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Additional file 1: Table S1. KCD-8codesof diagnoses used for defining the study population, comorbidities, andoutcomes. Table S2. The HealthInsurance Review and Assessment (HIRA) codes of treatments used in the study. Table S3. Multivariable Cox Regression Analysesof the UTIs and the OS According to Age Group and CCI. Figure S1. Kaplan-Meirsurvival curves of mortality for patients with UTIs according to treatments,age group at diagnosis, and year of diagnosis.

As artificial intelligence (AI) and machine learning (ML) technologies disrupt a wide range of industries, cloud datacenters face ever-increasing demand in inference workloads. However, conventional CPU-based servers cannot handle excessive computational requirements of deep neural network (DNN) models, while GPU-based servers suffer from huge power consumption and high operating cost. In this paper, we present a scalable systolic-vector architecture that can cope with dynamically changing DNN workloads in cloud datacenters. We first devise a lightweight DNN model description format called unified model format (UMF) that enables general model representation and fast decoding in hardware accelerator. Based on this model format, we propose a heterogeneous architecture that features a load balancer that performs a high-level workload distribution and multiple systolic-vector clusters, in which each cluster consists of a programmable scheduler, throughput-oriented systolic arrays, and function-oriented vector processors. We also propose a heterogeneity-aware scheduling algorithm that enables concurrent execution of multiple DNN workloads while maximizing heterogeneous hardware utilization based on computation and memory access time estimation. Finally, we build an architecture simulation framework based on actual synthesis and place-and-route implementation results and conduct design space exploration for the proposed architecture. As a result, the proposed systolic-vector architecture achieves 10.9x higher throughput performance and 30.17x higher energy efficiency than a compatible GPU on realistic ML workloads. The proposed heterogeneity-aware scheduling algorithm improves the throughput and energy efficiency by 81% and 20%, respectively, compared to a standard round-robin scheduling.

Because sampled-data systems have h-periodic nature with the sampling period h, an arbitraryΘ ∈ [0, h) is taken and the quasi L∞∕L₂ Hankel operator at Θ is defined as the mapping from L₂(−∞, Θ) to L∞[Θ, ∞). Its normcalled the quasi L∞∕L₂ Hankel normat Θ is used to define the L∞∕L₂ Hankel norm as the supremum of their values overΘ ∈ [0, h). If the supremum is actually attained as the maximum, then a maximum-attaining Θ is called a critical instant and the L∞∕L₂ Hankel operator is said to be well-definable. An earlier study establishes a computation method of the L∞∕L₂ Hankel norm, which is called a sophisticated method if our interest lies only in its computation. However, the feature of the method that it is free from considering the quasi L∞∕L₂ Hankel norm for any Θ ∈ [0, h) prevents the earlier study to give any arguments as to whether the obtained L∞∕L₂ Hankel norm is actually attained as the maximum, as well as detecting all the critical instants when the L∞∕L₂ Hankel operator is well-definable. This paper establishes further arguments to tackle these relevant questions and provides numerical examples to validate the arguments in different aspects of authors' theoretical interests.

Supplemental material, sj-docx-1-acr-10.1177_02841851221100318 for Development of a deep learning-based auto-segmentation algorithm for hepatocellular carcinoma (HCC) and application to predict microvascular invasion of HCC using CT texture analysis: preliminary results by Sungeun Park, Jung Hoon Kim, Jieun Kim, Witanto Joseph, Doohee Lee and Sang Joon Park in Acta Radiologica

Functional imaging data of the brain using Magnetic Resonance Imaging (MRI) – fMRI data exhibits complex but structured patterns. This fMRI data has opened a new venue for understanding the brain system at the whole-brain scale. However, the underlying origins of fMRI data are unclear and entangled. In this dissertation, I establish a variational auto-encoder, a generative model trainable with an unsupervised learning algorithm, to disentangle the unknown sources of fMRI activity. After being trained with large fMRI data in cooperation with a new reformatting strategy of input fMRI data, the model has learned the representations of cortical activity using latent variables. In Chapter 3, I found that the latent representation and its trajectory represented the spatiotemporal characteristics of fMRI activity under resting state. The latent variables reflected the principal gradients of the latent trajectory and drove activity changes in cortical networks. Latent representations were clustered by both individuals and brain states. Representational geometry captured as the covariance between latent variables, rather than cortical connectivity, was used as a more reliable feature to accurately identify subjects from a large group, even if only a short period of data was available per subjects. In Chapter 4, I further applied the VAE model pretrained with fMRI data in the resting state to new fMRI data from subjects watching naturalistic movies. I further validated that my VAE model was highly generalizable to fMRI data under different brain conditions and different scanning parameters. Additionally, I showed the task-evoked brain activity and spontaneous brain activity could be linearly separable in the VAE-derived latent space. Task-evoked latent representations and trajectory were employed to understand the dynamics of brain networks during naturalistic movie stimuli. I found that the principal gradients of the task-evoked latent trajectory were related to many aspects of the movie stimuli: low-, middle-, high-level video features. Cortical mapping of principal gradients showed the interactions between distributed cortical networks spanning from low-level sensory to high-level cognitive. Taken together, the VAE model proposed in this dissertation is a novel and effective tool that can potentially be used for understanding cortical dynamics in different brain conditions and disease conditions.

Supplemental material, sj-pdf-1-acr-10.1177_0284185121999998 for Prediction of residual tumor and overall survival after first-line surgery in patients with pancreatic ductal adenocarcinoma using preoperative magnetic resonance imaging findings by Jae Seok Bae, Jung Hoon Kim, Hyo-Jin Kang and Joon Koo Han in Acta Radiologica

Additional file 1. Details regarding cell density in the hippocampal region and serum level changes in TNF-α and IL-6 with tMCAO-mediated brain injury and pre-treatment with RAGE are available in additional information.

The rhizome of Atractylodes japonica, which is a herbal medicine used for gastrointestinal therapeutics, has been categorized with A. macrocephala rhizome or A. chinensis rhizome based on different therapeutic criteria in Korea, China, and Japan. In the present study, 61 A. japonica, A. macrocephala, and A. chinensis rhizomes were collected from Korea and China and were genetically identified by internal transcribed spacer sequencing analysis. Chromatographic profiles were obtained from high-performance liquid chromatography analysis of the methanol and hot-water extracts of Atractylodes rhizomes and chemical differentiation of the rhizomes was carried out using chemometric statistical analyses such as principal component analysis, hierarchical clustering analysis, and Pearson’s correlation coefficient analysis. The results from chromatographic profiles and chemometric analyses demonstrate that A. japonica rhizomes showed apparent chemical differences from A. macrocephala and A. chinensis rhizomes in the methanol extracts. In contrast, no clear distinction was apparent for the hot-water extracts of Atractylodes rhizomes, especially A. chinensis rhizomes. These results indicate that there is a clear chemical difference between A. japonica and A. macrocephala rhizomes; however, the chemical diversity of A. chinensis rhizome shows different chemical relationships with A. japonica or A. macrocephala rhizome, dependent on the chemical features.

Rationale: Primary immune thrombocytopenia (ITP) is an immune-mediated disease that is defined as increased platelet destruction and impaired platelet production. Treatment is recommended for highly selected patients, the standard regimen includes glucocorticoid, intravenous immunoglobulin (IVIG). The recombinant thrombopoietin (TPO) receptor agonists, romiplostim, stimulate platelet production and have approved for glucocorticoid or IVIG, splenectomy-refractory chronic ITP patients. Patient concerns: A patient has been diagnosed with ITP, reftractory to steroid, IVIG, splenectomy, danazol, and cyclosporine. The patient received romiplostim to normalize his platelet count, however, over the course of the following year, his platelet counts progressively decreased despite increasing the romiplostim dosing. Diagnoses: A peripheral blood smear showed a severe leukoerythroblastic reaction and bone marrow biopsy demonstrated myelofibrosis due to romiplostim. Outcomes: Since this diagnosis, romiplostim was discontinued for a while, after 3 months, romiplostim was re-administered to improve thrombocytopenia. His platelet count recovered to 70,000/mm3 after the administration of romiplostim at 2 μg/kg, and he did not experience complications for 6 months. Lessons: This report represents the first evidence of romiplostim-induced myelofibrosis, which was associated with increased levels of bone marrow reticulin and Masson trichrome staining.

This paper provides a discretization method for computing the induced norm from L₂ to L∞ in single-input/ single-output (SISO) linear time-invariant (LTI) sampled-data systems. We first follow the lifting-based treatment for the induced norm from L₂ to L∞ of SISO LTI sampled-data systems, but further apply the key idea of fast-lifting, by which the sampling interval [0, h) is divided into M subintervals with an equal width. Such an idea allows us to develop two methods for computing the induced norm with gridding and piecewise constant approximations. These methods leads to approximately equivalent discretization methods of the generalized plant that can be used for readily computing upper and lower bounds of the induced norm together with the derivation of the associated convergence rates. More precisely, it is shown that the approximation error converges to 0 at the rate of 1/√M and 1/M in the gridding and piecewise constant approximation methods, respectively.

Recent experiments and numerical simulations demonstrated that discrete roughness elements can be used to control cross-flow instability over a swept wing. Here, the application of this passive technique requires a row of thin cylindrical elements of a few microns high immediately downstream of the leading edge to excite the subcritical modes of cross-flow instability. By properly choosing the spanwise spacing of these roughness elements, one can suppress the growth of most unstable modes, thereby delay transition. However, this passive technique of controlling cross-flow instability is very sensitive to the size (diameter and height), shape and location of discrete roughness. To mimic the discrete roughness elements and to be able to adjust the roughness parameters dynamically, virtual roughness elements based on dielectric-barrier-discharge plasma actuators have been developed and tested. In this paper, we show the plasma-induced flow field of several different prototype virtual-roughness elements for cross-flow instability control, by describing the mechanisms of vortex generation from the virtual roughness elements through an interaction with the incoming laminar boundary layer.

We present a focal liver lesion detection model leveraged by custom-designed multi-phase computed tomography (CT) volumes, which reflects real-world clinical lesion detection practice using a Single Shot MultiBox Detector (SSD). We show that grouped convolutions effectively harness richer information of the multi-phase data for the object detection model, while a naive application of SSD suffers from a generalization gap. We trained and evaluated the modified SSD model and recently proposed variants with our CT dataset of 64 subjects by five-fold cross validation. Our model achieved a 53.3% average precision score and ran in under three seconds per volume, outperforming the original model and state-of-the-art variants. Results show that the one-stage object detection model is a practical solution, which runs in near real-time and can learn an unbiased feature representation from a large-volume real-world detection dataset, which requires less tedious and time consuming construction of the weak phase-level bounding box labels., Comment: Accepted at MICCAI 2018. 8 pages, 4 figures, 1 table

Figure S1. Physicochemical characterization of Tm80. (a) Absorbance spectra, FT-IR spectra; and (b) 1H-NMR spectra of 3-MPA (black line), T80 (blue line), and Tm80 (red line). Figure S2. Schematics showing the volume of oxygen ionic radius in single FCC unit. Figure S3. Half-life of anti-HER2 aptamer in serum: the half-life of control (non-modified), 3 h; NapdU-modified, 151 h. Figure S4. In vivo MR images of HER2+ tumor mouse model (a) AptHER2-MNS—injected group, (b) WMNC—injected group. Scale bars, 5 mm. Table S1. Relative intensities of in vivo MR images measured from figure 5 and S4 (red solid-lined ROI) (DOCX 1110 kb)

This paper provides a method for the L 1 analysis of sampled-data systems, by which we mean the computation of their L ∞ -induced norm. We first apply the lifting approach to sampled-data systems and derive an operator theoretic representation of their input/output relation. We then apply fast-lifting by which the sampling interval [0, h) is divided into M subintervals with an equal width, and provide methods for computing the L ∞ -induced norm. Specifically, we use an idea of kernel approximation approach, in which the kernel function of an input operator and the hold function of an output operator are approximated by staircase or piecewise linear functions. Furthermore, it is shown that the approximation errors in staircase or piecewise linear approximation are ensured to be reciprocally proportional to M or M ², respectively.

This study deals with the L₁ analysis of stable finite-dimensional linear time-invariant (LTI) systems, by which the authors mean the computation of the L∞-induced norm of these systems. To compute this norm, they need to integrate the absolute value of the impulse response of the given system, which corresponds to the kernel function in the convolution formula for the input/output relation. However, it is very difficult to compute this integral exactly or even approximately with an explicit upper bound and lower bound. They first review an approach named input approximation, in which the input of the LTI system is approximated by a staircase or piecewise linear function and computation methods for an upper bound and lower bound of the L∞-induced norm are given. They further develop another approach using an idea of kernel approximation, in which the kernel function in the convolution is approximated by a staircase or piecewise linear function. These approaches are introduced through fast-lifting, by which the interval [0, h) with a sufficiently large h is divided into M subintervals with an equal width. It is then shown that the approximation errors in staircase or piecewise linear approximation are ensured to be reciprocally proportional to M or M², respectively. The effectiveness of the proposed methods is demonstrated through numerical examples.

This paper deals with the L1 analysis of linear sampled-data systems, by which we mean the computation of the L∞-induced norm of linear sampled-data systems. Two computation methods based on piecewise constant and piecewise linear approximations are provided through fast-lifting, by which the sampling interval [0, h) is divided into M subintervals with an equal width. Even though the central part of the method with the former approximation essentially coincides with a conventional method via fast-sample/fast-hold (FSFH) approximation after all, we show that both methods successfully lead to the upper and lower bounds of the L∞-induced norm, whose gap converges to 0 at the rate of 1/M in the former approximation and 1/M{2} in the latter extended approximation. Such achievements are in sharp contrast with an existing result on the former (i.e., FSFH) approximation, which only shows the convergence rate of the error in the resulting estimate of the L∞-induced norm, without providing any readily computable upper and lower bounds. A numerical example is given to illustrate the effectiveness of these methods.

The herbal combination is the basic unit of a herbal formula that affects the chemical characteristics of individual herbs. In the present study, a method of simultaneous determination of the 11 marker compounds in Jakyakgamcho-tang was developed using high-performance liquid chromatography with photodiode array detection. The validated analytical method was successfully applied to approach the chemical interaction between Paeonia lactiflora and Glycyrrhiza uralensis in co-decoction. In P. lactiflora, the contents of gallic acid, oxypaeoniflorin, (+)-catechin, paeoniflorin, and benzoylpaeoniflorin were decreased, while those of albiflorin and benzoic acid were increased; in G. uralensis, the contents of liquiritin, isoliquiritin, ononin, and glycyrrhizin were decreased, when decocting two herbs together. Moreover, as the ratio between P. lactiflora and G. uralensis was increased, the contents of chemical contents from each herb were proportionally increased. However, each content of marker compound per the gram of herbal medicine was decreased as the ratio of combinative herbs increased. The results showed that P. lactiflora and G. uralensis affect the extraction efficiency of chemical compounds in a Jakyakgamcho-tang decoction. Overall, the method established in this study was simple, rapid, and accurate, and would be useful for the determination of marker compounds and for the investigation of the chemical interaction between herbal medicines.

Purpose: To evaluate the usefulness of dynamic contrast-enhanced ultrasonography (DCE-US) in the early quantification of hemodynamic change following administration of the vascular disrupting agent (VDA) CKD-516 using a rabbit VX2 liver tumor model. Methods: This study was approved by our institutional animal care and use committee. Eight VX2 liver-tumor-bearing rabbits were treated with intravenous CKD-516, and all underwent DCE-US using SonoVue before and again 2, 4, 6, and 24 hours following their treatment. The tumor perfusion parameters were obtained from the time-intensity curve of the DCE-US data. Repeated measures analysis of variance was performed to assess any significant change in tumor perfusion over time. Relative changes in the DCE-US parameters between the baseline and follow-up assessments were correlated with the relative changes in tumor size over the course of seven days using Pearson correlation. Results: CKD-516 treatment resulted in significant changes in the DCE-US parameters, including the peak intensity, total area under the time-intensity curve (AUCtotal), and AUC during wash-out (AUCout) over time (Ptotal and AUC during wash-in (AUCin) seen on the two-hour follow-up were significantly lower than the baseline values (P Conclusion: Our results suggest that a novel VDA (CKD-516) can cause disruption of tumor perfusion as early as two hours after treatment and that the therapeutic effect of CKD-516 treatment can be effectively quantified using DCE-US.

As malfunction/absence of immune cells causes a variety of immunosuppressive disorders and chemical synthetic drugs for curing these diseases have many adverse effects, vigorous studies are being conducted. The Acanthopanax family has been used as traditional medicines for gastric ulcer, diabetes, etc. and culinary materials in East-South Asia. In this study, the immunostimulating properties of A. sessiliflorus were evaluated. A. sessiliflorus increased not only the splenocyte number but also immune-related cytokines such as TNF-α. However, it could not upregulate the expressions of IFN-γ and IL-2. A. sessiliflorus increased the swimming time, and comparison of organ weights relative to body weights for immune-related organs such as the spleen and thymus after a forced swim test showed that it could recover the spleen and thymus weights. It also increased the expression of TNF-α and slightly increased the concentration of IFN-γ but not IL-2. From the results, we concluded that as A. sessiliflorus has not only a host defense effect but also a stress-ameliorating property, further study it will be a promising material of immunostimulating material.

Arbeit an der Bibliothek noch nicht eingelangt - Daten nicht geprüft Graz, Univ., Dipl.-Arb., 2015

Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: Due to development of MDCT, Computed tomography is taking major role in diagnostic area. Good resolution and short examination time is major advantage of CT in medical environment where speed is virtue. However, radiation dose of CT is inevitable...

The AIS(Automatic Identification System) is the useful equipment in order to promote the maritime safety and real time monitoring at the sea. In this study, the plan to construct the ubiquitous Aids to navigation is proposed. In order to verify the construction of ubiquitous Aids to navigation, the experiment on the sea was carried out and analyzed.

The port entry system of the inner harbor in PyeongtaekDangjin was planned as lock-gate in `Master plan project on port planning in Asan industry base(1990)‘, but was changed to tidal harbor in `Project maintaining Master plan for comprehensive development of PyeongtaekDangjin port(2001)`. Accordingly, southern sea bank constructed under the lower part of Seohae-bridge will be removed so that inbound/outbound vessels for the inner harbor can navigate at all times. However, in the view of the safety on passing through the lower of Seohae-bridge, navigating conditions for the inner harbor will be restricted in the single-way of 50,000 DWT vessel and the two-way of vessel less than 30,000 DWT Therefore, this study carried out the estimation of traffic congestion arising from these vessels with above restrictions q[ter supposing annual inbound/outbound vessel`s numbers for loading and unloading cargo surveyed on the inner harbor.

This paper compares the money beliefs and behaviors of Japanese (J) college students and Asian American (AA) college students and to identify gender differences in each country by using Money Beliefs and Behaviors Scale instrument. As compared with J, AA student's money attitude is formed by the relationship with other people. AA are very conscious about how their money attitude is seen. While. J have a strong tendency to save money and they feel anxiety if they do not have enough cash. Such an attitude is linked to the attitude that they think money is the only thing they can count on and the attitude they do not want to borrow money. In both Japan and the U.S., male students agreed with the opinions in which money is used as a tool buying friendship and a weapon controlling people. Male students' power oriented money attitude is seen common in both countries. Female students in both countries have a similar tendency to value saving attitude. They preferred to save money more than male students. Then, in both countries female students spend money when they are frustrated. Typical money attitudes in which gender roll is reflected are observed in both countries., 紀要論文

A 34-year-old Asian woman presented with painful corneal bee sting. Examinations revealed severe corneal swelling with stinger stuck in deep stroma and endothelial cell loss. She was treated with early surgery including stinger removal and anterior chamber irrigation combined with systemic high-dose steroid therapy. Vision and corneal clarity was recovered in 5 days and no additional corneal endothelial damage was observed. This report suggests that early surgical intervention and high-dose steroid therapy appear to be a useful option in the treatment of corneal bee sting.

The goal of this research is to develop effective maintenance procedures and quantifying maintenance schedules for stormwater treatment systems, known as Hydrodynamic Separators (HDS). The HDS has started to be widely used with the purpose of removing pollutants from stormwater runoff and its use is expected to continue in the foreseeable future. Therefore, determining optimum maintenance procedures, intervals and costs is vital for their successful utilization. Information on the characteristics and location of each installed HDS is collected and identified through a field monitoring and maintenance study. Based on this information, twelve (12) HDS were selected and three (3) data forms are developed: asset data form, inspection data form, and maintenance data form to help level the playing field, properly track the devices, and inspect and maintain the devices in a timely fashion and in a cost-effective way. For analysis of quantity and quality of floatables, stormwater, and sediment, the quantities of bottom sediment, oil, and buoyant debris were measured and the samples of trapped water and bottom sediment were taken for the quality analysis. Measured quantity and quality of the trapped stormwater solids varied widely from site to site. Total depth of the bottom sediment ranged from 2.& feet (exceeding the maintenance limit of 2 feet) to 0.5 feet. On average, about 90 percent of the solids trapped at the bottom had a mean particle size larger than &5 microns: coarse sediment. Organic content of the bottom sediment ranged from 3 to 34 percent. Concentrations of all the measured heavy metals (copper, zinc, lead, cadmium, and arsenic) in the bottom sediment were much lower than the New Jersey residential soil contamination limits indicating that the bottom solids can be disposed of at standard sanitary landfills. Concentrations of phosphorus and nitrogen in the bottom sediment were much lower than those in typical sewage sludge. The units were frequently (every two to three months) and continuously monitored across a full spectrum of storms. After three years of monitoring, six (6) devices had reached capacity, and they were cleaned out and restarted for monitoring. As a result of the monitoring and evaluation, an ideal and efficient maintenance procedure and interval are determined. For the general sites, the maintenance intervals were measured to be from three to four and one half years. For planning future maintenance/cleanout activities, it is recommended that the predictive model be used with the number of vehicles on the road(s) and the impervious drainage area as inputs.

The dissertation discusses the possibility of a reconciliation between neo-Kaleckian and classical (neo-Marxian/Sraffian) two-sector growth models, and it explores the issues relative to the long-run convergence of actual degrees of capacity utilization towards their normal values. In a simplified analytical framework, that examines regimes incorporating neo-Kaleckian and neo-Marxian/Sraffian perspectives, the thesis shows that the paradox of thrift and the paradox of costs hold both in the short run and in the long run. A more sophisticated two-sector Kaleckian model is also developed within a stock-flow consistent framework that includes conflicting-claim inflation and Kaldorian technical progress. Simulation results show that inflation-targeting policies based on the so-called 'Taylor rule' could remove the long-run paradoxes, leading to economic depression. These policies also fail to achieve normal rates of capacity utilization, because of 'path-dependency', in contrast to the arguments put forth by some neo-Marxists and the proponents of the neoclassical New Consensus. Furthermore, our empirical tests show that the Phillips curve has a horizontal segment within a large intermediate range of capacity utilization rates, using Canadian data over the last two decades. This horizontal Phillips curve implies that the adjustment mechanisms suggested by classical models just may not exist in reality during this time period. Therefore, the thesis offers some justification for using simple neo-Kaleckian growth models where effective demand plays a crucial role in the determination of capacity utilization, employment and growth, both in the short run and in the long run, and it suggests the use of expansionary fiscal policy and monetary policy as long as the economy remains within the intermediate range of capacity utilization. Key words: Two-Sector Growth Model; Long-Run Convergence; Paradox of Thrift; Paradox of Costs; Inflation; Phillips Curve; Monetary Policy; Stock-Flow Consistent Approach.

Background: Along with the biological risk factors, social environment plays an important factor for childhood asthma. Aims: This study aimed to evaluate whether household income is associated with...

Background: Atopic diseases have been increasing over the years in Korea. It seems that environmental factors including hazardous chemicals in products which are used in everyday life may be associ...

Background: Recent studies suggest an association between the exposure to phthalate and atopic diseases in children. Aims: We aimed to evaluate whether the concentrations of urinary mono-n-butyl ph...

This paper gives two methods for the L₁ analysis of sampled-data systems, by which we mean computing the L∞-induced norm of sampled-data systems. This is achieved by developing what we call the kernel approximation approach in the setting of sampled-data systems. We first consider the lifting treatment of sampled-data systems and give an operator theoretic representation of their input/output relation. We further apply the fast-lifting technique by which the sampling interval [0, h) is divided into M subintervals with an equal width, and provide methods for computing the L∞-induced norm. In contrast to a similar approach developed earlier called the input approximation approach, we use an idea of kernel approximation, in which the kernel function of an input operator and the hold function of an output operator are approximated by piecewise constant or piecewise linear functions. Furthermore, it is shown that the approximation errors in the piecewise constant approximation or piecewise linear approximation scheme converge to 0 at the rate of 1/M or 1/M², respectively. In comparison with the existing input approximation approach, in which the input function (rather than the kernel function) of the input operator is approximated by piecewise constant or piecewise linear functions, we show that the kernel approximation approach gives improved computation results. More precisely, even though the convergence rates in the kernel approximation approach remain qualitatively the same as those in the input approximation approach, the newly developed former approach could lead to quantitatively improved approximation errors than the latter approach particularly when the piecewise linear approximation scheme is taken. Finally, a numerical example is given to demonstrate the effectiveness of the kernel approximation approach with this scheme.

In capital markets research, market expectation of future earnings plays a vital role. However, almost all proxies inevitably measure the market expectation of future earnings with error, which results in unsatisfactory empirical outcomes in prior research (e.g., small empirical values of earnings response coefficient and poor quality estimates of expected rates of return). Using analysts' consensus forecasts, this study investigates how noisy measurement of the market expectation of future earnings affects the predictability of future earnings and stock returns. Based on the errors-in-variables approach, this study first provides a framework to capture cross-sectional variation of the measurement error in analysts' consensus forecasts. With this framework in place, this study documents that analysts' consensus forecasts with more measurement error have less ability to predict future earnings and stock returns, and that incorporating information about cross-sectional variation of the measurement error can improve the predictability of future earnings and stock returns. These findings will be useful to accounting research that relies on the market expectation of future earnings and to practitioners seeking to forecast profitability and stock returns.

Magnetic susceptibilities at several potential ordering wave vectors (0,0), (\pi,0), and (\pi,\pi) are analyzed for the antiferromagnetic J1-J2 spin Hamiltonian by classical Monte Carlo and Schwinger boson mean field theories over the parameter range 0 \le 2J_2/J_1 \le 2. We find a nearly linear-T behavior of the uniform susceptibility that extends up to the temperature scale of J_1 within both calculation schemes when 2J_2/J_1 is sufficiently removed from the critical point 2J_2/J_1=1. The window of linear temperature dependence diminishes as the critical point is approached., Comment: 9 pages, 5 figures