Generative/Discriminative Models

Segmenting sub-cortical structures from 3D brain images is of significant practical importance. This paper presents an experimental study for caudate segmentation in MRI images using a fully automatic algorithm based on [9], which is an extension of the hybrid model approach described in [1]. The method in [9] tackles multiple sub-cortical and cortical structures. In this study (the grand challenge competition), the focus is on segmenting the left and right caudate from MRI images. Given a set of training data, a classifier (PBT [9]) is used to learn a discriminative model for each voxel being on the left caudate, right caudate, or the background. In testing, a variational approach is used to perform segmentation by minimizing an energy of a hybrid model. This hybrid model is composed of a discriminative model term by the trained PBT classifier and a generative shape term. It takes, typically, 5 minutes to perform the segmentation on a modern PC.
Effects of high-fat diet and exercise training on intracellular glucose metabolism in rats.

We examined the effects of high-fat diet (HFD) and exercise training on insulin-stimulated whole body glucose fluxes and several key steps of glucose metabolism in skeletal muscle. Rats were maintained for 3 wk on either low-fat (LFD) or high-fat diet with or without exercise training (swimming for 3 h per day). After the 3-wk diet/exercise treatments, animals underwent hyperinsulinemic euglycemic clamp experiments for measurements of insulin-stimulated whole body glucose fluxes. In addition, muscle samples were taken at the end of the clamps for measurements of glucose 6-phosphate (G-6-P) and GLUT-4 protein contents, hexokinase, and glycogen synthase (GS) activities. Insulin-stimulated glucose uptake was decreased by HFD and increased by exercise training (P < 0.01 for both). The opposite effects of HFD and exercise training on insulin-stimulated glucose uptake were associated with similar increases in muscle G-6-P levels (P < 0.05 for both). However, the increase in G-6-P level was accompanied by decreased GS activity without changes in GLUT-4 protein content and hexokinase activities in the HFD group. In contrast, the increase in G-6-P level in the exercise-trained group was accompanied by increased GLUT-4 protein content and hexokinase II (cytosolic) and GS activities. These results suggest that HFD and exercise training affect insulin sensitivity by acting predominantly on different steps of intracellular glucose metabolism. High-fat feeding appears to induce insulin resistance by affecting predominantly steps distal to G-6-P (e.g., glycolysis and glycogen synthesis). Exercise training affected multiple steps of glucose metabolism both proximal and distal to G-6-P. However, increased muscle G-6-P levels in the face of increased glucose metabolic fluxes suggest that the effect of exercise training is quantitatively more prominent on the steps proximal to G-6-P (i.e., glucose transport and phosphorylation).
An electrochemical sensor ASIC for agriculture applications

This paper presents a fully integrated application specific integrated circuit (ASIC) for the readout of multiple electrochemical sensor signals. It consists of an analog router that routes readout circuits to various sensors, a 10bit successive approximation register analog to digital converter (SAR ADC), two digital to analog converters (DAC), a multiplexer and I2C/SPI serial interfaces for connection to micro-controller (MCU). The ASIC is fabricated in a 0.35-μm CMOS technology, occupies an area of 1.5 × 3.0 mm2, and consumes a current of 3.2 mA from a 3.3 V supply. All calibration and configurations are set using the I2C/SPI serial interface connections.
Speech Act Based Classification of Email Messages in Croatian Language

Speech acts provide an effective way of summarizing the intended purpose of an email message. In this paper we address the task of speech act classification of email messages in Croatian language. We frame the task as a multilabel text classification problem. We perform thorough evaluation using six machine learning algorithms on message-level, paragraph-level, and sentence-level features. Using message-level features, we achieved an overall best F1 score of over 94%. Razvrščanje sporočil elektronske pošte v hrvaškem jeziku na podlagi govornih dejanj Govorna dejanja predstavljajo učinkovit način za povzemanje namena sporočila elektronske pošte. V članku obravnavamo nalogo razvrščanja sporočil elektronske pošte v hrvaškem jeziku na podlagi govornih dejanj. Nalogo opredelimo kot problem razvrščanja besedila na podlagi oznak. Izvedemo poglobljeno evalvacijo z uporabo šestih algoritmov strojnega učenja in več nabori značilk na različnih ravneh – na ravni sporočila, na ravni odstavka ter na ravni stavka. Z uporabo značilk na ravni sporočila smo dosegli najboljši F1
The Effect of 2 Hz and 100 Hz Electrical Stimulation of Acupoint on Ankle Sprain in Rats

The electrical stimulation of acupoint (ESA) releases several endogenous neuropeptides, which play important roles in management of pain and inflammation. ESA with low and high frequencies has been shown to release different neuropepides, suggesting its various therapeutic effects. Pain and edema are major problems for ankle sprain. However, there have been few reports on the effects of ESA for ankle sprain. We aimed to investigate that ESA can reduce pain and edema resulting from ankle sprain, and whether there is a difference in therapeutic effects between low and high frequency ESA. To induce ankle sprain in Sprague-Dawley rats, the ankle of right hindpaw was overextended in direction of simultaneous inversion and plantar flexion. Stepping force and edema in the paw of the sprained ankle were measured by electronic balance and plethysmometer, respectively. In both 2 and 100 Hz ESA groups, stepping force was increased significantly in similar degrees (p<0.05). Only 2 Hz ESA produced the significant rapid decrease in ankle edema. This study demonstrates that ESA of 2 Hz and 100 Hz shows comparable analgesic effects, but only 2 Hz ESA can facilitate the reduction of edema caused by ankle sprain.
Connecting partitioned frequency-domain filters in parallel or in cascade

The efficient implementation of connected filters is an important issue in signal processing. A typical example is the cascade of two filters, e.g., an adaptive filter with a time-invariant prefilter. The filtering and adaptation is carried out very efficiently in the frequency domain whenever filters with many coefficients are required. This is implemented as a block algorithm by using overlap-save or overlap-add techniques. However, in many real-time applications also, a short latency time through the system is required, which leads to a degradation of the computational efficiency. Partitioned frequency-domain adaptive filters, also known as multidelay adaptive filters, provide an efficient way for the filtering and adaptation with long filters maintaining short processing delays. This paper shows a computationally efficient way of implementing two or more partitioned frequency-domain filters in cascade or in parallel when their filter lengths are large. The methods presented require only one fast Fourier transform (FFT) and one inverse fast Fourier transform per input and output port, respectively. The FFT size can be even smaller than the length of the filters, The filters can be either time invariant or adaptive.
Exploiting replicated checkpoints for soft error detection and correction

Register renaming is a widely used technique to remove false dependencies in contemporary superscalar microprocessors. A register alias table (RAT) is formed to hold current locations of the values that correspond to the architectural registers. Some recently designed processors take a copy of the rename table at each branch instruction, in order to recover its contents when a misspeculation occurs. In this paper first we investigate the RAT vulnerability against transient errors. Then we analyze the vulnerability of RAT checkpoints and propose two techniques for soft error detection and correction utilizing redundantly taken copies of the entries whose content is the same with the previous and/or next checkpoints. Simulation results of the spec 2006 benchmarks reveal that on the average RAT vulnerability is 25% and checkpoint vulnerability is 6%. Results also reveal that redundancy exists at sequential checkpoint copies and can be used for error detection and correction purposes. We propose techniques that exploit this redundancy and show that faults in 41% of all checkpoints and 44% of rolled-back checkpoints can be detected and errors in 33% of the rolled-back checkpoints can be corrected. Since we exploit the already available storage, proposed error detection and correction techniques can be implemented with minimal hardware overhead.