Miracle-dz/UniFAR-FTUs huggingface dataset:
Posterior Atlantoaxial Fixation: Biomechanical In Vitro Comparison of Six Different Techniques

Study Design. Six different techniques for atlantoaxial fixation were biomechanically compared in vitro by nondestructive testing. Objective. To evaluate the immediate three-dimensional stability of a new atlas claw combined with transarticular screws and alternative techniques for transarticular screw fixation in comparison with established techniques. Summary of Background Data. Posterior transarticular screw fixation in combination with wire–bone graft constructs is frequently used for C1–C2 fixation. Sublaminar wire passage carries the potential risk of neurologic complication. Transarticular screw fixation is technically demanding and, for anatomic reasons, not always feasible. Methods. Six human cervical specimens were loaded nondestructively with pure moments, and unconstrained motion at C1–C2 was measured. The six specimens were instrumented with each of the following fixation techniques: Gallie fixation, transarticular screws and Gallie fixation, transarticular screws, transarticular screws and a new atlas claw, isthmic screws in the axis and the atlas claw, and lateral mass screws in the atlas and isthmic screws in the axis connected with rods. Results. The transarticular screws restricted lateral bending and axial rotation best. The three-point fixations (transarticular + Gallie and transarticular + claw) additionally restricted flexion–extension, with lowest values for transarticular screws and the atlas claw. The alternative techniques were not as stable as the three-point fixations, but more stable than the Gallie fixation. Conclusions. Biomechanically, the three-point fixation with transarticular screws and the atlas claw provides a rigid internal fixation that is not dependent on bone graft and sublaminar wiring. In cases wherein transarticular screws are not feasible, the isthmic screws and claw or the lateral mass screws and isthmic screws are biomechanical alternatives with less immediate stability.
Some new error estimates for Ritz-Galerkin methods with minimal regularity assumptions

New uniform error estimates are established for finite element approximations u h of solutions u of second-order elliptic equations Lu = f using only the regularity assumption ∥u∥ 1 ≤ c∥f∥ −1 . Using an Aubin-Nitsche type duality argument we show for example that, for arbitrary (fixed) e sufficiently small, there exists an h 0 such that for 0<h<h 0 ∥u − u h ∥0 ≤ e∥u − u h ∥ 1 . Here, ∥. ∥ s denotes the norm on the Sobolev space H s . Other related results are established.
Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization

Abstract : Solutions of learning problems by Empirical Risk Minimization (ERM) -- and almost-ERM when the minimizer does not exist -- need to be consistent, so that they may be predictive. They also need to be well-posed in the sense of being stable, so that they might be used robustly. We propose a statistical form of leave-one-out stability, called CVEEE(loo) stability. Our main new results are two. We prove that for bounded loss classes CVEEE(loo) stability is (a) sufficient for generalization, that is convergence in probability of the empirical error to the expected error, for any algorithm satisfying it and, (b) necessary and sufficient for generalization and consistency of ERM. Thus CVEEE(loo) stability is a weak form of stability that represents a sufficient condition for generalization for general learning algorithms while subsuming the classical conditions for consistency of ERM. We discuss alternative forms of stability. In particular, we conclude that for ERM a certain form of well-posedness is equivalent to consistency.
Unusual cell wall ultrastructure of Leptotrichia buccalis

Leptotrichia buccalis was examined by transmission electron microscopy. Its cell wall structure in generally compatible with that of gram-negative bacteria. However, the scale-like membranous folds associated with the external surface of the outer cell membrane appear to be sufficiently unusual to serve as a useful morphological criterion in the identification of L. baccalis cells.
Localized LRR on Grassmann Manifolds: An Extrinsic View

Subspace data representation has recently become a common practice in many computer vision tasks. It demands generalizing classical machine learning algorithms for subspace data. Low-Rank Representation (LRR) is one of the most successful models for clustering vectorial data according to their subspace structures. This paper explores the possibility of extending LRR for subspace data on Grassmann manifolds. Rather than directly embedding the Grassmann manifolds into the symmetric matrix space, an extrinsic view is taken to build the LRR self-representation in the local area of the tangent space at each Grassmannian point, resulting in a localized LRR method on Grassmann manifolds. A novel algorithm for solving the proposed model is investigated and implemented. The performance of the new clustering algorithm is assessed through experiments on several real-world datasets including MNIST handwritten digits, ballet video clips, SKIG action clips, DynTex++ dataset and highway traffic video clips. The experimental results show the new method outperforms a number of state-of-the-art clustering methods
Web-scale multi-task feature selection for behavioral targeting

A typical behavioral targeting system optimizing purchase activities, called conversions, faces two main challenges: the web-scale amounts of user histories to process on a daily basis, and the relative sparsity of conversions. In this paper, we try to address these challenges through feature selection. We formulate a multi-task (or group) feature-selection problem among a set of related tasks (sharing a common set of features), namely advertising campaigns. We apply a group-sparse penalty consisting of a combination of an l1 and l2 penalty and an associated fast optimization algorithm for distributed parameter estimation. Our algorithm relies on a variant of the well known Fast Iterative Thresholding Algorithm (FISTA), a closed-form solution for mixed norm programming and a distributed subgradient oracle. To efficiently handle web-scale user histories, we present a distributed inference algorithm for the problem that scales to billions of instances and millions of attributes. We show the superiority of our algorithm in terms of both sparsity and ROC performance over baseline feature selection methods (both single-task -regularization and multi-task mutual-information gain).
Do people trust their eyes more than ears?: media bias in detecting cues of expertise

Enabling users to identify trustworthy actors is a key design concern in online systems and expertise is a core dimension of trustworthiness. In this paper, we investigate (1) users' ability to identify expertise in advice and (2) effects of media bias in different representations. In a laboratory study, we presented 160 participants with two advisors -- one represented by text-only; the other represented by one of four alternate formats: video, audio, avatar, or photo+text. Unknown to the participants, one was an expert (i.e. trained) and the other was a non-expert (i.e. untrained). We observed participants' advice seeking behavior under financial risk as an indicator of their trust in the advisor. For all rich media representations, participants were able to identify the expert, but we also found a tendency for seeking video and audio advice, irrespective of expertise. Avatar advice, in contrast, was rarely sought, but -- like the other rich media representations -- was seen as more enjoyable and friendly than text-only advice. In a future step we plan to analyze our data for effects on advice uptake.
The Clinical Utility of FDG PET-CT in Evaluation of Bone Marrow Involvement by Lymphoma

Purpose Bone marrow biopsy is a standard method for the evaluation of bone marrow infiltration by lymphoma; however, it is an invasive and painful procedure. Fluorodeoxyglucose positron emission tomography–computed tomography (FDG PET-CT) is a noninvasive imaging technique with the potential to detect bone marrow involvement by lymphoma. Materials and Methods We retrospectively reviewed medical records of lymphoma patients. All patients were examined by FDG PET-CT and iliac crest bone marrow biopsy for initial staging work-up. Results The study population comprised 94 patients (median age, 60 years; 56 males) with Hodgkin’s lymphoma (n=8) or non-Hodgkin’s lymphoma (n=86). Maximum standardized uptake values on the iliac crest of patients with lymphoma infiltrated bone marrow were significantly higher than those of patients with intact bone marrow (2.2±1.2 g/mL vs. 1.3±0.4 g/mL; p=0.001). The calculated values for FDG PET-CT during evaluation of bone marrow involvement were as follows: sensitivity 50%, specificity 96%, positive predictive value 80%, negative predictive value 85%, and positive likelihood ratio (LR+) 11.7. The value of LR+ was 16.0 in patients with aggressive subtypes of non-Hodgkin’s lymphoma (NHL). Conclusion FDG PET-CT could not replace bone marrow biopsy due to the low sensitivity of FDG PET-CT for detection of bone marrow infiltration in lymphoma patients. Conversely, FDG PET-CT had high specificity and LR+; therefore, it could be a useful tool for image-guided biopsy for lymphoma staging, especially for aggressive subtypes of NHL. In addition, unilateral bone marrow biopsy could be substituted for bilateral bone marrow biopsy in lymphoma patients with increased FDG uptake on any iliac crest.
Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation.

PURPOSE Intensity modulated proton therapy (IMPT) of head and neck (H&N) cancer patients may be improved by plan adaptation. The decision to adapt the treatment plan based on a dose recalculation on the current anatomy requires a diagnostic quality computed tomography (CT) scan of the patient. As gantry-mounted cone beam CT (CBCT) scanners are currently being offered by vendors, they may offer daily or weekly updates of patient anatomy. CBCT image quality may not be sufficient for accurate proton dose calculation and it is likely necessary to perform CBCT CT number correction. In this work, the authors investigated deformable image registration (DIR) of the planning CT (pCT) to the CBCT to generate a virtual CT (vCT) to be used for proton dose recalculation. METHODS Datasets of six H&N cancer patients undergoing photon intensity modulated radiation therapy were used in this study to validate the vCT approach. Each dataset contained a CBCT acquired within 3 days of a replanning CT (rpCT), in addition to a pCT. The pCT and rpCT were delineated by a physician. A Morphons algorithm was employed in this work to perform DIR of the pCT to CBCT following a rigid registration of the two images. The contours from the pCT were deformed using the vector field resulting from DIR to yield a contoured vCT. The DIR accuracy was evaluated with a scale invariant feature transform (SIFT) algorithm comparing automatically identified matching features between vCT and CBCT. The rpCT was used as reference for evaluation of the vCT. The vCT and rpCT CT numbers were converted to stopping power ratio and the water equivalent thickness (WET) was calculated. IMPT dose distributions from treatment plans optimized on the pCT were recalculated with a Monte Carlo algorithm on the rpCT and vCT for comparison in terms of gamma index, dose volume histogram (DVH) statistics as well as proton range. The DIR generated contours on the vCT were compared to physician-drawn contours on the rpCT. RESULTS The DIR accuracy was better than 1.4 mm according to the SIFT evaluation. The mean WET differences between vCT (pCT) and rpCT were below 1 mm (2.6 mm). The amount of voxels passing 3%/3 mm gamma criteria were above 95% for the vCT vs rpCT. When using the rpCT contour set to derive DVH statistics from dose distributions calculated on the rpCT and vCT the differences, expressed in terms of 30 fractions of 2 Gy, were within [-4, 2 Gy] for parotid glands (D(mean)), spinal cord (D(2%)), brainstem (D(2%)), and CTV (D(95%)). When using DIR generated contours for the vCT, those differences ranged within [-8, 11 Gy]. CONCLUSIONS In this work, the authors generated CBCT based stopping power distributions using DIR of the pCT to a CBCT scan. DIR accuracy was below 1.4 mm as evaluated by the SIFT algorithm. Dose distributions calculated on the vCT agreed well to those calculated on the rpCT when using gamma index evaluation as well as DVH statistics based on the same contours. The use of DIR generated contours introduced variability in DVH statistics.
Semantic context modeling with maximal margin Conditional Random Fields for automatic image annotation

Context modeling for Vision Recognition and Automatic Image Annotation (AIA) has attracted increasing attentions in recent years. For various contextual information and resources, semantic context has been exploited in AIA and brings promising results. However, previous works either casted the problem into structural classification or adopted multi-layer modeling, which suffer from the problems of scalability or model efficiency. In this paper, we propose a novel discriminative Conditional Random Field (CRF) model for semantic context modeling in AIA, which is built over semantic concepts and treats an image as a whole observation without segmentation. Our model captures the interactions between semantic concepts from both semantic level and visual level in an integrated manner. Specifically, we employ graph structure to model contextual relationships between semantic concepts. The potential functions are designed based on linear discriminative models, which enables us to propose a novel decoupled hinge loss function for maximal margin parameter estimation. We train the model by solving a set of independent quadratic programming problems with our derived contextual kernel. The experiments are conducted on commonly used benchmarks: Corel and TRECVID data sets for evaluation. The experimental results show that compared with the state-of-the-art methods, our method achieves significant improvement on annotation performance.
Motion blur identification from image gradients

Restoration of a degraded image from motion blurring is highly dependent on the estimation of the blurring kernel. Most of the existing motion deblurring techniques model the blurring kernel with a shift-invariant box filter, which holds true only if the motion among images is of uniform velocity. In this paper, we present a spectral analysis of image gradients, which leads to a better configuration for identifying the blurring kernel of more general motion types (uniform velocity motion, accelerated motion and vibration). Furthermore, we introduce a hybrid Fourier-Radon transform to estimate the parameters of the blurring kernel with improved robustness to noise over available techniques. The experiments on both simulated images and real images show that our algorithm is capable of accurately identifying the blurring kernel for a wider range of motion types.
Design Thinking Based Implementation of Zoom FFT in Ultrasonic Blood Flow Analysis Using VLSI Technology

All of the body's organs require a sufficient quantity of blood. Blood flow analysis reveals its significance in disease diagnosis. There are numerous methods for examining blood flow. The impoverished cannot afford these procedures due to their exorbitant cost. Thus, we have employed a method known as Zoom-FFT. This method for identifying blood clots and other illnesses is easy to use and reasonably priced. Here, a particular application-and blood flow analyzer using ultrasonic waves employing Zoom FFT-will be discussed. Just one VLSI chip is enough to accomplish the Design Thinking-based implementation for the system to be broadly accepted which is both cost- and power-effective.
Biomechanics and Mechanotransduction in Cells and Tissues Mechanical , biochemical , and extracellular matrix effects on vascular smooth muscle cell phenotype

Stegemann, Jan P, Helen Hong, and Robert M. Nerem. Mechanical, biochemical, and extracellular matrix effects on vascular smooth muscle cell phenotype. J Appl Physiol 98: 2321–2327, 2005; doi:10.1152/japplphysiol.01114. 2004. —The vascular smooth muscle cell (VSMC) is surrounded by a complex extracellular matrix that provides and modulates a variety of biochemical and mechanical cues that guide cell function. Conventional two-dimensional monolayer culture systems recreate only a portion of the cellular environment, and therefore there is increasing interest in developing more physiologically relevant three-dimensional culture systems. This review brings together recent studies on how mechanical, biochemical, and extracellular matrix stimulation can be applied to study VSMC function and how the combination of these factors leads to changes in phenotype. Particular emphasis is placed on in vitro experimental studies in which multiple stimuli are combined, especially in three-dimensional culture systems and in vascular tissue engineering applications. These studies have provided new insight into how VSMC phenotype is controlled, and they have underscored the interdependence of biochemical and mechanical signaling. Future improvements in creating more complex in vitro culture environments will lead to a better understanding of VSMC biology, new treatments for vascular disease, as well as improved blood vessel substitutes.
Compact pulse width modulation circuitry for silicon photomultiplier readout

The adoption of solid-state photodetectors for positron emission tomography (PET) system design and the interest in 3D interaction information from PET detectors has lead to an increasing number of readout channels in PET systems. To handle these additional readout channels, PET readout electronics should be simplified to reduce the power consumption, cost, and size of the electronics for a single channel. Pulse-width modulation (PWM), where detector pulses are converted to digital pulses with width proportional to the detected photon energy, promises to simplify PET readout by converting the signals to digital form at the beginning of the processing chain, and allowing a single time-to-digital converter to perform the data acquisition for many channels rather than routing many analogue channels and digitizing in the back end. Integrator based PWM systems, also known as charge-to-time converters (QTCs), are especially compact, reducing the front-end electronics to an op-amp integrator with a resistor discharge, and a comparator. QTCs, however, have a long dead-time during which dark count noise is integrated, reducing the output signal-to-noise ratio. This work presents a QTC based PWM circuit with a gated integrator that shows performance improvements over existing QTC based PWM. By opening and closing an analogue switch on the input of the integrator, the circuit can be controlled to integrate only the portions of the signal with a high signal-to-noise ratio. It also allows for multiplexing different detectors into the same PWM circuit while avoiding uncorrelated noise propagation between photodetector channels. Four gated integrator PWM circuits were built to readout the spatial channels of two position sensitive solid-state photomultiplier (PS-SSPM). Results show a 4 × 4 array 0.9 mm × 0.9 mm × 15 mm of LYSO crystals being identified on the 5 mm × 5 mm PS-SSPM at room temperature with no degradation for twofold multiplexing. In principle, much larger multiplexing ratios are possible, limited only by count rate issues.
The Effects of Individual Differences in Imagery Ability and Working Memory Capacity on False Memories for Imagined Actions

False memories for events that never actually occurred can have detrimental effects, particularly in eyewitness testimony. The current study is used to explore how individual differences in imagery ability and working memory capacity may contribute to the formation of false memories and the phenomenological experience of both true and false memories. Results suggest that although imagery ability does not appear to affect overall false memory rate, individuals with high imagery ability appear to have more vivid memories of both experienced and imagined events than individuals with low imagery ability. Results involving working memory capacity suggest that the effect of imagery ability on the phenomenological experience of memory is not influenced by working memory capacity; however the working memory capacity scores in the sample were of limited range. Results are discussed in the context of both the Source Monitoring Framework and the Dual Process Theory. INDEX WORDS: Mental imagery, Imagery vividness, Working memory capacity, Source monitoring, False memories, Misinformation effect THE EFFECTS OF INDIVIDUAL DIFFERENCES IN IMAGERY ABILITY AND WORKING MEMORY CAPACITY ON FALSE MEMORIES FOR IMAGINED ACTIONS
Development and Crisis of the Welfare State: Parties and Policies in Global Markets

This text offers a systematic examination of the origins, character, effects and prospects of generous welfare states in advanced industrial democracies in the post-World War II era. The authors demonstrate that prolonged government by different parties results in different welfare states, with strong differences in levels of poverty and inequality. Combining quantitative studies with historical qualitative research, the authors look closely at nine countries that achieved high degrees of social protection through different types of welfare regimes: social democratic states, Christian democratic states, and "wage earner" states.
Italian households' debt: determinants of demand and supply

This paper analyzes the determinants of Italian householdsi? ½ participation in the debt market, considering both demand and supply effects and using the Bank of Italyi? ½s Survey of Household Income and Wealth. The probability of debt is positively influenced by age (until 35 years), which essentially acts as a demand factor. Among the economic variables, the role of income is important: it shows a positive correlation with the probability of debt, determined by forces acting in the same direction on both sides of the market. The uncertainty of income reduces the demand for loans, with the exception of self-employed workers, who are nevertheless subject to very rigid evaluation by lenders. Living in very small municipalities negatively affects loan demand, probably because of higher entry costs in the debt market. Education influences both the demand side, through entry costs, and banksi? ½ evaluation. Residence is a crucial parameter: beyond the negative impact of a greater economic risk, living in regions where banks face higher enforcement costs increases the probability that the loan demand is not accepted. The final part of the paper considers the size of desired debt, which seems positively linked to net wealth and future income profile. The recovered share of loans in the case of default has a positive effect on desired debt. In general, enforcement costs have increased in importance in the recent period.
Tracing the meta-level: PyPy's tracing JIT compiler

We attempt to apply the technique of Tracing JIT Compilers in the context of the PyPy project, i.e., to programs that are interpreters for some dynamic languages, including Python. Tracing JIT compilers can greatly speed up programs that spend most of their time in loops in which they take similar code paths. However, applying an unmodified tracing JIT to a program that is itself a bytecode interpreter results in very limited or no speedup. In this paper we show how to guide tracing JIT compilers to greatly improve the speed of bytecode interpreters. One crucial point is to unroll the bytecode dispatch loop, based on two kinds of hints provided by the implementer of the bytecode interpreter. We evaluate our technique by applying it to two PyPy interpreters: one is a small example, and the other one is the full Python interpreter.