Miracle-dz/UniFAR-FTUs huggingface dataset:
Three Levels Are Not Enough: Scaling Laws for Multilevel Converters in AC/DC Applications

Single-phase inverters and rectifiers in 230 V$_{\text{rms}}$ applications, with a dc-side voltage of 400 V, achieve ultrahigh efficiency with a simple two-level topology. These single-phase designs typically utilize a line-frequency unfolder stage, which has very low losses and essentially doubles the peak-to-peak voltage that can be generated on the ac side for a given dc-link voltage. For certain applications, however, such as higher power grid-connected photovoltaic inverters, electric vehicle chargers, and machine drives, three-phase converters are needed. Because of the three-phase characteristic of the system, unfolders cannot be similarly used, leading to a higher minimum dc-link voltage of the three-phase line-to-line voltage amplitude, which is typically set to 800 V for 230 V$_{\text{rms}}$ phase voltage systems. Previous demonstrations indicate that significantly more levels—and the associated higher cost and complexity—are required for ultrahigh-efficiency three-phase converters relative to their single-phase counterparts. In this article, we seek to determine the fundamental reason for the performance difference between three-phase 800 V dc-link converters and single-phase 400 V converters. First, we build a 2.2 kW dc/ac hardware demonstrator to confirm the necessity of higher complexity converters, showing a simultaneous reduction in efficiency and power density between a two-level 400 V benchmark (99.2% peak efficiency at 18.0 kW/L) and a three-level 800 V inverter phase-leg (98.8%, 9.1 kW/L). With the motivation confirmed, we derive general scaling laws for bridge-leg losses across the number of levels and dc-link voltage, finding the efficiency-optimal chip area and the minimum semiconductor losses. With commercially available Si or GaN power semiconductors, the scaling laws indicate that six or more levels would be required for an 800 V three-phase ac/dc converter to meet or exceed the bridge-leg efficiency of a two-level 400 V GaN benchmark for a fixed output filter. With a complete Pareto optimization, we find that at least seven levels are necessary to recover the efficiency of the two-level 400 V benchmark, and we validate this theory with a seven-level 800 V 2.2 kW hardware prototype with a power density of 15.8 kW/L and a peak efficiency of 99.03%. Finally, two practical solutions that make use of the benefits of unfolder bridges familiar in single-phase systems are identified for three-phase systems.
SD-MTCNN: Self-Distilled Multi-Task CNN

Multi-task learning (MTL) using convolutional neural networks (CNN) deals with training the network for multiple correlated tasks in concert. For accuracy-critical applications, there are endeavors to boost the model performance by resorting to a deeper network, which also increases the model complexity. However, such burdensome models are difﬁcult to be deployed on mobile or edge devices. To ensure a trade-off between performance and complexity of CNNs in the context of MTL, we introduce the novel paradigm of self-distillation within the network. Different from traditional knowledge distillation (KD), which trains the Student in accordance with a cumbersome Teacher, our self-distilled multi-task CNN model: SD-MTCNN aims at distilling knowledge from deeper CNN layers into the shallow layers. Precisely, we follow a hard-sharing based MTL setup where all the tasks share a generic feature-encoder on top of which separate task-speciﬁc decoders are enacted. Under this premise, SD-MTCNN distills the more abstract features from the decoders to the encoded feature space, which guarantees improved multi-task performance from different parts of the network. We validate SD-MTCNN on three benchmark datasets: CityScapes, NYUv2, and Mini-Taskonomy, and results conﬁrm the improved generalization capability of self-distilled multi-task CNNs in comparison to the literature and baselines.
Declared exchange rate and quality-driven pass-through to import prices

This paper argues that the product quality is an important channel of price adjustments to the exchange rate changes. To support this view, I elaborate a model of monopolistic competition with heterogenous quality and sizes of trading firms. I further empirically test the role of quality for the exchange rate pass-through to import prices. I employ a highly detailed dataset that incorporates the universe of declarations submitted to Russian customs authorities on a daily basis, over the 2011 – 2015 period. The Russian Ruble has been largely volatile and sharply depreciated in 2014 – 2015, which generated large variance to explore. I compute import quality as the residual of the demand and introduce it to the standard exchange rate pass-through regression. I find that the higher product quality is associated with softer exchange rate pass-through. I further isolate the role of the invoicing currency. I make use of the declared exchange rates and conclude that corresponding pass-through is significantly lower than for the official exchange rates. Finally, I differentiate the pass-through with respect to the importers’ characteristics, which I source from BvD Orbis. I conclude that when the importers are bigger, the pass-through is getting stronger. Meanwhile, the magnitude for the most productive importers is softer.
Thompson Sampling via Local Uncertainty: Appendix

In the implementation, we all use ReLU activation function, except for using the exponential link function for parameterizing the standard deviations of the Gaussian distributions. We set prior distribution p(zt) as a normal distribution with mean 0 and standard deviation σI, where σ is a point estimation with initial value σ = 1.25. Denote the latent dimension as H , the number of actions as A, and context dimension as C.
Ranking comments: An Entropy-based Method with Word Embedding Clustering

Automatically ranking comments by their relevance plays an important role in text mining and text summarization area. In this thesis, firstly, we introduce a new text digitalization method: the bag of word clusters model. Unlike the traditional bag of words model that treats each word as an independent item, we group semantic-related words as clusters using pre-trained word2vec word embeddings and represent each comment as a distribution of word clusters. This method can extract both semantic and statistical information from texts. Next, we propose an unsupervised ranking algorithm that identifies relevant comments by their distance to the “ideal” comment. The “ideal” comment is the maximum general entropy comment with respect to the global word cluster distribution. The intuition is that the “ideal” comment highlights aspects of a product that many other comments frequently mention. Therefore, it can be regarded as a standard to judge a comment’s relevance to this product. At last, we analyze our algorithm’s performance on a real Amazon product.
N-modulation signals in a single-mode optical waveguide under nonlinear conditions

The Darboux representations in combination with numerical methods are used to obtain Nmodulation solutions of the nonlinear Schrodinger equation describing the process of transformation of a continuous signal with a weak initial modulation into a periodic sequence of pulses. The optimal parameters for such a transformation are found, as well as the conditions under which the off-duty factor of the pulses is maximal. It is shown that in the optimal case the off-duty factor and the peak value of the power in a pulse are proportional to the square of the number N of the modulation harmonics in the input signal. Examples are given of numerical calculations of the profiles of the resultant pulses. A numerical analysis is made of the evolution of a spectrum of N-modulation signals. It is shown that a suitable selection of the parameters makes it possible to use such signals also for effective transformation of the spectra.
A martingale transformation for superprocesses ∗ March 31 , 2012

A Girsanov’s type theorem for superprocesses was first proved by Dawson (1978). A bivariate version was given by Evans and Perkins (1994); see also Theorem IV.1. 6 in Perkins (2002). Motivated by some recent works on Girsanov’s type theorems for continuous state branching processes and Lévy trees; see Abraham and Delmas (2010), in this work, we will prove a Girsanov’s type theorem for superprocesses with general branching mechanisms. The processes could be discontinuous and have non-local branchings. Suppose that E is a Lusin topological space and d is a metric for its topology so that the d-completion of E is compact. Let ξ = (Ω,G,Gt, ξt,Px) be a conservative Borel right process in E with transition semigroup (Pt)t≥0. We denote by B(E) the Banach space of bounded Borel functions on E endowed with the supremum/uniform norm “‖ · ‖”. Let C(E) be the subspace of B(E) consisting of continuous functions. We use the superscript “+” to denote the subset of positive elements of the function spaces, e.g., B(E) and C(E). For a measure μ and a Borel function f on E write μ(f) or 〈μ, f〉 for ∫ fdμ if the integral exists. Let M(E) denote the space of finite measures on E endowed with the topology of weak convergence.
Image Harmonization with Attention-based Deep Feature Modulation

We present a learning-based approach for image harmonization, which allows for adjusting the appearance of the foreground to make it compatible with background. We consider improving the realism by adjusting the high-level feature statistics of the foreground according to those of the background, which is motivated by the fact that spe-ciﬁc image statistics between the foreground and background typically match in realistic composite images. Based on a fully convolutional network, we propose a novel attention-based module that aligns the standard deviation of the foreground features with that of the background features, capturing global dependencies in the entire image. This module is easily inserted into any types of convolutional neural networks, and allows improving the harmony of the composites with only a small additional computational cost. Experimental results on the image harmonization dataset and real composite images show that our method outperforms existing methods both quantitatively and qualitatively. Furthermore, in our experiment, our module is able to boost existing harmonization networks by simply inserting it into intermediate layers of those networks.
Event-Triggered Control in Presence of Measurement Noise: A Space-Regularization Approach

In this paper, general conditions for set stabilization of (distributed) event-triggered control systems affected by measurement noises are presented. It is shown that, under these conditions, both static and dynamic triggers can be designed using a space-regularization approach such that the closed-loop system ensures an input-to-state practical stability property. Additionally, by proper choice of the tuning parameters, the system does not exhibit Zeno behavior. Contrary to various results in the literature, the noises do not have to be differentiable. The general results are applied to point stabilization and consensus problems as particular cases. Simulations illustrate our results.
What do CNNs gain by imitating the visual development of primate infants?

Deep convolutional neural networks have emerged as strong candidates for a model of human vision, often outperforming competing models on both computer vision benchmarks and computational neuroscience benchmarks of neural response correspondence. The design of these models has undergone several reﬁnements in recent years drawing on both statistical and cognitive insights and, in the process, shown increasing correspondence to primate visual processing representations. However, their training methodology still remains in contrast to the process of primate visual development, and we believe that it can beneﬁt from being more aligned with this natural process. Primate visual development is characterized by low visual acuity and colour sensitivity as well as high plasticity and neuronal growth in the ﬁrst year of infancy, prior to the development of speciﬁc visual-cognitive functions such as visual object recognition. In this work, we investigate the synergy between the gradual variation in the distribution of visual input and the concurrent growth of a statistical model of vision on the task of large-scale object classiﬁcation, and discuss how it may yield better approaches to training deep convolutional neural networks. The experiments we performed across multiple object classiﬁcation benchmarks indicate that a growing statistical model trained with a gradually varying visual input distribution converges to a better generalization at a faster rate than traditional, more static training setups.
Making Sense of Contemporary Strategic Implementation: Towards A Conceptual Model

Nowadays, demands on corporate strategists are increasingly heavy, as strategic implementation in becoming more complex in the real world. Therefore, there is a need to develop a conceptual model to integrate and make the theory of strategy implementation easier to understand and apply. Pettigrew’s conceptual model represents three implementation levels at which top managers are required to operate: the context, content, and process of strategy implementation. Strategic managers need to understand why strategic decisions have been made in relation to the organizational context, both internal and external; what information, rationales, mechanisms and impacts are included in the strategic decision made; and how the decisions are reached and implemented.
Agency Problem, Trade Liberalization and Aggregate Productivity: Theory and Evidence

Evidence shows that trade liberalization mitigates the agency problem inside the firm and improves firm productivity, which does not square well with the existing literature. I propose a general equilibrium model to explain this finding. When an economy opens up to trade, managers of unproductive surviving non-exporters are incentivized to exert more effort, which leads to a within-firm productivity improvement. Furthermore, this effect only applies to unproductive firms that are subject to the agency problem, which is a new channel through which trade liberalization improves firm productivity. Despite of this new channel, gains in aggregate productivity might be smaller in a world with the agency problem, since the improvement of inefficient firms retards resource reallocation toward efficient firms after trade liberalization. Using Colombia plant-level data, I show that the agency problem matters for gains in aggregate productivity and welfare gains from trade liberalization quantitatively. In addition, I provide evidence to support a key empirical prediction of the model.
TITLE: EFFECTS OF MYXOMA VIRUS AND RABBIT HEMORRHAGIC 1 DISEASE VIRUS ON THE PHYSIOLOGICAL CONDITION OF WILD EUROPEAN

21 22 Myxomatosis and rabbit hemorrhagic disease (RHD) are the major viral diseases that 23 affect the wild European rabbit (Oryctolagus cuniculus). These diseases arrived in 24 Europe within the last decades and have caused wild rabbit populations to decline 25 dramatically. Both viruses are currently considered to be endemic in the Iberian 26 Peninsula; periodic outbreaks that strongly impact wild populations regularly occur. 27 Myxoma virus (MV) and rabbit hemorrhagic disease virus (RHDV) alter the physiology 28 of infected rabbits, resulting in physical deterioration. Consequently, the persistence and 29 viability of natural populations are affected. The main goal of our study was to 30 determine if blood biochemistry is correlated with serostatus in wild European rabbits. 31 We carried out seven live-trapping sessions in three wild rabbit populations over a two32 year period. Blood samples were collected to measure anti-MV and anti-RHDV 33 antibody concentrations and to measure biochemical parameters related to organ 34 function, protein metabolism, and nutritional status. Overall, we found no significant 35 relationships between rabbit serostatus and biochemistry. Our main result was that 36 rabbits that were seropositive for both MV and RHDV had low gamma 37 glutamyltransferase concentrations. Given the robustness of our analyses, the lack of 38 significant relationships may indicate that the biochemical parameters measured are 39 poor proxies for serostatus. Another explanation is that wild rabbits might be producing 40 attenuated physiological responses to these viruses because the latter are now enzootic 41 in the study area. 42
Enhancement of nucleate boiling by combining the effects of surface structure and mixed wettability: A lattice Boltzmann study

The combination of microstructures and mixed wettability for enhancing nucleate boiling has attracted much attention in recent years. However, in the existing experimental and numerical studies, the tops of microstructures are entirely subjected to wettability modification, which makes the influences of mixed wettability dependant on the characteristic length of microstructures. In order to disclose the joint effects of surface structure and mixed wettability on nucleate boiling, in this work we propose an improved type of pillar-textured surface with mixed wettability, in which the tops of square pillars are partially subjected to wettability modification. Numerical investigation of the boiling heat transfer performance on the improved mixed-wettability surface is carried out using a three-dimensional thermal multiphase lattice Boltzmann model. The numerical results show that the width of the wettability-modified region plays an important role in the boiling performance of the improved mixed-wettability surface and the best boiling performance is achieved in the situation that the width of the wettability-modified region is sufficiently large but the bubble nucleated on the pillar top still does not interfere with the coalescence-departure mechanism of the bubbles nucleated around the pillar, which optimizes the joint effects of surface structure and mixed wettability for enhancing nucleate boiling. The influences of the shape of the wettability-modified region are also studied. Among the investigated shapes, the square is found to perform better than the other two shapes.
Typology of Human Values Making: Challenges and Dilemmas in Making Value-systems

“In The Name of Allah” In this paper, I have dealt with some key questions that normally arise when someone faces the task of making a value system. Discovery of these answers empowers us to do a professional job in the field of values education. Along with having a belief system, making a values system is the second important and crucial job for a Muslim who decides to practice Islam in his/her everyday life. What do we mean by the term ‘values’? How could we help our children to come up with a sound values system? Is there any link between values system and our world view? Shahid Allamah Muhammad Baqir Al-Sadr, a great Muslim thinker, approaches this issue from an Islamic perspective. He provides a triple classifying model for human values making. He consults some of the Quranic verses for his dialog to argue for three types of values making namely: contextuality and traditionalism, pathological generalization and transcendentalism. Allamah Al-Sadr also tries to explain why making a value system could happen both in two different forms, namely a normal or a pathological way. Human beings initially run in an avenue to arrive at a destiny through an inevitable journey. To make a conscious values system is indeed a way to make this journey conscious and willingly. Utilizing a qualitative and analytical research method, I have attempted to re-build and formulate AlSadr’s explanation of human values-making. In my review, I have targeted an exploration of Al-Sadr’s discoveries in this field with the hope to find a way through which we could enable our children to make a sound & healthy Islamic values system. Key terms: Values-making, ideals, metacognitions, contextuality and traditionalism, pathological generalization and transcendentalism.
Unsupervised Anomaly Detection in Time Series with Convolutional-VAE

We propose an unsupervised machine learning algorithm for anomaly detection that exploits self-learnt features of mono-dimensional time series. A Variational Autoencoder, where convolution takes place of dot product, is trained to compress each input to a low-dimensional point from a normal distribution, detecting an anomaly as low probability and high density sequence. We validate our work on di ﬀ erent public datasets, obtaining results that shed new light on Variational Autoencoders applied to anomaly detection
SketchHealer: A Graph-to-Sequence Network for Recreating Partial Human Sketches

To perceive and create a whole from parts is a prime trait of the human visual system. In this paper, we teach machines to perform a similar task by recreating a vectorised human sketch from its incomplete parts. This is fundamentally different to prior work on image completion (i) sketches exhibit a severe lack of visual cue and are of a sequential nature, and more importantly (ii) we ask for an agent that does not just ﬁll in a missing part, but to recreate a novel sketch that closely resembles the partial input from scratch . Central to our contribution is a graph model that encodes both the visual and structural features over multiple categories . A novel sketch graph construction module is proposed that leverages the sequential nature of sketches to associate key parts centred around stroke junctions. The intuition is then that message passing within the said graph will naturally provide the healing power when it comes to missing parts (nodes). Finally, an off-the-shelf LSTM-based decoder is employed to decode sketches in a vectorised fashion. Both qualitative and quantitative results show that the proposed model signiﬁcantly outperforms state-of-the-art alternatives.
2 0 Ju n 20 14 Prospects For High Frequency Burst Searches Following Binary Neutron Star Coalescence With Advanced Gravitational Wave Detectors

The equation of state plays a critical role in the physics of the merger of two neutron stars. Recent numerical simulations with microphysical equation of state suggest the outcome of such events depends on the mass of the neutron stars. For less massive systems, simulations favor the formation of a hypermassive, quasi-stable neutron star, whose oscillations produce a short, high frequency burst of gravitational radiation. Its dominant frequency content is tightly correlated with the radius of the neutron star, and its measurement can be used to constrain the supranuclear equation of state. In contrast, the merger of higher mass systems results in prompt gravitational collapse to a black hole. We have developed an algorithm which combines waveform reconstruction from a morphologyindependent search for gravitational wave transients with Bayesian model selection, to discriminate between post-merger scenarios and accurately measure the dominant oscillation frequency. We demonstrate the efficacy of the method using a catalogue of simulated binary merger signals in data from LIGO and Virgo, and we discuss the prospects for this analysis in advanced ground-based gravitational wave detectors. From the waveforms considered in this work and assuming an optimally oriented source, we find that the post-merger neutron star signal may be detectable by this technique to ∼ 10–25Mpc. We also find that we successfully discriminate between the post-merger scenarios with ∼ 95% accuracy and determine the dominant oscillation frequency of surviving post-merger neutron stars to within ∼ 10Hz, averaged over all detected signals. This leads to an uncertainty in the estimated radius of a non-rotating 1.6M⊙ reference neutron star of ∼ 100m.