U.S. Antidumping Policies: The Case of Steel

This paper examines the controversy surrounding recent allegations that foreign producers are dumping steel products onto U.S. markets. The paper is in four sections, which take four quite distinct views of dumping and recent U.S. antidumping policies, emphasizing the changing definition of dumping and the development of administrative procedures. Section II focuses on the application of these procedures to the international steel trade, taking as a case study the most noteworthy of recent innovations : the Trigger Price Mechanism for steel. Section III considers models that can be used to analyze dumping. The models of most relevance to the practices currently at issue in the steel industry seem to us models of oligopolistic rivalry in imperfectly competitive, segmented markets. We develop a model designed to identify crucial factors upon which the incidence of dumping will depend: the number of firms producing for each national market,their costs, their market shares, and the extent to which they recognizeand exploit their mutual dependence. Finally, in Section IV we calibrate these models to illustrate how the extent of dumping and the effects of the TPM depend on the model's parameters.

Auditing spreadsheets: With or without a tool?

Spreadsheets are known to be error-prone. Over the last decade, research has been done to determine the causes of the high rate of errors in spreadsheets. This paper examines the added value of a spreadsheet tool (PerfectXL) that visualizes spreadsheet dependencies and determines possible errors in spreadsheets by defining risk areas based on previous work. This paper will firstly discuss the most common mistakes in spreadsheets. Then we will summarize research on spreadsheet tools, focussing on the PerfectXL tool. To determine the perceptions of the usefulness of a spreadsheet tool in general and the PerfectXL tool in particular, we have shown the functionality of PerfectXL to several auditors and have also interviewed them. The results of these interviews indicate that spreadsheet tools support a more effective and efficient audit of spreadsheets; the visualization feature in particular is mentioned by the auditors as being highly supportive for their audit task, whereas the risk feature was deemed of lesser value.Comment: 15 Pages, 2 Tables, 8 Colour Figure

High-Frame-Rate Power Doppler Ultrasound Is More Sensitive than Conventional Power Doppler in Detecting Rheumatic Vascularisation

Early recognition of joint inflammation will increase treatment efficacy in rheumatoid arthritis (RA). Yet, conventional power Doppler (PD) ultrasound might not be sufficiently sensitive to detect minor inflammation. We investigated the sensitivity of high-frame rate Doppler, combined with singular value decomposition technique, to suppress tissue signals, for microvascular flow in a flow phantom setup and in a proof-of-principle study in healthy controls and in RA patients with different disease activities. In the flow phantom, minimal detectable flow velocity was a factor 3 lower with high-frame-rate PD than with conventional PD ultrasound. In the proof-of-principle study we detected a positive PD signal in all volunteers, diseased or healthy, with high-frame-rate PD ultrasound. We saw a gradual increase in PD signal in RA patients depending on disease activity. In conclusion, high-frame rate Doppler is more sensitive in detecting vascularisation than conventional PD ultrasound

Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes

The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tunable nanoelectronic devices were developed based on hopping electrons through a network of dopant atoms in silicon. These "Dopant Network Processing Units" (DNPUs) are highly energy-efficient and have potentially very high throughput. By adapting the control voltages applied to its terminals, a single DNPU can solve a variety of linearly non-separable classification problems. However, using a single device has limitations due to the implicit single-node architecture. This paper presents a promising novel approach to neural information processing by introducing DNPUs as high-capacity neurons and moving from a single to a multi-neuron framework. By implementing and testing a small multi-DNPU classifier in hardware, we show that feed-forward DNPU networks improve the performance of a single DNPU from 77% to 94% test accuracy on a binary classification task with concentric classes on a plane. Furthermore, motivated by the integration of DNPUs with memristor arrays, we study the potential of using DNPUs in combination with linear layers. We show by simulation that a single-layer MNIST classifier with only 10 DNPUs achieves over 96% test accuracy. Our results pave the road towards hardware neural-network emulators that offer atomic-scale information processing with low latency and energy consumption

The Association Between Exposure to COVID-19 and Mental Health Outcomes Among Healthcare Workers

Due to the unprecedented impact of the COVID-19 pandemic on health care systems, there has been great interest in the mental wellbeing of healthcare workers. While most studies investigated mental health outcomes among frontline vs. non-frontline healthcare workers, little is known about the impact of various work-related variables. The present study aimed to examine the association between work-related [i.e., having contact with COVID-19 patients, being redeployed due to the pandemic and availability of sufficient personal protective equipment (PPE)] and subjective (i.e., worries about getting infected or infecting others) exposures and self-reported mental health outcomes (i.e., psychological distress, depressive symptoms, and posttraumatic stress symptoms). Between February and May 2021, 994 healthcare workers employed at a variety of healthcare settings in the Netherlands filled out an online survey as part of the COVID-19 HEalth caRe wOrkErS (HEROES) study. Mental health outcomes were measured using the General Health Questionnaire-12, the Patient Health Questionnaire-9, and the Primary Care PTSD Screen for DSM-5. Approximately 13% reported depressive symptoms, 37% experienced psychological distress, and 20% reported posttraumatic stress symptoms. Multilevel linear models consisted of three levels: individual (work-related and subjective exposures), healthcare center (aggregated redeployment and availability of sufficient PPE), and regional (cumulative COVID-19 infection and death rates). Worries about infection were associated with all three mental health outcomes, whereas insufficient PPE was associated with psychological distress and depressive symptoms. There were no differences in outcomes between healthcare centers or provinces with different COVID-19 infection and death rates. Our findings highlight the importance of adequate PPE provision and the subjective experience of the COVID-19 pandemic. These factors should be part of interventions aimed at mitigating adverse mental health outcomes among healthcare workers during the COVID-19 pandemic

Gradient Descent in Materio

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning. One of its key enablers has been backpropagation, an algorithm that computes the gradient of a loss function with respect to the weights in the neural network model, in combination with its use in gradient descent. However, the implementation of deep learning in digital computers is intrinsically wasteful, with energy consumption becoming prohibitively high for many applications. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits and integrated photonic tensor cores to unconventional, material-based computing systems. The learning process in these material systems, taking place, e.g., by artificial evolution or surrogate neural network modelling, is still a complicated and time-consuming process. Here, we demonstrate an efficient and accurate homodyne gradient extraction method for performing gradient descent on the loss function directly in the material system. We demonstrate the method in our recently developed dopant network processing units, where we readily realize all Boolean gates. This shows that gradient descent can in principle be fully implemented in materio using simple electronics, opening up the way to autonomously learning material systems

Very different performance of the power Doppler modalities of several ultrasound machines ascertained by a microvessel flow phantom

Introduction: In many patients with rheumatoid arthritis (RA) subclinical disease activity can be detected with ultrasound (US), especially using power Doppler US (PDUS). However, PDUS may be highly dependent on the type of machine. This could create problems both in clinical trials and in daily clinical practice. To clarify how the PDUS signal differs between machines we created a microvessel flow phantom.Methods: The flow phantom contained three microvessels (150, 1000, 2000 microns). A syringe pump was used to generate flows. Five US machines were used. Settings were optimised to assess the lowest detectable flow for each US machine.Results: The minimal detectable flow velocities showed very large differences between the machines. Only two of the machines may be able to detect the very low flows in the capillaries of inflamed joints. There was no clear relation with price. One of the lower-end machines actually performed best in all three vessel sizes.Conclusions: We created a flow phantom to test the sensitivity of US machines to very low flows in small vessels. The sensitivity of the power Doppler modalities of 5 different machines was very different. The differences found between the machines are probably caused by fundamental differences in processing of the PD signal or internal settings inaccessible to users. Machines considered for PDUS assessment of RA patients should be tested using a flow phantom similar to ours. Within studies, only a single machine type should be used