Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Cortical Activity During Auditory Processing with Functional Near-Infrared Spectroscopy

Functional near-infrared spectroscopy (fNIRS) is an optical, non-invasive neuroimaging technique that investigates human brain activity by calculating concentrations of oxy- and deoxyhemoglobin. The aim of this publication is to review the current state of the art as to how fNIRS has been used to study auditory function. We address temporal and spatial characteristics of the hemodynamic response to auditory stimulation as well as experimental factors that affect fNIRS data such as acoustic and stimulus-driven effects. The rising importance that fNIRS is generating in auditory neuroscience underlines the strong potential of the technology, and it seems likely that fNIRS will become a useful clinical tool.
The Editor vs. the Algorithm: Targeting, Data and Externalities in Online News

We run a field experiment with a major news outlet to quantify the economic returns to data and informational externalities associated with algorithmic recommendation. Automated recommendation can outperform a human editor in terms of user engagement, though this crucially depends on the amount of training data. Limited individual data or breaking news leads the editor to outperform the algorithm. Additional data helps algorithmic performance but decreasing economic returns set in rapidly. Investigating informational externalities highlights that personalized recommendation reduces consumption diversity. Moreover, users associated with lower levels of digital literacy and more extreme political views engage more with algorithmic recommendations.
A Fast Image Region Segmentation Method Based on Gaussion Statistical Model

According to the randomness of image information and the timeliness of facing application,a fast image region segmentation algorithm based on the Gaussion statistical model is introduced in this paper.Compared with some existed image region segmentation algorithms,the method which this article proposed establishes the Gaussion statistical model using the noise instead of image denoising.Besides,max-min method is also used in this article and the initial division thought is proposed.The experiments indicate that the new method strengthened the stability of division result,raised the division speed and also had conformed to the accuracy and the timely image request.
Erebus , the largest point source of NO 2 in Antarctica

We report here the first observations of NO2 emission from Mt. Erebus, a volcano with an active lava lake located on Ross Island, Antarctica. Erebus generates a persistent plume, which is entrained at an altitude of about 4 km above sea level. Its NO2 flux, measured by scattered light ultraviolet spectroscopy in December 2003, was equivalent to 0.6Gg (N) yr . The total reactive nitrogen supply may be significantly higher than this since other NOy species are likely to have been present in the plume. We believe the NO2 is generated by thermal fixation of atmospheric nitrogen at the hot lava surface, forming NO, which then reacts rapidly with oxidants including ozone to yield NO2. Erebus volcano has displayed lava lake activity for many decades and may, therefore, play a significant long-term role in Antarctic tropospheric chemistry, and represent an important source of nitrogen deposited to the ice surface. r 2005 Elsevier Ltd. All rights reserved.
Household ( Under ) Adoption of Sanitation : Externalities and Borrowing Constraints ∗

This paper analyses under-adoption of sanitation and the effectiveness of loans and price subsidy policies to increase coverage in the developing world. Under-adoption may be the result of externalities and borrowing constraints: while sanitation is an expensive investment for many poor potentially liquidity constrained households, it also generates positive health externalities. To investigate the impact of the two policies on sanitation coverage I estimate a dynamic model of household demand on a unique dataset from rural India. The model embeds both sources of market failure in order to compute equilibrium adoption levels under the loan and the subsidy policy. Using simulations from the estimated model, I study optimal policy design in an equilibrium setting with potential multiple equilibria. Counterfactual analysis reveals that existing sanitation levels are on average 53% below the social optimum, implying under-adoption. I find price subsidies to be more cost effective at increasing sanitation coverage. However, the policy impacts are heterogeneous by coverage levels: in villages with low coverage loans are equally, if not marginally more, effective. A price subsidy has a high social rate of return where externalities account for a substantial fraction of the policy impact. While a sanitation loan policy generates smaller social returns it is also cost efficient under targeted delivery.
Loss of cutaneous unmyelinated and myelinated fibers in streptozotocin-induced diabetic rats : a time course study

Background: Quantification of density of intraepidermal nerve fibers (IENFD), targeting mainly unmyelinated C fibers using skin biopsies, has emerged as a promising method for assessment of diabetic peripheral neuropathy. However, detailed histological changes in unmyelinated fibers in combination with large myelinated fibers in rat models have not been adequately explored. Methods: The current study assessed both cutaneous unmyelinated and myelinated fiber changes, as well as pain-related behavior, in an 8-week streptozotocin (STZ)-induced rat model of diabetic mellitus (DM). Mechanical allodynia and thermal hyperalgesia were evaluated at baseline and weekly after injections with STZ. Quantification of IENFD, dermal nerve fiber (DNF) density, and density of intrapapillary myelinated endings (IMEs) was conducted at weeks 4, 6, and 8 post-injection. Results: Mechanical allodynia started at 1-week post-injection. It progressed through week 5, then plateaued at week 8. Thermal hyperalgesia occurred at week 4 and only lasted through week 5 in DM rats. IENFD reduction had already occurred at week 4. However, both DNFL and IME densities were still comparable at that time. IME density in the DM rats started to decrease right after week 4. It was significantly reduced at week 8. DNFL reduction in the DM rats did not occur until week 8. Conclusion: Present results show early persistent mechanical allodynia and delayed temporary hyperalgesia in this STZ-induced diabetic rat model. Significant IENF loss preceded changes in IMEs and DNFs, suggesting that IENFD quantification might be a potential marker for detection of early diabetic neuropathy.
Feature Selection and Classification for Chinese Web Documents

A wide range of methods for feature selection have been applied to English Web documents, relatively few studies have been done on Chinese Web documents. This paper gives a term weighting method based on inverse document frequency, HTML tags and length of Chinese phrase, presents the method to select Web text feature based on the messy genetic algorithm. The experiments show that the method is resultful .
PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification

The Electrocardiogram (ECG) is performed routinely by medical personnel to identify structural, functional and electrical cardiac events. Many attempts were made to automate this task using machine learning algorithms including classic supervised learning algorithms and deep neural networks, reaching state-of-the-art performance. The ECG signal conveys the specific electrical cardiac activity of each subject thus extreme variations are observed between patients. These variations are challenging for deep learning algorithms, and impede generalization. In this work, we propose a semisupervised approach for patient-specific ECG classification. We propose a generative model that learns to synthesize patient-specific ECG signals, which can then be used as additional training data to improve a patient-specific classifier performance. Empirical results prove that the generated signals significantly improve ECG classification in a patient-specific setting.
DEM Working Paper Series Heterogeneity in Sectoral Price Stickiness , Aggregate Dynamics and Monetary Policy Pitfalls with Real Shocks 1

6 Ample di¤erences in sectoral price stickiness is a widely documented fact. 7 This paper shows that in presence of real shocks, heterogeneity in sectoral price 8 stickiness plays a key role in the determination of the aggregate dynamics. The 9 larger price stickiness heterogeneity, the smaller the persistence of ination and 10 the volatility of ination, interest rate and output-gap. Thus, two economies 11 with the same average degree of sectoral price stickiness but unlike variance 12 may behave very di¤erently. In terms of monetary policy, they can require in13 terest rate paths that substantially di¤er both qualitatively and quantitatively. 14 Generally, with real shocks, disregarding the dispersion in sectoral price stick15 iness leads policymakers to overvalue the variation and persistence of ination 16 and output gap. 17 JEL Classi cation: E31, E32, E37, E52. 18 Key Words: Price Stickiness Heterogeneity, Real Shocks, New Keynesian 19 model, Persistence, Volatility, Monetary Policy Mistakes. 20 Current address: Department of Economics, University of Pavia, Via S. Felice 5, 27100 Pavia, Italy. Email: alessandro. amini@unipv.it. Tel: +39 0382 986 203 (o¢ce). I am grateful to Guido Ascari, Mustafa Caglayan, Alpay Filiztekin and Paul Levine. I have also bene ted from seminar participants at the University of Pavia, University of Nottingham, University of Surrey, and the National Bank of Poland conference "Monetary Transmission Mechanism in Diverse Economies".
A Typology of Carsharing Customers in Montreal Based on Large-Scale Behavioural Dataset

The emerging modes of transportation in the recent years, are a pointer to the fact that owning a private car is not all we need for the sake of traveling. Confirmed by the researchers, many features might be the reasons of choosing a mode of transportation. The cost, parking issues and environmental effects are some of the major features. Carsharing or in general vehicle sharing is one of these recent modes whose mission is providing the population with a travelling facility that is cheaper, easier and more environmentally friendly. Communauto, founded in 1994 in Quebec City, Canada, is now one of the major carsharing companies in Montreal, Canada. Using k-means clustering and Principal Component Analysis, the purpose of this paper is to study the behaviour of the Communauto regularservice carsharing users in Montreal over a year (2014) and find the usage patterns in each cluster. Also, by having the Communauto customer features available, the characteristics of the customers in each cluster will be defined. While previously several works in this field are performed utilising k-means clustering on the original data, our methodology emphasizes on implementing Principal Component Analysis (PCA) before k-means. Although, it always depends on the data characteristics, for the Communauto available data the assumption of k-means, i.e. the distribution of the data is spherical, could not be met without PCA transformation. However, without considering this assumption the accuracy of clustering results on the original data was always less than on the PCA transformed data. The k-means clustering results show that the Communauto regular-service carsharing users, are divided into nine different clusters. Some of the clusters patterns are similar during the weeks and some are similar during a year, but none of them are similar during week and year. So, each cluster has a unique usage pattern over the weeks and the year. Furthermore, the resulting clusters are ordered from high intensive users to the occasional ones based on the frequency of carsharing usage over one year.
A Network Analysis on the Combinatorial Generativity of a Platform Ecosystem: The Case of WordPress

Contemporary platform ecosystems are characterized by their generativity. In such platform ecosystems, third-party developers primarily drive generative innovations by building new complementary products, such as plug-ins by combining the boundary resources such as API (Application Program Interface) offered by the focal firm of a platform ecosystem. However, not all boundary resources are developed by the focal firm, and precisely how different boundary resources affect the re-combinatorial design actions by thirdparty developers is not well understood. Furthermore, we do not know how individual combinatorial design actions by third-party developers affect the overall generativity of the platform ecosystem. To answer these questions, we draw on a combinatorial perspective on innovation and a network perspective to model how different types of APIs influence third-party developers’ design decisions, which in turn collectively shape a seemingly ever-changing platform ecosystem. We conduct an empirical study using data from WordPress.org, the world’s largest content management platform ecosystem using text mining and network analysis to extract how third-party developers use different APIs in creating plug-ins. Specifically, we construct a plug-in network where edges between nodes indicate how different APIs are used by different plug-ins. The plug-in network allows us to examine how third-party developer use different combinations of APIs to build their plug-ins and, how such decisions collectively influence the diversity of the entire platform ecosystem. We find that the generativity of the platform ecosystem exponentially increased over time. Over that 10 years that we studied, WordPress ecosystem has grown in not only in size but in scope with 11 unique clusters of plug-ins, represents a high degree of product variety. We further discover that the generative growth of WordPress ecosystem was fueled by the generative combination of APIs that belong in three distinct layers: complete core boundary resources, regular core boundary resources, and periphery boundary resources. The combination patterns of these APIs shape the generativity of this digital ecosystem. WordPress controls the APIs in the complete core, shaping the overall boundary of the ecosystem. At the same time, we show that the generativity measured by the product varieties in each cluster is shaped by the APIs in the regular core layer, some of which are owned by external players. External APIs generally help to increase the generativity in function among clusters.
Contribution POINT SHEARWAVE ELASTOGRAPHY USINGMACHINE LEARNING TO DIFFERENTIATE RENAL CELL CARCINOMA AND ANGIOMYOLIPOMA

T AGGEDPHERSH SAGREIYA MD* ALIREZA AKHBARDEH PHD* DANDAN LI, MD, PHD* ROSA SIGRIST MD* BENJAMIN I. CHUNG MDy GEOFFREY A. SONN MD*,y LU TIAN PHDz DANIEL L. RUBIN MD, MS*,x,{ and JÜRGEN K. WILLMANN, MD* T AGGEDEND *Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA; yDepartment of Urology, Stanford University School of Medicine, Stanford, CA, USA; zDepartment of Health, Research & Policy, Stanford University, Stanford, CA, USA; xDepartment of Biomedical Data Science, Stanford University, Stanford, CA, USA; and {Department of Medicine (Biomedical Informatics Research), Stanford University, Stanford, CA, USA
Efficacy of anterior cervical discectomy and fusion versus artificial cervical disc replacement for cervical degenerative disease

Objective: To compare the clinical efficacy of anterior cervical discectomy and fusion (ACDF) and artificial cervical disc replacement (ACDR) in the management of cervical degenerative disease in patients. Methods: Seventy-nine patients with cervical degenerative disease admitted to The First Hospital of Nanping City Affiliated to Fujian Medical University from January 2012 to December 2014 were assigned to undergo ACDF or ACDR and followed for 3 years. In the ACDF group, 24 patients were male and 19 were female, with an age range of 43 to 55 years (mean, 48.9±1.6 years). All the patients had 36 months of follow-up. In the ACDR group, 21 patients were male and 15 were female and they varied in age from 42 to 57 years (mean, 49.1±1.4 years) and were followed up for 36 months. There were no remarkable differences between the two groups in basic data, as well as imaging data at baseline. The Japanese Orthopaedic Association (JOA) score, the neck disability index (NDI), the visual analogue scale (VAS) for pain in the neck and upper-limb, the Odom scale score, the cervical spine range of motion (ROM) and the cervical curvature index were employed to assess the clinical efficacy of the surgeries in the patients. Results: Greater improvements in the JOA, VAS, and NDI scores after surgery were noted than those before surgery in the two groups (P<0.05), but the corresponding scores were insignificantly different between the two groups (P>0.05). At the final follow-up after surgery, the excellent and good rate (83.72%) of the Odom scores in the ACDF group was insignificantly different from that (88.89%) in the ACDR group (P>0.05). The surgical segments in the ACDF group were fully fused, but the ROM of the surgical segments in the ACDR group was 7.4±3.9, which was mildly different from than before surgery. The cervical spine ROM in the ACDF group was remarkably smaller than that of the ACDR group (P<0.05), and that of the ROM group before surgery (P<0.05). However, the ROM of the cervical spine in the ACDR group at final follow-up after surgery differed insignificantly from that before surgery (P>0.05). The values for the cervical curvature index were strikingly lower in the ACDF group than in ACDR group at 3, 12, 24, and 36 months after surgery. The cervical curvature index in the ACDF group at 36 months was remarkably lower than that before surgery (P<0.05), but the cervical curvature index in the ACDR group was insignificantly different from that before surgery (P>0.05). Conclusion: Among the patients with cervical degenerative disease, the clinical efficacy of ACDR was similar to that of ACDF, but ACDR was superior to ACDF in maintaining the ROM and physiological curvature of the cervical spine.
Intelligent Fault Diagnosis of Road Roller Based on Neural Network and Expert System

According to the complicated corresponding relationships between fault symptoms and fault reasons when road roller occurs fault,a new fault diagnosis method using neural network and expert system is presented.This method makes full use of characteristics of neural network and expert system.The paper introduces the composition,function and realization of the system.This system can improve the accuracy and the realtime character of the road roller fault diagnosis.
SkelSeg: Segmentation and Rigging of Raw-Scanned 3D Volume with User-Specified Skeleton

Although RGB-D camera-based scanning has become popular, a raw-scanned 3D model contains several problems that hinder animation such as fused arms and legs. We propose a system that allows a user to generate a rigged 3D mesh from a raw-scanned 3D volume with simple annotations. The user annotates the skeleton structure on the calibrated images captured at the scanning step, and our system automatically segments the raw-scanned volume into parts, generating a skinned 3D mesh based on the user-specified 3D skeleton. We tested our method with several raw-scanned 3D plush toy models, and successfully generated plausible animations.
Features in Deep Learning Architectures with Unsupervised Kernel k-Means

Deep learning technology and related algorithms have dramatically broken landmark records for a broad range of learning problems in vision, speech, audio, and text processing. Meanwhile, kernel methods have found common-place usage due to their nonlinear expressive power and elegant optimization formulation. Based on recent progress in learning high-level, class-specific features in unlabeled data, we improve upon the result by combining nonlinear kernels and multi-layer (deep) architecture, which we apply at scale. In particular, our experimentation is based on k-means with an RBF kernel, though it is a straightforward extension to other unsupervised clustering techniques and other reproducing kernel Hilbert spaces. With the proposed method, we discover features distilled from unorganized images. We augment high-level feature invariance by pooling techniques.
Expedition 315 participants Expedition 315 scientists

During Integrated Ocean Drilling Program Expedition 315, coring at two planned riser drilling sites was conducted. For Nankai Trough Seismogenic Zone Experiment (NanTroSEIZE) Stage 2, 3.5 km riser drilling is planned at Site C0001. We cored at this site to 458 m core depth below seafloor (CSF; IODP Method A: core expansion lengths overlap [are not scaled]) and cut 59 cores (31 with the hydraulic piston coring system [HPCS], 2 with the extended shoe coring system [ESCS], and 26 with the rotary core barrel [RCB]) from five holes covering a slope basin (Unit I) and the top 250 m of the underlying accretionary prism (Unit II). The slope basin is composed mainly of Quaternary to late Pliocene silty clay and clayey silt with intercalations of volcanic ash. The boundary between Units I and II, identified at 207.17 m CSF, is an unconformity characterized by a thick sand layer. Unit II is composed of more consolidated muddominated sediments of late Pliocene to late Miocene age. Structural style and stress state vary widely across a highly deformed zone at 220 m CSF. A normal fault indicating northeast–southeast extension is dominant above this zone; however, a few thrust faults dipping at 50° were encountered just above the deformed zone. These thrust faults are consistent with the northwest–southeast shortening subparallel to the direction of plate convergence. On the other hand, many thrust and strike-slip faults as well as a normal fault are found below the 220 m CSF deformed zone. The geometry and kinematics of planar structures display great variation. Fault plane solutions computed from normal and thrust faults are consistent with northeast–southwest extension and northwest–southeast shortening, respectively. A total of 48 whole-round samples were taken for interstitial geochemistry. Obtained data show meaningful trends for most elements, and potential contamination of drilling fluid is taken into consideration; however, changing trends do not necessarily correspond to unit boundaries. Methane and ethane concentrations and their ratio (C1/C2) decrease with depth to 100 m CSF and remain constant to the base of Unit I. The increase of methane concentrations and C1/C2 ratios in Unit II indicate the contribution of biogenic methane. Total organic carbon and calcium carbonate decrease monotonously to the base of Unit I and remain low throughout Unit II. Physical properties also show a clear break at the boundary between Units I and II. Porosity decreases downhole within each unit; however, there is a gap across the unit boundary. Thermal conductivity is almost constant throughout Unit I and decreases with depth in Unit II. Downhole temperature was measured with the advanced piston corer temperature tool (APCT3) at seven depths to 170.98 m CSF and yielded a generally linear downhole temperature increase, with a gradient of 0.042°C/m.