Miracle-dz/UniFAR-FTUs huggingface dataset:
Jet quenching tests of the QCD Equation of State.

After almost two decades of investigation, jet quenching has become a fundamental tool to study the properties of the QCD matter produced in high-energy nuclear collisions. Despite the large progress in both experimental and theoretical tools, several unknowns remain to be solved. Particularly worrisome is the extraction of the QCD matter parameters and the corresponding comparison with finite temperature QCD, notably from lattice. We show here that improvements in the computation of the medium-induced gluon radiation spectrum are needed for a proper extraction of these parameters, and we make a first attempt to compare them with lattice results. To that end, we employ an improved numerical implementation of multiple scatterings that resums all possible terms in the opacity expansion beyond the Gaussian approximation. We find significant differences in the extracted medium parameters when we compare our exact resummation results with either including only the first order in opacity, or using the Gaussian approximation, the two most common approximations in phenomenological analyses to date.
Estimating the Frequency of Sexual Reproduction in the Diatom Stephanodiscus niagarae

Diatoms are single-celled micro-algae with cell walls composed of silica, that reproduce in a way that results in a decrease in cell size after each round of mitotic (asexual) division. A cell cannot continue to shrink indefinitely, so when the average size of the population reaches a critical threshold, diatoms reproduce sexually and restore their maximal size. It is unclear, however, how frequently diatoms undergo sexual reproduction in nature. As a proxy for sexual reproduction, I monitored changes in cell size of a population of the diatom Stephanodiscus niagarae from the plankton community of Lake Fayetteville (Arkansas, USA). Weekly sampling during the Fall 2015-Spring 2016 period revealed that S. niagarae is a winter species, occurring after a shift in the planktonic community of Lake Fayetteville, in which populations of warmwater green and blue-green algae decline and are replaced by a diatom-dominated community. S. niagarae first occurred in December, in clear colder water at 10 C or below. Within weeks, this population grew steadily from a few hundred to several thousand cells per milliliter, reaching a maximum density in late January. The population declined rapidly thereafter. Although this decline coincided with increased lake temperature and the proportion of cells infected by a chytrid parasite, it is not clear if these two factors are the proximal cause for S. niagarae declines. Asexual reproduction resulted in substantial variation in average cell size, and an increase in variance towards the end of the growth period. However, the modest variation in median cell diameter of about 5 μm indicated that a synchronous population-wide sexual reproduction event did not occur during the course of the study. These results suggest that the frequency of sexual reproduction in S. niagarae in Lake Fayetteville might be on the order of several to dozens of years. However, the possibility of asynchronous sexual reproduction and a population with overlapping generations cannot be ruled out.
Optimization of Penalty Parameter in Penalized Nonlinear Canonical Correlation Analysis by using Cross-Validation

There is Canonical Correlation Analysis (CCA) as a way to find a linear relationship between a pair of random vectors. However, CCA cannot find a nonlinear relationship between them since the method maximizes the correlation between linear combinations of the vectors. In order to find the nonlinear relationship, we convert the vectors through some known conversion functions like a kernel function. Then we find the nonlinear relationship in the original vectors through the conversion function. However, this method has a critical issue in that the maximized correlation sometimes becomes 1 even if there is no relationship between the random vectors. Some author proposed a penalized method with a penalty parameter that avoids this issue when the kernel functions are used for conversion. In this method, however, methods have not been proposed for optimizing the penalty and other hyper parameters in the conversion function, even though the results heavily depend on these parameters. In this study, we propose an optimization method for the penalty and other parameters, based on the simple cross-validation method.
Optimizing the LO Distribution Architecture of mm-Wave Massive MIMO Receivers.

Wireless networks at millimeter wavelengths have significant implementation difficulties. The path loss at these frequencies naturally leads us to consider antenna arrays with many elements. In these arrays, local oscillator (LO) generation is particularly challenging since the LO specifications affect the system architecture, signal processing design, and circuit implementation. We thoroughly analyze the effect of LO architecture design choices on the performance of a mm-wave massive MIMO uplink. This investigation focuses on the tradeoffs involved in centralized and distributed LO generation, correlated and uncorrelated phase noise sources, and the bandwidths of PLLs and carrier recovery loops. We show that, from both a performance and implementation complexity standpoint, the optimal LO architecture uses several distributed subarrays locked to a single intermediate-frequency reference in the low GHz range. Additionally, we show that the choice of PLL and carrier recovery loop bandwidths strongly affects the performance; for typical system parameters, loop bandwidths on the order of tens of MHz achieve SINRs suitable for high-order constellations. Finally, we present system simulations incorporating a complete model of the LO generation system and consider the case of a 128-element array with 16x-spatial multiplexing and a 2 GHz channel bandwidth at 75 GHz carrier. Using our optimization procedure we show that the system can support 16-way spatial multiplexing with 64-QAM modulation.
DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning

DeepRacer is a platform for end-to-end experimentation with RL and can be used to systematically investigate the key challenges in developing intelligent control systems. Using the platform, we demonstrate how a 1/18th scale car can learn to drive autonomously using RL with a monocular camera. It is trained in simulation with no additional tuning in physical world and demonstrates: 1) formulation and solution of a robust reinforcement learning algorithm, 2) narrowing the reality gap through joint perception and dynamics, 3) distributed on-demand compute architecture for training optimal policies, and 4) a robust evaluation method to identify when to stop training. It is the first successful large-scale deployment of deep reinforcement learning on a robotic control agent that uses only raw camera images as observations and a model-free learning method to perform robust path planning. We open source our code and video demo on GitHub: this https URL.
Blood viscosity Mood Alcohol Menstrual cycle Hemoglobin Cognitive capacity Recreational opioids Pregnancy Fibrinogen Creativity Amphetamines Menopause Blood glucose Personality Cocaine Diurnal rhythm Homocysteine Sleep Cannabis BMI Cholesterol Drowsiness / sleepiness Solvents and inhalants Physical 

Quantitative measurements of brain perfusion are influenced by perfusion-modifiers. Standardization of measurement conditions and correction for important modifiers is essential to improve accuracy and to facilitate the interpretation of perfusion-derived parameters. An extensive literature search was carried out for factors influencing quantitative measurements of perfusion in the human brain unrelated to medication use. A total of 58 perfusion modifiers were categorized into four groups. Several factors (e.g., caffeine, aging, and blood gases) were found to induce a considerable effect on brain perfusion that was consistent across different studies; for other factors, the modifying effect was found to be debatable, due to contradictory results or lack of evidence. Using the results of this review, we propose a standard operating procedure, based on practices already implemented in several research centers. Also, a theory of ‘deep MRI physiotyping’ is inferred from the combined knowledge of factors influencing brain perfusion as a strategy to reduce variance by taking both personal information and the presence or absence of perfusion modifiers into account. We hypothesize that this will allow to personalize the concept of normality, as well as to reach more rigorous and earlier diagnoses of brain disorders.
"Treasure the Exceptions": An Investigation into why Heat-treated, Isolated Chloroplasts Fail to Respond to Uncoupling Reagents

Heat treated chloroplasts lose the ability to transfer electrons from the oxidation of water to a synthetic electron acceptor (DCPIP) but will still transfer electrons to the acceptor in the presence of an artificial electron donor (DPC). In many respects, heat treated chloroplasts respond predictably to a variety of experimental conditions but, unexpectedly, they do not respond to uncoupling reagents that usually greatly increase the rate of electron transport in untreated chloroplasts. An investigation of this unexpected result reveals that heat treatment not only prevents chloroplasts from oxidizing water but also uncouples the thylakoid membranes. Moreover, evidence is presented suggesting that heat treatment has one or more additional effects that reduce the rate at which electrons are transferred to the acceptor. This investigation of an unexpected result provides a useful lesson in how scientific progress is sometimes made by paying attention to experimental results that are not predicted. Keywords Isolated chloroplasts, Thylakoids, Heat treatment, Uncoupling reagents, Dichlorophenol indophenol (DCPIP), gramicidin D, Diphenyl carbazide (DPC), Ammonium chloride, Electron transport rate
Norms of Allocation within Nuclear and Extended-Family Households

In many parts of Africa, traditional household structures consisting of an extended family headed by a patriarch are giving way to other types of households -- e.g. nuclear families, female-headed households -- as a result of migration, urbanisation and population pressures on land. In this paper, we explore whether traditional norms which determine how resources are allocated within the household are affected by the evolution of household structures. We show that the allocation of resources, for production and consumption, are closer to being efficient in nuclear family households as compared to extended family households. The findings are consistent with the hypothesis that individuals belonging to the same nuclear family have stronger ties, enabling them to commit to more efficient contracts infeasible for those connected through an extended family relation.
Waste identification diagrams

Value Stream Mapping (VSM) is a very popular tool in lean environments to represent production flows, mapping value stream of a product or family of products, and helps to identify some types of waste. Although very popular, this tool has some limitations as already described in many publications, especially in terms of restrictions in showing most types of waste as well as in its inability to represent various production routes. The purpose of this study is to introduce the waste identification diagram (WID), a new tool to represent production units with its different forms of waste, which overcomes some VSM limitations. The originality of WID comes from the use of its symbols’ dimensions to convey, in a visual and immediate way, relevant information about a production unit. In this paper, WID is applied on a production unit of a lift manufacturer, for testing its performance and comparing it to VSM. The main findings are that WID is in general more effective than VSM in terms of representation of complex production units and in terms of identification of more forms of waste. WID must however overcome some of its limitations such as the lack of information-flow representation and the links to suppliers and clients.
2 Rough Sets and Rule Induction 2

In this paper, we examine the effects of the application of LEM2 to a hierarchical structure of decision classes. We consider classification problems with multiple decision classes by nominal condition attributes. To such a problem, we first apply an agglomerative hierarchical clustering method to obtain a dendrogram of decision classes, i.e., a hierarchical structure of decision classes. At each branch of the dendrogram, we then apply LEM2 to induce rules inferring a cluster to which an object belongs. A classification system suitable for the proposed rule induction method is designed. By a numerical experiment, we compare the proposed methods with different similarity measure calculations, the standard application of LEM2 and a method with randomly generated dendrogram. As the result, we generally demonstrate the advantages of the proposed method.
Long-range concealed object detection through active covert illumination

A common surveillance problem is the automatic detection of objects concealed under clothing and the identification of those carrying them. As many 2D methods rely on texture information, the application of patterned clothing can be used to camouflage features that may provide a clue as to the shape of the object hidden beneath. Photometric stereo (PS) is a 3D surface reconstruction technique utilising several images of an object, lit from multiple directions, a particular advantage of which is that it reliably separates textural elements, such as printed patterns, from physical shape offering many possibilities for concealed object detection. The success of such a technique is primarily dependent on the ability to artificially illuminate the subject considerably more brightly than the ambient lighting. At night, this is readily plausible; and longer wavelength, near-infrared (nIR) lighting allows us to capture the images covertly. However in daytime, sunlight can prevent sufficient illumination of the subject to calculate the surface image, especially at long range. Certain wavelengths of light are attenuated by airborne moisture considerably more than others. By using a wavelength of light that is heavily attenuated by the atmosphere, in combination with a narrow bandpass filter, we show that it is possible to provide sufficient lighting contrast to perform PS over much longer distances than in previous work. We examine the 940nm wavelength, which falls within one of these spectral regions and evaluate sensor technology equipped with a “black silicon” CMOS, offering extreme light sensitivity, against cameras using traditional silicon sensors, with application to long distance surface reconstruction using PS. Having shown that we can produce reconstructions of considerably better quality than those from traditional cameras, we present several methods for the reliable detection of concealed objects and recognition of faces, using the high level of surface detail that PS can provide.
A Linkage Based Imputation Method for Missing SNP Markers in Association Mapping

A Linkage based Imputation Method for Missing SNP Markers in Association Mapping Association mapping has been widely used in detecting genetic markers associated with traits of importance in research areas such as plant breeding, human disease and animal breeding. In recent years, various useful statistical methods and computing tools have been developed for association mapping studies. One of the critical challenges in association mapping is missing markers.
Extractive NarrativeQA with Heuristic Pre-Training

Although advances in neural architectures for NLP problems as well as unsupervised pre-training have led to substantial improvements on question answering and natural language inference, understanding of and reasoning over long texts still poses a substantial challenge. Here, we consider the task of question answering from full narratives (e.g., books or movie scripts), or their summaries, tackling the NarrativeQA challenge (NQA; Kocisky et al. (2018)). We introduce a heuristic extractive version of the data set, which allows us to approach the more feasible problem of answer extraction (rather than generation). We train systems for passage retrieval as well as answer span prediction using this data set. We use pre-trained BERT embeddings for injecting prior knowledge into our system. We show that our setup leads to state of the art performance on summary-level QA. On QA from full narratives, our model outperforms previous models on the METEOR metric. We analyze the relative contributions of pre-trained embeddings and the extractive training paradigm, and provide a detailed error analysis.
Deconstruction project planning based on automatic acquisition and reconstruction of building information for existing buildings

As energetic, health and environmental requirements for buildings are changing, deconstruction of buildings in the course of retrofits or replacing construction is increasingly important. To plan change measures in existing buildings, buildings have to be audited manually which is associated with great effort. In our contribution, we propose and describe a combined system of a hardware sensor with software modules for building information acquisition, reconstruction and project planning. Furthermore, technical requirements, the acquired data, user interaction and system architecture are discussed. Our tool enables planner, experts or decision makers to inspect a building and at the same time digitally audit the building room by room. For this purpose, point clouds are acquired, analysed and a 3D building model is automatically derived to record it. Furthermore, the acquired data is automatically analysed in realtime to detect construction elements that are saved in a database. Then, based on the generated building element database building reconstruction and planning algorithms use the information for building inventorying and project planning. This allows integrated planning of decontamination, site clearance, and deconstruction activities, as well as to coordinate secondary raw material recovery, resources, logistics, material storage and recycling options time and cost efficiently onsite. Results from first field tests on acquisition, reconstruction and deconstruction planning are presented and discussed. Finally, a summary and a conclusion are given. This is followed by an outlook on potential future research and application areas.
Development and Application of a Hot-Dry-Windy Index (HDW) Climatology

In this paper, we describe and analyze a climatology of the Hot-Dry-Windy Index (HDW), with the goal of providing fire-weather forecasters with information about the daily and seasonal variability of the index. The 30-year climatology (1981–2010) was produced using the Climate Forecast System Reanalysis (CFSR) for the contiguous United States, using percentiles to show seasonal and geographical variations of HDW contained within the climatology. The method for producing this climatology is documented and the application of the climatology to historical fire events is discussed. We show that the HDW climatology provides insight into near-surface climatic conditions that can be used to identify temperature and humidity trends that correspond to climate classification systems. Furthermore, when used in conjunction with daily traces of HDW values, users can follow trends in HDW and compare those trends with historical values at a given location. More usefully, this climatology adds value to HDW forecasts; by combining the CFSR climatology and a Global Ensemble Forecast System (GEFS) ensemble history and forecast, we can produce a single product that provides seasonal, climatological, and short-term context to help determine the appropriate fire-management response to a given HDW value.
The group of boundary fixing homeomorphisms of the disc is not
 left-orderable

A left-order on a group $G$ is a total order $<$ on $G$ such that for any $f$, $g$ and $h$ in $G$ we have $f < g \Leftrightarrow hf < hg$. We construct a finitely generated subgroup $G$ of $\operatorname{Homeo} (I^2;\delta I^2)$, the group of those homeomorphisms of the disc that fix the boundary pointwise, and show $G$ does not admit a left-order. Since any left-order on $\operatorname{Homeo} (I^2;\delta I^2)$ would restrict to a left-order on $G$ this shows that $\operatorname{Homeo} (I^2;\delta I^2)$ does not admit a left-order. Since $\operatorname{Homeo} (I;\delta I)$ admits a left-order it follows that neither $G$ nor $\operatorname{Homeo} (I^2;\delta I^2)$ embed in $\operatorname{Homeo} (I;\delta I)$.
Marine Ice Cliff Instability Mitigated by Slow Removal of Ice Shelves

The accelerated calving of ice shelves buttressing the Antarctic Ice Sheet may form unstable ice cliffs. The marine ice cliff instability hypothesis posits that cliffs taller than a critical height (~90 m) will undergo structural collapse, initiating runaway retreat in ice‐sheet models. This critical height is based on inferences from preexisting, static ice cliffs. Here we show how the critical height increases with the timescale of ice‐shelf collapse. We model failure mechanisms within an ice cliff deforming after removal of ice‐shelf buttressing stresses. If removal occurs rapidly, the cliff deforms primarily elastically and fails through tensile‐brittle fracture, even at relatively small cliff heights. As the ice‐shelf removal timescale increases, viscous relaxation dominates, and the critical height increases to ~540 m for timescales greater than days. A 90‐m critical height implies ice‐shelf removal in under an hour. Incorporation of ice‐shelf collapse timescales in prognostic ice‐sheet models will mitigate the marine ice cliff instability, implying less ice mass loss.
Symmetries and invariants for non-Hermitian Hamiltonians

We discuss Hamiltonian symmetries and invariants for quantum systems driven by non-Hermitian Hamiltonians. For time-independent Hermitian Hamiltonians, a unitary or antiunitary transformation AHA† that leaves the Hamiltonian H unchanged represents a symmetry of the Hamiltonian, which implies the commutativity [H,A]=0 and, if A is linear and time-independent, a conservation law, namely the invariance of expectation values of A. For non-Hermitian Hamiltonians, H† comes into play as a distinct operator that complements H in generalized unitarity relations. The above description of symmetries has to be extended to include also A-pseudohermiticity relations of the form AH=H†A. A superoperator formulation of Hamiltonian symmetries is provided and exemplified for Hamiltonians of a particle moving in one-dimension considering the set of A operators that form Klein’s 4-group: parity, time-reversal, parity&time-reversal, and unity. The link between symmetry and conservation laws is discussed and shown to be richer and subtler for non-Hermitian than for Hermitian Hamiltonians.
Operating System Mobile Applications User-level System software Environment Aware Subsystem

In mobile computing, factors such as add-on hardware components and heterogeneous networks result in an environment of changing resource constraints. An application in such a constrained environment must react to these changes so that available resources are properly utilized. In this paper, we propose a framework to build environment aware applications. The architecture is based on an event delivery mechanism that decouples event detection from delivery, giving the flexibility and extensibility that is necessary in a mobile computing environment. Information associated with the event is delivered as part of the event notification, while delivery latency is reduced by clever thread scheduling. We demonstrate the utility of this framework by structuring an environment aware networking subsystem around a prototype implementation. The performance of this implementation is competitive with current event delivery mechanisms such as the Unix signal.