Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of the Aura Microwave Limb Sounder middle atmosphere water vapor and nitrous oxide measurements

[1] The quality of the version 2.2 (v2.2) middle atmosphere water vapor and nitrous oxide measurements from the Microwave Limb Sounder (MLS) on the Earth Observing System (EOS) Aura satellite is assessed. The impacts of the various sources of systematic error are estimated by a comprehensive set of retrieval simulations. Comparisons with correlative data sets from ground-based, balloon and satellite platforms operating in the UV/visible, infrared and microwave regions of the spectrum are performed. Precision estimates are also validated, and recommendations are given on the data usage. The v2.2 H2O data have been improved over v1.5 by providing higher vertical resolution in the lower stratosphere and better precision above the stratopause. The single-profile precision is � 0.2–0.3 ppmv (4–9%), and the vertical resolution is � 3–4 km in the stratosphere. The precision and vertical resolution become worse with increasing height above the stratopause. Over the pressure range 0.1–0.01 hPa the precision degrades from 0.4 to 1.1 ppmv (6–34%), and the vertical resolution degrades to � 12–16 km. The accuracy is estimated to be 0.2–0.5 ppmv (4–11%) for the pressure range 68–0.01 hPa. The scientifically useful range of the H2O data is from 316 to 0.002 hPa, although only the 82–0.002 hPa pressure range is validated here. Substantial improvement has been achieved in the v2.2 N2O data over v1.5 by reducing a significant low bias in the stratosphere and eliminating unrealistically high biased mixing ratios in the polar regions. The single-profile precision is � 13–25 ppbv (7–38%), the vertical resolution is � 4–6 km and the accuracy is estimated to be 3–70 ppbv (9–25%) for the pressure range 100–4.6 hPa. The scientifically useful range of the N2O data is from 100 to 1 hPa.
Mechanical Engineering for Society and Industry

This article reports on technological mastery assistance in three small metal forming industries in Indonesia. Problems in the blangking and piercing separately process caused increased production time which resulted in inefficiency cost. Therefore, the expert team aided in metal forming technology through participatory action research (PAR) methods and experimental methods through reverse engineering for several products. The PAR method involves optimal contribution and participation from the industry. Assistance in mastering technology in small metal-forming industries reduces the manufacturing process from seven to three stages, increasing efficiency. The press machine's tonnage capacity must balance with the force blanking/piercing requirement. The minimum press machine requirement is 6.7 tons, and based on the availability of existing press machines, the expert team recommends a 20-ton capacity press machine. Total efficiency can be further increased by implementing full progressive die technology by combining piercing, blanking, and bending processes.
Neural Dynamics of Executive Function in Cognitively-able Kindergarteners with Autism Spectrum Disorders (ASD) as Predictors of Concurrent Academic Achievement: Preliminary Study

Although electrophysiological (EEG) measures of executive functions (EF) (e.g. error monitoring) and their link to academic achievement in typically developing children have been suggested, work investigating the link between error monitoring and academic skills in children with autism spectrum disorder (ASD) is limited. We employed traditional electrophysiological and advanced time-frequency methods, combined with principal components analyses, to extract neural activity related to error monitoring, and tested their relations to academic achievement in 35 cognitively-able kindergarteners with ASD. A child-friendly Go/No-go “Zoo Game” successfully elicited an error-related negativity (ERN) and error positivity (Pe) in children with ASD as young as 5 years. We also observed increased response-related theta power during error relative to correct trials. Both larger Pe and theta power were associated with academic achievement. However, only the effects of theta power on reading and math skills remained significant after controlling for behavioral performance on the Zoo Game and IQ. Results suggest that the use of time frequency EEG analyses may provide new opportunities to investigate neurobiological mechanisms of EF in young children with ASD. Results also provide further insights for the development of more targeted treatment programs designed to improve EF, which could have cascading effects on academic development.
Cognitive dysfunction in bipolar disorder and schizophrenia: a systematic review of meta-analyses

: Cognitive impairment is a core feature of schizophrenia (SZ) and bipolar disorder (BD). A neurocognitive profile characterized by widespread cognitive deficits across multiple domains in the context of substantial intellectual impairment, which appears to antedate illness onset, is a replicated finding in SZ. There is no specific neuropsychological signature that can facilitate the diagnostic differentiation of SZ and BD, notwithstanding, neuropsychological deficits appear more severe in SZ. The literature in this field has provided contradictory results due to methodological differences across studies. Meta-analytic techniques may offer an opportunity to synthesize findings and to control for potential sources of heterogeneity. Here, we performed a systematic review of meta-analyses of neuropsychological findings in SZ and BD. While there is no conclusive evidence for progressive cognitive deterioration in either SZ or BD, some findings point to more severe cognitive deficits in patients with early illness onset across both disorders. A compromised pattern of cognitive functioning in individuals at familiar and/or clinical risk to psychosis as well as in first-degree relatives of BD patients suggests that early neurodevelopmental factors may play a role in the emergence of cognitive deficits in both disorders. Premorbid intellectual impairment in SZ and at least in a subgroup of patients with BD may be related to a shared genetically determined influence on neurodevelopment.
Eﬀects of charge doping on Mott insulator with strong spin-orbit coupling, Ba 2 Na 1 − x Ca x OsO 6

The eﬀects of doping on the electronic evolution of the Mott insulating state have been extensively studied in eﬀorts to understand mechanisms of emergent quantum phases of materials. Here, we present a comprehensive study of the eﬀect of charge doping in the double perovskite Ba 2 NaOsO 6 , a Mott insulator with both strong electron correlations and SOC, utilizing muon spin relaxation ( µ SR), magnetization, and nuclear magnetic resonance (NMR) measurements. We establish that the insulating magnetic ground state evolves from canted antiferromagnet (AF) [1] to N´eel order for dopant levels exceeding ≈ 10% , indicating the enhancement of electron-electron correlations with doping. Furthermore, we determine that a broken local point symmetry (BLPS) phase, precursor to the magnetically ordered state [1], occupies an extended portion of the ( H − T ) phase diagram with increased doping. This ﬁnding reveals that the breaking of the local cubic symmetry is driven by a multipolar order, most-likely of the antiferro-quadrupolar type [2, 3].
Accuracy Comparison among LSTM based RNNs on TIMIT ID Layer

Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations. The recent pruning based work ESE (Han et al., 2017) suffers from degradation of performance/energy efficiency due to the irregular network structure after pruning. We propose block-circulant matrices for weight matrix representation in RNNs, thereby achieving simultaneous model compression and acceleration. We aim to implement RNNs in FPGA with highest performance and energy efficiency, with certain accuracy requirement (negligible accuracy degradation). Experimental results on actual FPGA deployments shows that the proposed framework achieves a maximum energy efficiency improvement of 35.7× compared with ESE.
Towards Evology: a Market Ecology Agent-Based Model of US Equity Mutual Funds II

Agent-based models (ABMs) are fit to model heterogeneous, interacting systems like financial markets. We present the latest advances in Evology: a heterogeneous, empirically calibrated market ecology agent-based model of the US stock market. Prices emerge endogenously from the interactions of market participants with diverse investment behaviours and their reactions to fundamentals. This approach allows testing trading strategies while accounting for the interactions of this strategy with other market participants and conditions. Those early results encourage a closer association between ABMs and ML algorithms for testing and optimising investment strategies using machine learning algorithms.
Squeeze-and-Excitation Networks

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the “Squeeze-and-Excitation” (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251 percent, surpassing the winning entry of 2016 by a relative improvement of <inline-formula><tex-math notation="LaTeX">${\sim }$</tex-math><alternatives><mml:math><mml:mo>∼</mml:mo></mml:math><inline-graphic xlink:href="shen-ieq1-2913372.gif"/></alternatives></inline-formula>25 percent. Models and code are available at <uri>https://github.com/hujie-frank/SENet</uri>.
"One man's meat is another man's poison." An experimental study of voluntarily providing public projects that raise mixed feelings

We compare, on the basis of a procedurally fair "provision point" mechanism, bids for a public project from which some gain and some lose with bids for a less efficient public project from which all gain. In the main treatment, participants independently decide which one, if any, of the public projects should be implemented. We also run control treatments where only one of the two projects can be implemented. We find that (a) mixed feelings per se do not affect bidding behavior, and (b) the provision frequency of the project that raises mixed feelings declines significantly when it faces competition from the public good.
Trajectory Tracking for Underwater Swimming Manipulators using a Super Twisting Algorithm

The underwater swimming manipulator (USM) is a snake‐like, multi‐articulated, underwater robot that is equipped with thrusters. One of the main purposes of the USM is to act like an underwater floating base manipulator. As such, it is essential to achieve good station‐keeping and trajectory tracking performance for the USM by using the thrusters and by using the joints to attain the desired position and orientation of the head and tail of the USM. In this ‘paper, we propose a sliding mode control (SMC) law, specifically the super‐twisting algorithm with adaptive gains, for the trajectory tracking of the USM's centre of mass. A higher‐order sliding mode observer is proposed for state estimation. Furthermore, we show the ultimate boundedness of the tracking errors. We demonstrate the applicability of the proposed control law and show that it leads to better performance than a linear PD‐controller.
Estimates of spatial and inter-channel observation error characteristics for current sounder radiances for NWP

This paper uses three methods to estimate and examine observation errors and their correlations for clearsky sounder radiances used in the ECMWF assimilation system. The study considers sounder-radiances from the main instruments currently in use, ie. , AMSU-A, HIRS, MHS, AIRS, and IASI. The analysis is based on covariances derived from pairs of First Guess and analysis departures. The methods used are the so-called Hollingsworth/Lonnberg method, a method based on subtracting a scaled version of mapped assumed background errors from FG-departure covariances, and the Desroziers diagnostic. The findings suggest that mid-tropospheric to stratospheric temperature sounding channels for AIRS and IASI and all AMSU-A sounding channels show little or no inter-channel or spatial observation error correlations, and estimates for the observation error are close to the instrument noise. Channels with stronger sensitivity to the surface show larger observation errors compared to the instrument noise, and some of this error is correlated spatially and between channels. Short-wave infrared temperature sounding channels also appear more prone to spatial observation error correlations. The three methods show good consistency for these estimates. Estimating observation errors for humidity sounding channels appears more difficult. A considerable proportion of the observation error for humidity sounding channels appears correlated spatially for short separation distances, as well as between channels. Observation error estimates for humidity channels are generally considerably larger than those provided by the instrument noise. Our statistics suggest that assumed background errors for tropospheric temperature are inflated (by about 30-60%), whereas there is little indication for background error inflation for stratospheric temperatures.
E DUCATION P OLICY A NALYSIS A RCHIVES

This report examines effects of structural and process features of professional development programs on teachers’ knowledge, practice and efficacy. It is based on four recent (2002-2003) studies undertaken through the Australian Government Quality Teacher Programme, designed to enhance teacher quality. The total data set for the survey study includes 3,250 teachers who had participated in eighty individual professional development 1 activities within these studies. Teachers were surveyed at least three months after participating in an activity
DEEP NEURAL NETWORKS

With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency. In this work, we explore unconventional narrow-precision floating-point representations as it relates to inference accuracy and efficiency to steer the improved design of future DNN platforms. We show that inference using these custom numeric representations on production-grade DNNs, including GoogLeNet and VGG, achieves an average speedup of 7.6× with less than 1% degradation in inference accuracy relative to a state-of-the-art baseline platform representing the most sophisticated hardware using single-precision floating point. To facilitate the use of such customized precision, we also present a novel technique that drastically reduces the time required to derive the optimal precision configuration.
Capital accumulation, inflation and long-run conflict in international objectives

It is shown that, when there is a genuine long-run trade-off between inflation and output, coordination under pre-commitment yields substantial improvements in economic welfare. The analysis is conducted within the context of a two-country model with capital accumulation, immobile labour, perfect capital mobility and floating exchange rates. If the home government increases its monetary growth rate, it increases home inflation, reduces the world real interest rate and therefore boosts both home and foreign capital accumulation. The foreign country thus enjoys a gain in output without suffering from higher inflation. Competitive policies lead to monetary policies which are too tight and levels of activity which are too low, since each country attempts to be a "free rider". Coordination leads to a lower world real interest rate and higher welfare. Pre-commitment is necessary, for the success of coordinated policies, however. Otherwise each government has an incentive to renege and levy a "surprise" inflation tax. In the absence of binding contracts or reputation effects, both cooperation and competitive policy formulation lead to excessive monetary growth rates and higher levels of activity than under coordination or competition with pre-commitment. Coordination can be futile, since it exacerbates the lack of credibility perceived by the private sectors.
THE EFFECTS OF CYCLING SHOE OUTSOLE MATERIAL ON PLANTAR STRESS

INTRODUCTION Published reports have mentioned localized ischemia, paraesthesia, and metatarsalgia as being common problems among cyclists [12,17]. Elevated local pressure during cycling has been specifically linked to metararsalgia [5], and is likely a contributing factor in other foot problems. Several studies have examined in-shoe plantar pressure [17,20]. Several aspects of footwear related to comfort and to biomechanical abnormalities have been discussed [6,9,11,20] and typical pressure profiles during cycling [17,19] and running are also documented. Ultra-stiff carbon fiber composites are now being placed in the midsole and/or outsole of both road and mountain bike racing shoes. According to popular cycling magazines and cycling-related advertisements, stiffer cycling shoes made from composite materials are designed to transfer energy more efficiently from the legs and feet to the pedals, although this is not well documented. The current study attempted to determine whether: (a) carbon fiber composite cycling shoes result in higher forefoot plantar pressure than traditional plastic-soled cycling shoes; and (b) carbon fiber shoes are quantifiably stiffer than more traditional plastic-soled shoes. Plantar stress data were collected with in-shoe plantar pressure measurement insoles and material testing techniques were used to quantify shoe stiffness. MATERIALS AND METHODS Subjects Ten healthy male subjects were recruited to participate in plantar pressure data collection. Subjects had mean age, height, and weight of 28.3 ±5 years, 1.75 ± 0.04 meters, and 747 ± 85 Newtons, respectively. No subjects were competitive cyclists, but several had recreational cycling experience (1.3 ± 1.2 years). All subjects signed a human subject informed consent form reviewed by the University of Louisville Human Subjects Committees prior to participation. Shoes Two pairs of shoes were tested in this study: Shimano model SHM152 and SH-M220 (Shimano Corporation, Irvine, CA). The M152
Structured Set Matching Networks for One-Shot Part Labeling

Diagrams often depict complex phenomena and serve as a good test bed for visual and textual reasoning. However, understanding diagrams using natural image understanding approaches requires large training datasets of diagrams, which are very hard to obtain. Instead, this can be addressed as a matching problem either between labeled diagrams, images or both. This problem is very challenging since the absence of significant color and texture renders local cues ambiguous and requires global reasoning. We consider the problem of one-shot part labeling: labeling multiple parts of an object in a target image given only a single source image of that category. For this set-to-set matching problem, we introduce the Structured Set Matching Network (SSMN), a structured prediction model that incorporates convolutional neural networks. The SSMN is trained using global normalization to maximize local match scores between corresponding elements and a global consistency score among all matched elements, while also enforcing a matching constraint between the two sets. The SSMN significantly outperforms several strong baselines on three label transfer scenarios: diagram-to-diagram, evaluated on a new diagram dataset of over 200 categories; image-to-image, evaluated on a dataset built on top of the Pascal Part Dataset; and image-to-diagram, evaluated on transferring labels across these datasets.
MapLUR

Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this article, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. To illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep-learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled NO2 concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.
The Lottery Ticket Hypothesis : Finding Small , Trainable Neural Networks

Neural network compression techniques are able to reduce the parameter counts of trained networks by over 90%—decreasing storage requirements and improving inference performance—without compromising accuracy. However, contemporary experience is that it is difficult to train small architectures from scratch, which would similarly improve training performance. We articulate a new conjecture to explain why it is easier to train large networks: the lottery ticket hypothesis. It states that large networks that train successfully contain subnetworks that—when trained in isolation—converge in a comparable number of iterations to comparable accuracy. These subnetworks, which we term winning tickets, have won the initialization lottery: their connections have initial weights that make training particularly effective. We find that a standard technique for pruning unnecessary network weights naturally uncovers a subnetwork which, at the start of training, comprised a winning ticket. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis. We consistently find winning tickets that are less than 20% of the size of several fully-connected, convolutional, and residual architectures for MNIST and CIFAR10. Furthermore, winning tickets at moderate levels of pruning (20-50% of the original network size) converge up to 6.7x faster than the original network and exhibit higher test accuracy.