Miracle-dz/UniFAR-FTUs huggingface dataset:
A Versatile Mapping Approach for Technology Mapping and Graph Optimization

This paper proposes a versatile mapping approach that has three objectives: i) it can map from one technology-independent graph representation to another; ii) it can map to a cell library; iii) it supports logic rewriting. The method is cut-based, mitigates logic-sharing issues of previous graph mapping approaches, and exploits structural hashing. The mapper is the first one of its kind to support remapping among various graph representations, thus enabling specialized mapping to emerging technologies (such as AQFP) and for security applications (such as XAG-based design). We show that mapping to MIGs improves area by 10% as compared to the state of the art, and that technology mapping is 18% faster than ABC with slightly better results.
Multi-level voltage reinjection - a new concept in high voltage source conversion

A voltage source converter is proposed, in which the multi-step output voltage waveform is achieved by the reinjection of different DC voltage levels via the common neutral point of the converter bridges. The number of clamping switches are substantially reduced, permitting the use of higher level numbers to achieve very low distortion without the assistance of PWM. The reinjection concept causes the voltage across the main bridges to vary in step waveforms and the commutations in the main valves to occur at zero voltage, with considerably reduced dynamic voltage stress and switching losses.
Causal Discovery of Adverse Drug Events in Observational Data

Automatic causal discovery without experiments offers to accelerate scientific investigation and knowledge acquisition, for example, by searching databases of electronic health records to discover the unknown effects of drugs. However, effective causal discovery requires methods that control for confounders and that scale to large data sets which have the power to support or refute causal hypotheses. Accordingly, this dissertation first introduces a method for efficiently learning formal structural causal models of medical histories via parameter learning in log-linear temporal Markov networks. Such models work well when all of the effects of interest are already defined and measured, but it might not be the case that all possible effects are suspected beforehand, especially when considering the adverse effects of drugs. Therefore, this dissertation next develops machine learning methods for causal discovery, including differential classification and temporal inverse probability weighting, that hypothesize likely causal effects while analyzing controlled observational studies. Applying all of these methods to causal modeling and finding adverse drug effects in synthetic and real-world electronic health records demonstrates their ability to accurately discover causal effects despite the irregularity, noise, and sparsity of such data. This dissertation thus establishes (1) that scalable, causal methods discover causal effects more accurately than methods that ignore causality, do not scale to large databases, or are not robust to the messiness of medical data, and (2) that methods that hypothesize effects improve genuine causal discovery by avoiding the limitations of human bias. In summary, the methods herein distinguish themselves by bridging machine learning and epidemiology: they bring causal inference and observational studies to machine learning, and they apply learning techniques and formal causal models to tasks in epidemiology. By integrating multiple approaches to causality, these methods achieve a wider perspective that overcomes the limitations of the individual perspectives, and leads to newmethods for automatic causal discovery from observational data.
A pipeline for phase-based analysis of in vitro micro-electrode array recordings of gastrointestinal slow waves

Motility of the gastrointestinal tract (GI) is governed by an bioelectrical event termed slow waves. Accurately measuring the characteristics of GI slow waves is critical to understanding its role in clinical applications. High-resolution (HR) bioelectrical mapping involves placing a spatially dense array of electrodes directly over the surface of the GI wall to record the spatiotemporal changes in slow waves. A micro-electrode array (MEA) with spatial resolution of 200 μm in an 8x8 configuration was employed to record intestinal slow waves using isolated tissues from small animals including rodents, shrews and ferrets. A filtering, processing, and analytic pipeline was developed to extract useful metrics from the recordings. The pipeline relied on CWT and Hilbert Transform to identify the frequency and phase of the signals, from which the individual activation times of slow waves were identified and clustered using k-means. A structural similarity index was applied to group the major activation patterns. Overall, the pipeline identified 91 cycles of slow waves from 300 s of recordings in mice, with an average frequency of 20.68 ± 0.71 cpm, amplitude of 7.94 ± 2.15 µV, and velocity of 3.64 ± 1.75 mm s−1. Three major propagation patterns were identified during this period. The findings of this study will inform the development of a high throughput software platform for future in vitro pharmacological studies using the MEA.
ArtEmis: Affective Language for Visual Art Supplemental Material

In total, we annotated 80,031 artworks covering the entire WikiArt, as downloaded in 2015 [6]. We note that this version of the WikiArt dataset contains 81,446 artworks. However, as our analysis indicated 1,415 artworks were exact duplicates, of the 80,031 unique artworks we kept for annotation purposes. We found these duplicates using the ‘fdupes’ program [5] and limited manual inspection on pairs of nearest-neighbors artworks (using features of a ResNet-32, pretrained on ImageNet), whose distance was smaller than a manually selected threshold. When displaying the image of an artwork in AMT we scale down its largest size to 600 pixels, keeping the original aspect-ratio (or do not apply any scaling if the largest size is less than 600 pixels). We do this scaling to homogenize the presentation of our visual stimuli, and crucially to also reduce the loading and scrolling time required with higher resolution images.
Resilience, Family Functioning, and Psychological Well-Being: Findings from a Cross-sectional Survey of High-school Students

The present study examined the relationship between family functioning and resilience on the psychological well-being (PWB) in high school students. A cross-sectional study was conducted using multiple linear regression for prediction and descriptive statistical analysis in five Indonesian high school students. A total of 341 high school students participated in the study, completing three validated self-administered questionnaires. Statistical analysis revealed that greater family functioning was associated with higher PWB. A significant relationship between resilience and PWB was found in this cohort. This study showed that to maintain healthy mental well-being, developing resilience and family functioning is vital for high school students. More emphasis may be placed on the possible role of resilience training and other kinds of family functioning and coping strategies in dealing with the unavoidable causes of stress in public high schoolers.
Accelerating the energy transition through intermediary management of high-growth energy firms

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
Stability Analysis of Networked Control Systems Subjected to Packet-dropouts and Finite Level Quantization

In this paper, we study the stability of a networked control system involving signal quantization with finitely many levels and a bounded number of consecutive packet-dropouts. To compensate for the effect of packet-dropouts, the controller-encoder sends a packet which contains possible quantized control inputs for finite future steps. At the receiving end, i.e., at the plant actuator side, a buffer decides the actuator input based on the received data. The buffer has memory which is overwritten whenever it receives a packet from the controller. Within this setting, we derive a sufficient condition on quantization parameters for achieving small l∞ signal l∞ stability of the feedback system. The stability condition is characterized in terms of the number of quantization levels of the quantizer.
Improving Smallholder Agriculture via Video-Based Group Extension

Providing technical advice at scale poses operational challenges, particularly with respect to managing a sufficiently large staff. Technology may help, but risks reducing efficacy given reduced customization and human interaction. The researchers tested a video added onto standard human-provided extension services promoting a climate-smart practice, System Rice Intensification in India. Using frequentist statistical methods, they find large but imprecisely estimated treatment effects: The 95% confidence interval is 10kgs to 500kgs and 717Rps to 9650Rps for output and profits, respectively. However, the researchers’ data are not normally distributed: Specifically, key outcomes have fat tails. A Bayesian hierarchical model finds smaller but more precise treatment effects: analogous 95% intervals from -8kgs to 70kgs and -193Rps to 1380Rps. They also test two messaging sub-treatments designed to address commonly cited constraints to adoption: labor needs and self-efficacy. A frequentist analysis shows no added gains, while the Bayesian shows an added benefit when delivered in tandem.
Design end fabrication of a low cost water flow meter

A novel low-cost system consisting of a hot-wire water flow meter of capacity up to 9 liter/minute is presented. The flow does not come into direct contact with the silicon sensors. Thermal contact is ensured by pins in a lead frame. This system is less sensitive to the geometry and relative position of the sensor and the pipe, and simpler to pack, than previous solutions. This paper gives details of the design, fabrication and performance of the system extending our previous work reporting the basic ideas in a qualitative way (1997) and preliminary simulation results (1996).
Signal Quality Assessment of PPG Signals using STFT Time-Frequency Spectra and Deep Learning Approaches

Photoplethysmography (PPG) is an important signal which contains much physiological information like heart rate and cardiovascular health etc. However, PPG signals are easily corrupted by motion artifacts and body movements during their recordings, which may lead to poor quality. In order to accurately extract cardiovascular information, it is necessary to ensure high PPG quality in these applications. Although there are several existed methods to get the PPG signal quality, those algorithms are complex and the accuracies are not very high. Thus, this work proposes a deep learning network for the signal quality assessment using the STFT time-frequency spectra. A total of 5804 10s signals are preprocessed and transformed into 2D STFT spectra with 250 × 334 pixels. The STFT figures are as the input of the CNN networks, and the model gives the result as good or bad quality. The model accuracy is 98.3% with 98.9% sensitivity, 96.7% specificity, and 98.8% F1-score. And the heart rate error is much reduced after classification with the reference of ECG signals. Thus, the proposed deep learning approaches can be useful in the classification of good and bad PPG signals. As far as we know, this is the first article using deep learning methods combined with STFT time-frequency spectra to get the signal quality assessment of PPG signals.
Heat reclaim from air conditioning system with internally submerged and externally wound condenser tubes

Desuperheaters have been in common use for many years to recover heat from the superheated refrigerant of the vapour-compression refrigeration cycle to provide hot water for domestic consumption. Concentric pipe desuperheaters provided with a double-wall construction as a safety precaution are directly submerged in the water tank or indirectly with external recirculating pumps. They are more expensive than bare tube heat exchangers that are directly submerged in the storage tank. It is possible to wrap the condenser coil around the tank with loss of performance. An experimental investigation was conducted to compare the performance of an air conditioning system with these different condensers. The first condenser had a coil submerged in the hot water storage tank while the other had a coil wound round it. The internal coil was found to be more efficient.
Dense Molecular Clouds in the Crab Supernova Remnant

Molecular emission was imaged with ALMA from numerous components near and within bright H2-emitting knots and absorbing dust globules in the Crab Nebula. These observations provide a critical test of how energetic photons and particles produced in a young supernova remnant interact with gas, cleanly differentiating between competing models. The four fields targeted show contrasting properties but within them, seventeen distinct molecular clouds are identified with CO emission; a few also show emission from HCO+, SiO, and/or SO. These observations are compared with Cloudy models of these knots. It has been suggested that the Crab filaments present an exotic environment in which H2 emission comes from a mostly neutral zone probably heated by cosmic rays produced in the supernova surrounding a cool core of molecular gas. Our model is consistent with the observed CO J = 3 − 2 line strength. These molecular line emitting knots in the Crab Nebula present a novel phase of the ISM representative of many important astrophysical environments.
Polarization in broad emission lines of active galactic nuclei

Funding Information Ministry of Education, Science and Technological Development of R. Serbia, 451-03-68/202014/200002. 451-039/2021-14/200104. Ministry of Science and Higher Education of the Russian Federation, We discuss the polarization of broad emission lines in the type 1 active galactic nuclei (AGNs). The polarization depends on the geometry of the broad line region (BLR), and also on the polarization mechanism, or distribution of the scattering material. Therefore the polarization measurements can indicate the geometry of the BLR and the mechanism of polarization (equatorial or polar scattering). In addition, the polarization angle (PA) shape across the line profile can be used to measure the supermassive black hole (SMBH) mass, and constrain the BLR characteristics. We give an overview of ours and other recent investigations of the polarization in broad lines from both aspects: theoretical and observational.
Keys to accurate feature extraction using residual spiking neural networks

Spiking neural networks (SNNs) have become an interesting alternative to conventional artificial neural networks (ANN) thanks to their temporal processing capabilities and energy efficient implementations in neuromorphic hardware. However, the challenges involved in training SNNs have limited their performance in terms of accuracy and thus their applications. Improving learning algorithms and neural architectures for a more accurate feature extraction is therefore one of the current priorities in SNN research. In this paper we present a study on the key components of modern spiking architectures. We design a spiking version of the successful residual network architecture and provide an in-depth study on the possible implementations of spiking residual connections. This study shows how, depending on the use case, the optimal residual connection implementation may vary. Additionally, we empirically compare different techniques in image classification datasets taken from the best performing networks. Our results provide a state of the art guide to SNN design, which allows to make informed choices when trying to build the optimal visual feature extractor. Finally, our network outperforms previous SNN architectures in CIFAR-10 (94.14%) and CIFAR-100 (74.65%) datasets and matches the state of the art in DVS-CIFAR10 (72.98%), with less parameters than the previous state of the art and without the need for ANN–SNN conversion. Code available at: https://github.com/VicenteAlex/Spiking_ResNet.
Agent Spaces

Exploration is one of the most important tasks in Reinforcement Learning, but it is not well-defined beyond finite problems in the Dynamic Programming paradigm (see Subsection 2.4). We provide a reinterpretation of exploration which can be applied to any online learning method. We come to this definition by approaching exploration from a new direction. After finding that concepts of exploration created to solve simple Markov decision processes with Dynamic Programming are no longer broadly applicable, we reexamine exploration. Instead of extending the ends of dynamic exploration procedures, we extend their means. That is, rather than repeatedly sampling every state-action pair possible in a process, we define the act of modifying an agent to itself be explorative. The resulting definition of exploration can be applied in infinite problems and non-dynamic learning methods, which the dynamic notion of exploration cannot tolerate. To understand the way that modifications of an agent affect learning, we describe a novel structure on the set of agents: a collection of distances (see footnote 7) $d_{a} \in A$, which represent the perspectives of each agent possible in the process. Using these distances, we define a topology and show that many important structures in Reinforcement Learning are well behaved under the topology induced by convergence in the agent space.
Automatically identifying a mobile phone user's position within a vehicle

Traffic-related injuries and fatalities are major health risks in the United States. Mobile phone use while driving quadruples the risk for a motor vehicle crash. This work demonstrates the feasibility of using the mobile phone camera to passively detect the location of the phone's user within a vehicle. In a large, varied dataset we were able correctly identify if the user was in the driver's seat or one of the passenger seats with 94.9% accuracy. This model could be used by application developers to selectively change or lock functionality while a user is driving, but not if the user is a passenger in a moving vehicle.
Multifractal analysis of Earthquakes in Central Alborz, Iran; A phenomenological self-organized critical Model

This paper is devoted to a phenomenological study of the earthquakes in central Alborz, Iran. Using three observational quantities, namely weight function, quality factor, and velocity model in this region, we develop a phenomenological dissipative sandpile-like model which captures the main features of the system, especially the average activity field over the region of study. The model is based on external stimuli, the location of which are chosen (I) randomly, (II) on the faults, (III) on the highly active points in the region. We analyze all these cases and show some universal behaviors of the system depending slightly on the method of external stimuli. The multi-fractal analysis is exploited to extract the spectrum of the Hurst exponent of time series obtained by each of these schemes. Although the average Hurst exponent depends on the method of stimuli (the three cases mentioned above), we numerically show that in all cases it is lower than 0.5, reflecting the anticorrelated nature of the system. The lowest average Hurst exponent is for the case (III), in such a way that the more active the stimulated sites are the lower the value for the average Hurst exponent is obtained, i.e. the larger earthquakes are more anticorrelated. However, the different activity fields in this study provide the depth of the basement, the depth variation (topography) of the basement, and an area that can be the location of the future probability event.
An Enactivist account of Mind Reading in Natural Language Understanding

In this paper we apply our understanding of the radical enactivist agenda to the classic AI-hard problem of Natural Language Understanding. When Turing devised his famous test the assumption was that a computer could use language and the challenge would be to mimic human intelligence. It turned out playing chess and formal logic were easy compared to understanding what people say. The techniques of good old-fashioned AI (GOFAI) assume symbolic representation is the core of reasoning and by that paradigm human communication consists of transferring representations from one mind to another. However, one finds that representations appear in another’s mind without appearing in the intermediary language. People communicate by mind reading it seems. Systems with speech interfaces such as Alexa and Siri are of course common, but they are limited. Rather than adding mind reading skills, we introduced a “cheat” that enabled our systems to fake it. The cheat is simple and only slightly interesting to computer scientists and not at all interesting to philosophers. However, reading about the enactivist idea that we “directly perceive” the intentions of others, our cheat took on a new light and in this paper we look again at how natural language understanding might actually work between humans.
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot Manipulation

Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations. Prior work tackles the exploration problem by integrating motion planning and reinforcement learning. However, the motion planner augmented policy requires access to state information, which is often not available in the real-world settings. To this end, we propose to distill a state-based motion planner augmented policy to a visual control policy via (1) visual behavioral cloning to remove the motion planner dependency along with its jittery motion, and (2) vision-based reinforcement learning with the guidance of the smoothed trajectories from the behavioral cloning agent. We evaluate our method on three manipulation tasks in obstructed environments and compare it against various reinforcement learning and imitation learning baselines. The results demonstrate that our framework is highly sample-efficient and outperforms the state-of-the-art algorithms. Moreover, coupled with domain randomization, our policy is capable of zero-shot transfer to unseen environment settings with distractors. Code and videos are available at https://clvrai.com/mopa-pd
Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews

In this paper, we conduct a sentence-level sentiment analysis on the product reviews from Amazon and thorough analysis on the model’s interpretability. For the sentiment analysis task, we mainly use the Bi-LSTM model with attention mechanism. For the study of interpretability, we consider the attention weights distribution of single sentence and the attention weights of main aspect terms. The model has an accuracy of up to 96%. And we find that the aspect terms have the same or even more attention weights than the sentimental words in sentences.