Miracle-dz/UniFAR-FTUs huggingface dataset:
RADIATION BIOLOGY AND ITS ROLE IN THE CANADIAN RADIATION PROTECTION FRAMEWORK.

The linear no-threshold (linear-non-threshold) model is a dose-response model that has long served as the foundation of the international radiation protection framework, which includes the Canadian regulatory framework. Its purpose is to inform the choice of appropriate dose limits and subsequent as low as reasonably achievable requirements, social and economic factors taken into account. The linear no-threshold model assumes that the risk of developing cancer increases proportionately with increasing radiation dose. The linear no-threshold model has historically been applied by extrapolating the risk of cancer at high doses (>1,000 mSv) down to low doses in a linear manner. As the health effects of radiation exposure at low doses remain ambiguous, reducing uncertainties found in cancer risk dose-response models can be achieved through in vitro and animal-based studies. The purpose of this critical review is to analyze whether the linear no-threshold model is still applicable for use by modern nuclear regulators for radiation protection purposes, or if there is sufficient scientific evidence supporting an alternate model from which to derive regulatory dose limits.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the Journal.
Rejoinder

Professor Vining’s discussion was mostly concerned with general experimental strategy anddealt withmany issues common to all applications of designed experiments. He starts with an assertion, “The ultimate basis for using DSDs is that they are ‘optimal”’ (p. 115). This assertion is incorrect. The Jones and Nachtsheim (2011) article that introduced definitive screening designs (DSDs) provides a table of the D-efficiency of DSDs for the main effects model. For 6 to 12 factors, these efficiencies range from 85.5 to 90.9 percent. In this work, my only reference to optimality is to point out that the standard two-level fractional factorial designs are optimal for the main effects model. I make no claim that DSDs are D-optimal for anymodel. The Vining discussion devotes substantial space to the separate viewpoints of Box and Kiefer with a strong preference for Box. However, this material is irrelevant to DSDs because they are not justified on the basis of optimality criteria. On the contrary, DSDs represent a design compromise that gives up some efficiency for fitting the main effects model in exchange for many other desirable properties. These properties are related to the fact that in the early phases of experimentation we do not know what factors are important and we also do not know what second-order effects will prove to be important. The compromise involved in DSDs is that they provide some information about all of the second-order effects (including pure quadratic effects) in exchange for a small increase in the standard errors for the main effects relative to two-level fractional factorial designs with the same number of runs.
Upregulation of Hippocampal Extracellular Signal-Regulated Kinase (ERK)–2 Induces Antidepressant-Like Behavior in the Rat Forced Swim Test

The hippocampus mediates responses to affect-related behavior in preclinical models of pharmacological antidepressant efficacy, such as the forced swim test. However, the molecular mechanisms that regulate escape-directed behavior in this preclinical model of despair are not well understood. Here, using viral-mediated gene transfer, we assessed how overexpression of extracellular signal-regulated protein kinase (ERK)–2 within the dorsal hippocampus influenced behavioral reactivity to inescapable swimming stress in adult male Sprague–Dawley rats. When compared to controls, rats overexpressing hippocampal ERK-2 displayed increases in the time to initially adopt a posture of immobility, along with decreases in total time spent immobile, without influencing general locomotor activity. Collectively, the results indicate that hippocampal upregulation of ERK-2 increases escape-directed behavior in the rat forced swim test, thus providing insight into the neurobiological mechanisms that mediate antidepressant efficacy.
Effective Demand: Securing the Foundations

Abstract This paper is one of three contributions to a symposium commenting on papers previously published by the other authors. Allain (Allain, O. (2009) Effective demand and short-term adjustments in the General Theory, Review of Political Economy, 21, pp. 1–22) argues that Keynes elides a distinction between aggregate demand and global expenditure that is necessary to explain the formation of price expectations by individual entrepreneurs. Allain's conclusions depend upon redefinitions of aggregate and effective demand and the consumption function. Hartwig (Hartwig, J. (2007) Keynes vs. the Post Keynesians on the principle of effective demand, European Journal of the History of Economic Thought, 14, pp. 725–739) argues that entrepreneurs must take into account the state of the economy as a whole, in order to form price expectations independently and not as a market equilibrium determined by aggregate supply and demand. This leaves demand price expectations to be determined outside the principle of effective demand. Neither author does full justice to Keynes's own treatment. We still need to agree by what mechanism individual entrepreneurs form a collective and mutually consistent state of expectation in The General Theory.
Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines.
Selective inversion recovery quantitative magnetization transfer imaging: Toward a 3 T clinical application in multiple sclerosis

Background: Assessing the degree of myelin injury in patients with multiple sclerosis (MS) is challenging due to the lack of magnetic resonance imaging (MRI) methods specific to myelin quantity. By measuring distinct tissue parameters from a two-pool model of the magnetization transfer (MT) effect, quantitative magnetization transfer (qMT) may yield these indices. However, due to long scan times, qMT has not been translated clinically. Objectives: We aim to assess the clinical feasibility of a recently optimized selective inversion recovery (SIR) qMT and to test the hypothesis that SIR-qMT-derived metrics are informative of radiological and clinical disease-related changes in MS. Methods: A total of 18 MS patients and 9 age- and sex-matched healthy controls (HCs) underwent a 3.0 Tesla (3 T) brain MRI, including clinical scans and an optimized SIR-qMT protocol. Four subjects were re-scanned at a 2-week interval to determine inter-scan variability. Results: SIR-qMT measures differed between lesional and non-lesional tissue (p < 0.0001) and between normal-appearing white matter (NAWM) of patients with more advanced disability and normal white matter (WM) of HCs (p < 0.05). SIR-qMT measures were associated with lesion volumes, disease duration, and disability scores (p ⩽ 0.002). Conclusion: SIR-qMT at 3 T is clinically feasible and predicts both radiological and clinical disease severity in MS.
An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor

A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small.
Stochastic volatility as a simple generator of apparent financial power laws and long memory

There has been renewed interest in power laws and various types of self-similarity in many financial time series. Most of these tests are visual in nature, and do not consider a wide range of possible candidate stochastic models capable of generating the observed results in small samples. This paper presents a relatively simple stochastic volatility model, which is able to produce visual power laws and long memory similar to those from actual return series using comparable sample sizes. These are small-sample features for the stochastic volatility model, since asymptotically it possesses none of these properties. The primary mechanism for this result is that volatility is assumed to have a driving process with a half life that is long relative to the tested aggregation ranges. It is argued that this might be a reasonable feature for financial, and other macroeconomic time series.
Blind Image Watermarking Based on Adaptive Data Spreading in n-Level DWT Subbands

This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction, invisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques: finding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the location, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowest-frequency subbands, resulting from n-level Mallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking data and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark data into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the strength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various resolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks. With experimental results and comparison to the existing works we show that the proposed scheme has better performance than the previous works, except those which specialize in certain types of attacks.
Book Reviews

Books reviewed: Joseph E. Stiglitz and Shahid Yusuf (eds) Rethinking the East Asian Miracle Hal Hill and Jo-o M. Saldanha, East Timor: Development Challenges for the World's Newest Nation Nat J. Colletta, Teck Ghee Lim and Anita Kelles-Viitanen (eds), Social Cohesion and Conflict Prevention in Asia S. Sugiyama and Linda Grove (eds), Commercial Networks in Modern Asia Marcel Timmer, The Dynamics of Asian Manufacturing: A Comparative Perspective in the Late Twentieth Century Jagdish Bhagwati, The Wind of the Hundred Days: How Washington Mismanaged Globalization Asian Development Bank, The Role of Central Banks in Microfinance in Asia and the Pacific, Volumes 1 and 2 Prema-Chandra Athukorala and Chris Manning, Structural Change and International Migration in East Asia Prema-Chandra Athukorala, Crisis and Recovery in Malaysia: The Role of Capital Controls Ruth McVey (ed.), Money and Power in Provincial Thailand David Gruen and John Simon (eds), Future Directions for Monetary Policy in East Asia Geoffrey B. Hainsworth (ed.), Globalization and the Asian Economic Crisis: Indigenous Responses, Coping Strategies and Governance Reform in Southeast Asia Brij V. Lal (ed.), Fiji Before the Storm: Elections and the Politics of Development Pradeep Agarwal, Subir V. Gokarn, Veena Mishra, Kirit S. Parikh and Kunal Sen, Policy Regimes and Industrial Competitiveness Iyanatul Islam and Anis Chowdhury, The Political Economy of East Asia: Post-crisis Debates
Sexual function and urinary incontinence complaints and other urinary tract symptoms of perimenopausal Turkish women

ABSTRACT Urinary incontinence (UI) and other lower urinary tract symptoms (LUTS) which are quite common among women, have a significant level of impact on women’s sexual function. Improving sexual function improves the quality of life. The purpose of this study is to evaluate the relationship between UI complaints with comorbid LUTS and sexual functions in Turkish women. The study is cross-sectional and descriptive. A total of 436 women was included in the study. Data were collected through Personal Information Form, The Bristol Female Lower Urinary Tract Symptom Questionnaire (BFLUTS) and The Pelvic Organ Prolapse/Urinary Incontinence Sexual Questionnaire (PISQ-12). The mean total of BFLUTS score was 31.99 ± 11.46, while the mean total of PISQ-12 score was 28.72 ± 6.92 in women. The most common symptoms were identified as storage and incontinence. There was a negative significant correlation between the total PISQ-12 scores and sub-dimension of BFLUTS scores (p < 0.01). Results of the study suggest that sexual function is negatively affected as the severity of symptoms increases.
Multidevice False Data Injection Attack Models of ADS-B Multilateration Systems

Location verification is a promising approach among various ADS-B security mechanisms, which can monitor announced positions in ADS-B messages with estimated positions. Based on common assumption that the attacker is equipped with only a single device, this mechanism can estimate the position state through analysis of time measurements of messages using multilateration algorithm. In this paper, we propose the formal model of multidevice false data injection attacks in the ATC system against the location verification. Assuming that attackers equipped with multiple devices can manipulate the ADS-B messages in distributed receivers without any mutual interference, such attacker can efficiently construct attack vectors to change the results of multilateration. The feasibility of a multidevice false data injection attack is demonstrated experimentally. Compared with previous multidevice attacks, the multidevice false data injection attacks can offer lower cost and more covert attacks. The simulation results show that the proposed attack can reduce the attackers’ cost by half and achieve better time synchronization to bypass the existing anomaly detection. Finally, we discuss the real-world constraints that limit their effectiveness and the countermeasures of these attacks.
Impact of Soil Moisture Data Characteristics on the Sensitivity to Crop Yields Under Drought and Excess Moisture Conditions

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.
Learning from Adversarial Features for Few-Shot Classification

Many recent few-shot learning methods concentrate on designing novel model architectures. In this paper, we instead show that with a simple backbone convolutional network we can even surpass state-of-the-art classification accuracy. The essential part that contributes to this superior performance is an adversarial feature learning strategy that improves the generalization capability of our model. In this work, adversarial features are those features that can cause the classifier uncertain about its prediction. In order to generate adversarial features, we firstly locate adversarial regions based on the derivative of the entropy with respect to an averaging mask. Then we use the adversarial region attention to aggregate the feature maps to obtain the adversarial features. In this way, we can explore and exploit the entire spatial area of the feature maps to mine more diverse discriminative knowledge. We perform extensive model evaluations and analyses on miniImageNet and tieredImageNet datasets demonstrating the effectiveness of the proposed method.
A uniformly distributed random portfolio

In this study, we propose a uniformly distributed random portfolio as an alternative benchmark for portfolio performance evaluation. The uniformly distributed random portfolio is analogous to an enumeration of all feasible portfolios without any prior on the market. Therefore, the relative ranking of a portfolio can be evaluated without peer group information. We derive a closed-form expression for the probability distribution of the Sharpe ratio of a uniformly distributed random portfolio, and conduct comparative analysis with US equity mutual funds. We find that the uniformly distributed random portfolio properly captures the historical performance distribution of equity mutual funds. In addition, we evaluate performance of cap-weighted equity portfolios via uniformly distributed random portfolios.
Rejoinder

All the discussants raised points concerning the use of prior information or the choice of the prior distribution. As Vining clearly stated, no experiment takes place in a vacuum and the use of informative prior distributions within the Bayesian paradigm formalises the many decisions required in the planning stage of the experiment. We agree that there will typically by more prior information available during the “confirmation” stage than the “discovery” stage. We think our description of the helicopter experiment fits into the confirmation stage, as does the pharmacokinetic example mentioned by Vining. This latter example can be found in Overstall and Woods (2016). However, that does not mean decisions made when planning experiments during the discovery stage should not also make use of available information. These decisions include the choice of model structure, explicitly mentioned by Vining. As alluded to by Harari and Bingham, common fractional factorial designs make strong, deterministic, assumptions about the class of model structures under consideration. A poor or inappropriate choice here can result in a use of a design that limits the number of different models that can subsequently be considered at the analysis stage. Use of, for example, a Bayesian model averaging approach, also discussed by Harari and Bingham and applied in Overstall and Woods (2016), can provide designs that are robust to the model uncertainty present at the planning stage.