Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel Synthesis of Nanosized Cellular Iron Oxide/Oxyhydroxide Thin Films I. Electrochemical Synthesis of Green Rust Thin Films and Their Chemical Oxidation

Iron oxide/oxyhydroxide thin films are currently being investigated for their applications in photoelectrochemical solar cells for solar energy conversion, sensors, catalysts, Li batteries, and electrochemical capacitors.The fabrication of nanoarchitectural thin films is very important for those applications because of the size-related effects of their performance. This study reports the novel synthesis of nanosized cellular iron oxide/oxyhydroxide thin films with high degrees of crystallinity and purity by electrochemical deposition of green rust thin films and their chemical oxidation. This technique is applicable to substrates with large surface areas of a few square centimeters or above without the aid of templates, high temperature, vacuum, or post-treatment.
Bimodal Mesoporous Carbon Nanofibers with High Porosity: Freestanding and Embedded in Membranes for Lithium–Sulfur Batteries

We demonstrate a synthetic approach for highly ordered hexagonal mesoporous carbon nanofibers with bimodal porosity, with an extremely high surface area and a high inner pore volume of 1928 m2/g and 2.41 cm3/g, respectively. A tubular silica template acts as an alternative fiber template for anodic alumina membranes. The sulfur cathode fabricated with the nanofibers shows much better electrochemical performance compared with our previously reported BMC-1/S cathodes by achieving a more homogeneous sulfur distribution in the carbon nanofiber framework. It is also noted that the variation of porous architectures of the carbon framework (i.e., low volumetric ratio of small mesopores over the total pore volume) results in very different electrochemistry, suggesting the significance of porosity optimization for sulfur electrodes.
Mapping the Visual Icon1

It is often claimed that pre-attentive vision has an ‘iconic’ format. This is seen to explain pre-attentive vision’s characteristically high processing capacity and to make sense of an overlap in the mechanisms of early vision and mental imagery. But what does the iconicity of pre-attentive vision amount to? This paper considers two prominent ways of characterising pre-attentive visual icons and argues that neither is adequate: one approach renders the claim ‘pre-attentive vision is iconic’ empirically false while the other obscures its ability to do the explanatory work which motivates positing pre-attentive visual icons in the first place. With this noted, I introduce the (heretofore unarticulated) notion of an ‘Analog Map’ and argue that it provides a superior characterisation of pre-attentive vision’s iconicity. I then argue that this forces a reassessment of debates which have traditionally presupposed the iconicity of pre-attentive vision, emphasising ramifications for the viability of a format-based perception-thought border.
The effect of HRT and carriers on the sludge specifications in MBR to remove VOCs from petrochemical wastewater

AbstractThis work employs two membrane bioreactors (with and without carrier) to evaluate the influence of carrier and hydraulic retention time (HRT) on the sludge specifications when removing organic volatile compounds (e.g. styrene and ethylbenzene) from petrochemical wastewater. This study is conducted during three various HRTs. The results indicate that the optimum HRT (e.g. 15 h.) was achieved in the reactor with carrier during biological removal and membrane fouling minimization. During the optimum HRT, the biological removal efficiency for styrene, ethylbenzene, and COD is 99.8 ± 0.1%, 99.8 ± 0.1%, and 99 ± 0.8%, respectively, and the concentration of styrene and ethylbenzene in the exit air reached to the minimum concentration (e.g. 0.1 and below 0.1 ppm, respectively). The fluctuation of the transmembrane pressure indicates a slight variation for the reactor with carrier rather than without carrier. Further, the sludge particle distribution in reactors demonstrates that the HRT reduction decrease.
An Overview of Text to Graphics Conversion Systems

Text to graphics conversion systems receive a text description of an image or animation from the user and convert it into corresponding image or animation. Such systems have applications in education and computer games. In this paper we describe six well-known text to graphics systems by classifying them into two categories: a) systems that convert text into images and b) systems that convert text into animations. In each category, we start with early system and continue with recent works. Then, we compare the systems based on the type of their input and output and their domain and coverage.
Secure and Resilient SoCs for Autonomous Vehicles

— Embedded systems-on-chip (SoCs) that are targeted to power autonomous vehicles of the future must meet stringent power, real-time performance, reliability, security and safety criteria in order to qualify for deployment in the field. General purpose processor cores, supported by carefully selected accelerators are needed to meet the power-performance requirements. Such heterogeneity at the hardware processing level immediately points to challenges in user-level programmability. In this paper, we present our solution strategy, and we present an updated results summary achieved after the first two phases of DARPA’s DSSoC (Domain-Specific System-on-Chip) program.
Path Formation in a Robot Swarm Self-organized Strategies to Find Your Way Home

We present two swarm intelligence control mechanisms used for distributed robot path formation. In our first approach the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. Our second approach is called forcefield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location. We test each controller on a task that consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our simulation experiments show promising results. All the controllers are able to form paths also in complex obstacle environments, and exhibit very good scalability. Additionally, we observe that chains perform better for smaller robot group sizes, while forcefield performs better for larger groups.
Do Managers Learn from Short Sellers? Evidence from Corporate Acquisitions

We examine how the exemption of short sale uptick tests due to the Regulation SHO pilot program affects managers’ decisions to abandon value-reducing acquisition attempts. We find that when deciding whether to abandon value-reducing acquisition attempts during the program, managers of pilot firms, whose stocks are less subject to short selling impediments, are more sensitive to stock price changes than managers of nonpilot firms. We find no difference in managers’ sensitivity prior to nor post the program. These results indicate that, despite managers’ repugnance toward short sellers, they learn more from stock price changes when short sellers are less impeded.
Pricing Ethics in the Foreign Exchange Market: Environmental, Social and Governance Ratings and Currency Premia

Abstract We examine the cross-sectional predictive ability of the Refinitiv Environmental, Social and Governance (ESG) score for returns in the foreign exchange market, using ESG scores aggregated at the national level, and find that ESG is a strong negative predictor of currency returns. Intuitively, investors require a premium for financing low-ESG countries while high-ESG countries offer lower returns and provide a hedge in the bad state of the world. We show that ESG is priced in the cross-section of currency returns. We also consider the different components of ESG and show that its predictability is driven by the environmental pillar of the ESG ratings. Our results have strong implications for portfolios of individual investors and fund managers and offer new insights for policymakers in the foreign exchange market. The profitability of the ESG currency strategy is not driven by the carry trade and is robust to transaction costs.
Income and Wealth Inequality in Hong Kong, 1981-2020: The Rise of Pluto-Communism?

The objective of this paper is to better understand the evolution and institutional roots of Hong Kong's growing economic inequality and political cleavages. By combining multiple sources of data (household surveys, fiscal data, wealth rankings, national accounts) and methodological innovations, two main findings are obtained. First, he evidence suggests a very large rise in income and wealth inequality in Hong Kong over the last four decades. Second, based on the latest opinion poll data, business elites, who carry disproportionate weight in Hong Kong's Legislative Council, are found to be more likely to vote for the pro-establishment camp (presumably to ensure that policies are passed that protect their political and economic interests). This paper argues that the unique alliance of government and business elites in a partially democratic political system is the plausible institutional root of Hong Kong's rising inequality and political cleavages.
Prehistoric archaeology GIS and intra-site spatial analysis

Intra-site spatial analyses have long been one of the most important methodologies for the study of the formation of paleosurfaces in prehistoric deposits, whose reconstruction undoubtedly plays a decisive role in the validation of subsequent deductive analyses (Texier 2000). An important contribution to the interpretation of prehistoric sites is provided by spatial analyses conducted in contexts explored extensively using the stratigraphic method. The systematic recording of the spatial distribution of finds, be they artefacts, animal bones, or any other archaeological evidence, helps to recognize functional units corresponding to areas set aside for work, butchering, waste dumping, etc. The spatial distribution of finds in an Early Pleistocene site frequented by humans is rarely the result of an undisturbed and non-selective abandonment. In general, this distribution is largely determined by post-depositional disturbance phenomena, whether anthropogenic or not, which always need to be taken into account in any spatial interpretation. One of the most difficult problems to solve is the difficulty of recognizing the sequence of these phenomena, which often occur simultaneously, and are hence hard to differentiate in an overall analysis. Furthermore, especially in the case of very rich layers produced by long-lasting human occupation, the increase and/or overlapping of zones of activity over periods of time that are hard to define limit the possibility of recognizing clear-cut distribution trends, thus undermining the significance of their subsequent chronological and socio-economic interpretation (Yellen 1977; Djindjian 1999). In the case of an Early Pleistocene deposit, the observation of spatial distribution is hence limited to the recognition of post-depositional phenomena, anthropogenic or not, of characteristic associations of archaeological remains, and, if possible, of structures connected to the exploitation of the site (Whallon 1974; Johnson 1976; Binford 1978). In consideration of the multiplicity of the above-listed factors, multidimensional analyses presently appear to be the most advanced techniques of spatial investigation, and have the better chance of solving problems of such complexity (Johnson 1976; Kintigh and Ammermann 1982; Simek 1984; Whallon 1984; Djindjian 1988, 1999). In the case of very rich paleosurfaces such as those of Garba IV D and Gombore I B, and, in general, the Oldowan and Acheulian sites of East Africa, it is impossible to conduct such analyStudies on the Early Paleolithic site of Melka Kunture, Ethiopia 2004: 589-597.
Does Lower Timely Loss Recognition Reflect Managers' Favorable Private Information?

A manager may choose not to record the full extent of bad economic news reflected in negative stock returns (i.e. a manager may exercise low timely loss recognition) if she believes she has private information that justifies a more favorable outlook than the stock market’s pessimistic outlook. Researchers often interpret the resulting deviations between earnings and stock returns as distortions in earnings. However, if managers' more favorable view is correct, earnings will more closely reflect the changes in fundamental value even as they become less aligned with stock returns. In other words, greater deviations between earnings and returns may actually be consistent with more rather than less transparent earnings. We test this possibility using a sample of firm years with negative annual returns. Using future returns as the proxy for the public revelation of managers’ favorable private information, we find that future returns offset current negative returns to a greater extent when managers exercise lower levels of timely loss recognition. Equivalently, we document a negative association between future returns and the portion of implied losses in current negative returns that managers do not recognize. This negative association is stronger when it is more likely that managers have private information. The association is also stronger when managers face higher disclosure costs that prevent full disclosure of their favorable private information. We also find that only a relatively small portion of unrecognized implied losses is recognized in earnings during the subsequent three years. These findings indicate that the traditional explanation for misalignments between earnings and negative stock returns as being due to delayed recognition of economic losses is only partial. Such misalignments are also due to managers’ use of appropriate discretion to include favorable private information in earnings.
THE FLORIDA STATE UNIVERSITY COLLEGE OF ARTS AND SCIENCES APPLICATIONS OF EFFECTIVE FIELD THEORIES TO THE MANY-BODY NUCLEAR PROBLEM AND FRUSTRATED SPIN CHAINS By COSIMO FELLINE

Modern effective-theory techniques are applied to the nuclear many-body problem and frustrated quantum spin chains. A novel approach is proposed for the renormalization of nucleon-nucleon operators in a manner consistent with the construction of the effective potential. To test this approach a one-dimensional, yet realistic, nucleon-nucleon potential is introduced. An effective potential is then constructed by tuning its parameters to reproduce the exact effective-range expansion and a variety of bare operators are renormalized in a fashion compatible with this construction. Predictions for the expectation values of these effective operators in the ground state reproduce the results of the exact theory with remarkable accuracy (at the 0.5% level). We illustrate the main ideas of this work using the elastic form factor of the deuteron as an example. We also apply the COntractor REnormalizator technique to the study of frustrated anti-ferromagnetic zig-zag spin chains with arbitrary half-integer spin. A basis is employed in which three neighboring spins are coupled to a well-defined value of total angular momentum. The basis is then truncated to retain only the lowest lying energy states, and the Hamiltonian renormalized to reproduce the low-lying energy spectrum of the original system. We prove the necessity of retaining two, rather than one, lowest energy eigenstates as frustration is increased. A finite size scaling approach is used to extract ground state energy densities in good agreement with DMRG calculations and spin gaps in qualitative agreement with the disappearance of the Haldane phase around α = 0.3. Moreover, we are able to develop a renormalization group equation that predicts accurately the ground state energy density of the chain in the thermodynamic limit.
Validation and bias correction techniques to improve Numerical Weather Prediction wind speed data

Wind power forecasting is most significant for integration of wind power generation into the Grid. Accuracy of wind prediction is one the biggest challenges faced by researchers around the world as it depends on the Numerical Weather Prediction (NWP) data. The NWP models have evolved over a period of time. However, it is noted that even with various statistical post-processing techniques, there is a void in the accuracy of wind speed forecast with NWP models. This is mainly due to the limitations in the data assimilation process, numerical approximations, parameterization, spatial/temporal resolution and non-availability of actual terrain/ground measurement data. The accuracy of medium term NWP model data output typically day-ahead or weekahead can be heavily impacted due to variation in localized or unforeseen weather changes in a particular site location. Wind speed and wind direction of NWP model data are the most crucial parameters in the modelling of wind power forecast
Industry-Specific Scale: A review on Job Satisfaction of Offshore Employees

The research of job satisfaction has become significant to any business and is a common issue in the area of unusual work environments such as the military, offshore, maritime, polar exploration, nuclear power plants and space programs. Although research in offshore has received much attention, studies on job satisfaction within the context is still limited. Moreover, none of these studies provided structural evidence of a job satisfaction instrument that specifically measures job satisfaction among offshore employees. In fact, these studies used existing instruments to measure employees’ job satisfaction in the offshore context. The absence of a suitable scale to assess employees’ attitudes on job satisfaction might not capture the employees’ real feelings about their satisfaction with the job. Therefore, there is a need for an industry-specific scale measuring offshore employee’s job satisfaction.
Distributed Game-Theoretical Route Navigation for Vehicular Crowdsensing

Vehicular CrowdSensing (VCS) has become a powerful sensing paradigm by selecting users driving vehicles to perform tasks. In most existing research, the platform centrally allocates tasks according to the collected user information. We argue that the information collection process results in user privacy leakage, and the centralized allocation leads to a heavy computation complexity. We propose to apply a distributed task allocation method in the widely-used route navigation system. The navigation system recommends several routes to a user and each route may cover some tasks. Then, the user distributively selects a route according to the route profit (task reward minus route cost). Since the task reward is shared by users, the route selections of users may influence each other. Hence, it remains unclear how to design a distributed route navigation approach to reach an equilibrium state (i.e., each user is satisfied with the selected route), while guaranteeing a good total profit. To this end, we formulate the problem as a multi-user potential game and propose a distributed route navigation algorithm. The trace-based simulation results verify that the proposed algorithm achieves a Nash equilibrium, while achieving a total user profit performance close to that of the optimal solution.
Autonomous Vehicle, Sensing and Communication Survey

Autonomous vehicles introduce dramatic changes to the way in which we travel. This technology has the potential to impact the transportation across a wide array of categories including safety, congestion, and travel behavior. In this report we review the main issues involved in autonomous driving as discussed in the literature and shed light on topics that we believe require further development.
LES3: Learning-based exact set similarity search

Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search. Past approaches on set similarity search utilize either heavy indexing structures, incurring large search costs or indexes that produce large candidate sets. In this paper, we design a learning-based exact set similarity search approach, LES 3 . Our approach first partitions sets into groups, and then utilizes a light-weight bitmap-like indexing structure, called token-group matrix (TGM), to organize groups and prune out candidates given a query set. In order to optimize pruning using the TGM, we analytically investigate the optimal partitioning strategy under certain distributional assumptions. Using these results, we then design a learning-based partitioning approach called L2P and an associated data representation encoding, PTR, to identify the partitions. We conduct extensive experiments on real and synthetic datasets to fully study LES 3 , establishing the effectiveness and superiority over other applicable approaches.
Smallest remnants of early matter domination

The evolution of the universe prior to Big Bang Nucleosynthesis could have gone through a phase of early matter domination which enhanced the growth of small-scale dark matter structure. If this period was long enough, self-gravitating objects formed prior to reheating. We study the evolution of these dense early halos through reheating. At the end of early matter domination, the early halos undergo rapid expansion and eventually eject their matter. We find that this process washes out structure on scales much larger than naively expected from the size of the original halos. We compute the density profiles of the early halo remnants and use them to construct late-time power spectra that include these non-linear effects. We evolve the resulting power spectrum to estimate the properties of microhalos that would form after matter-radiation equality. Surprisingly, cosmologies with a short period of early matter domination lead to an earlier onset of microhalo formation compared to those with a long period. In either case, dark matter structure formation begins much earlier than in the standard cosmology, with most dark matter bound in microhalos in the late universe.
Project Achoo: A Practical Model and Application for COVID-19 Detection From Recordings of Breath, Voice, and Cough

The COVID-19 pandemic created significant interest and demand for infection detection and monitoring solutions. In this paper, we propose a machine learning method to quickly detect COVID-19 using audio recordings made on consumer devices. The approach combines signal processing and noise removal methods with an ensemble of fine-tuned deep learning networks and enables COVID detection on coughs. We have also developed and deployed a mobile application that uses a symptoms checker together with voice, breath, and cough signals to detect COVID-19 infection. The application showed robust performance on both openly sourced datasets and the noisy data collected during beta testing by the end users.
Digital Einstein Experience: Fast Text-to-Speech for Conversational AI

We describe our approach to create and deliver a custom voice for a conversational AI use-case. More specifically, we provide a voice for a Digital Einstein character, to enable human-computer interaction within the digital conversation experience. To create the voice which fits the context well, we first design a voice character and we produce the recordings which correspond to the desired speech attributes. We then model the voice. Our solution utilizes Fastspeech 2 for log-scaled mel-spectrogram prediction from phonemes and Parallel WaveGAN to generate the waveforms. The system supports a character input and gives a speech waveform at the output. We use a custom dictionary for selected words to ensure their proper pronunciation. Our proposed cloud architecture enables for fast voice delivery, making it possible to talk to the digital version of Albert Einstein in real-time.