Miracle-dz/UniFAR-FTUs huggingface dataset:
What Do Extreme Weather Events Say About Climate Change? Comparing Politicization and Climate Policy in U.S. Wildfire and Hurricane News Coverage

ABSTRACT The increasing intensity of wildfires and hurricanes signal the reality of climate change, drawing media coverage that could capture the attention of policymakers. In a computational content analysis of 8906 news articles from four national newspapers, we compare coverage of wildfires and hurricanes in the U.S. from 2016 to 2021 examining volume and references to climate change, policy, and politicization. Our findings show patterns that provide new insight into how media may impact policymaking addressing climate change challenges. We find greater mentions of climate change in wildfire news coverage, suggesting that journalists more often associate wildfires with climate change than hurricanes. Volumetric data suggest a potential normalization effect implying decreased media attention of these events could reduce support for subsequent policy action. Overall, however, we do not see evidence that wildfires and hurricanes are focusing events for climate policy. We further discuss the implications of our findings, raising several questions and suggestions for future research. KEY POLICY HIGHLIGHTS Climate change is more often mentioned in mainstream national U.S. news media connected to wildfires, while economic factors are more associated with hurricanes. Related policy may be more accepted when framed accordingly. Because less media attention may be paid to hurricanes and wildfires over time, as the novelty and dramatization factors diminish, the likelihood of their presence on policy agendas may be reduced. Recurring extreme wildfires and hurricanes may become expected by the public, policymakers, and news media. Normalization would mean extreme hurricanes and wildfires won’t fit the definition of focusing events, suggesting that these events would not affect the policy agenda.
Exploring the Potential of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 as Promising Psychobiotics Using SHIME

Psychobiotics are probiotics that have the characteristics of modulating central nervous system (CNS) functions or reconciled actions by the gut–brain axis (GBA) through neural, humoral and metabolic pathways to improve gastrointestinal activity as well as anxiolytic and even antidepressant abilities. The aim of this work was to evaluate the effect of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on the gut microbiota of mildly anxious adults using SHIME®. The protocol included a one-week control period and two weeks of treatment with L. helveticus R0052 and B. longum R0175. Ammonia (NH4+), short chain fatty acids (SCFAs), gamma-aminobutyric acid (GABA), cytokines and microbiota composition were determined. Probiotic strains decreased significantly throughout the gastric phase. The highest survival rates were exhibited by L. helveticus R0052 (81.58%; 77.22%) after the gastric and intestinal phase when compared to B. longum (68.80%; 64.64%). At the genus level, a taxonomic assignment performed in the ascending colon in the SHIME® model showed that probiotics (7 and 14 days) significantly (p < 0.005) increased the abundance of Lactobacillus and Olsenella and significantly decreased Lachnospira and Escheria-Shigella. The probiotic treatment (7 and 14 days) decreased (p < 0.001) NH4+ production when compared to the control period. For SCFAs, we observed after probiotic treatment (14 days) an increase (p < 0.001) in acetic acid production and total SCFAs when compared to the control period. Probiotic treatment increased (p < 0.001) the secretion of anti-inflammatory (IL-6 and IL-10) and decreased (p < 0.001) pro-inflammatory cytokines (TNF-alpha) when compared to the control period. The gut–brain axis plays an important role in the gut microbiota, producing SCFAs and GABA, stimulating the production of anti-anxiety homeostasis. The signature of the microbiota in anxiety disorders provides a promising direction for the prevention of mental illness and opens a new perspective for using the psychobiotic as a main actor of therapeutic targets.
NASA+: Neural Architecture Search and Acceleration for Multiplication-Reduced Hybrid Networks

Multiplication is arguably the most computation-intensive operation in modern deep neural networks (DNNs), limiting their extensive deployment on resource-constrained devices. Thereby, pioneering works have handcrafted multiplication-free DNNs, which are hardware-efficient but generally inferior to their multiplication-based counterparts in task accuracy, calling for multiplication-reduced hybrid DNNs to marry the best of both worlds. To this end, we propose a Neural Architecture Search and Acceleration (NASA) framework for the above hybrid models, dubbed NASA+, to boost both task accuracy and hardware efficiency. Specifically, NASA+ augments the state-of-the-art (SOTA) search space with multiplication-free operators to construct hybrid ones, and then adopts a novel progressive pretraining strategy to enable the effective search. Furthermore, NASA+ develops a chunk-based accelerator with novel reconfigurable processing elements to better support searched hybrid models, and integrates an auto-mapper to search for optimal dataflows. Experimental results and ablation studies consistently validate the effectiveness of our NASA+ algorithm-hardware co-design framework, e.g., we can achieve up to 65.1% lower energy-delay-product with comparable accuracy over the SOTA multiplication-based system on CIFAR100. Codes are available at https://github.com/GATECH-EIC/NASA.
Characteristics of the Raindrop Size Distribution and Drop Shape Relation in Typhoon Systems in the Western Pacific from the 2D Video Disdrometer and NCU C-Band Polarimetric Radar

Abstract The drop size distribution (DSD) and drop shape relation (DSR) characteristics that were observed by a ground-based 2D video disdrometer and retrieved from a C-band polarimetric radar in the typhoon systems during landfall in the western Pacific, near northern Taiwan, were analyzed. The evolution of the DSD and its relation with the vertical development of the reflectivity of two rainband cases are fully illustrated. Three different types of precipitation systems were classified—weak stratiform, stratiform, and convective—according to characteristics of the mass-weighted diameter Dm, the maximum diameter, and the vertical structure of reflectivity. Further study of the relationship between the height H of the 15-dBZ contour of the vertical reflectivity profile, surface reflectivity Z, and the mass-weighted diameter Dm showed that Dm increased with a corresponding increase in the system depth H and reflectivity Z. An analysis of DSDs retrieved from the National Central University (NCU) C-band pola.
Pass-efficient algorithms for clustering

The proliferation of computational problems involving massive data sets has necessitated the design of computational paradigms that model the extra constraints placed on systems processing very large inputs. Among these algorithmic paradigms, the pass-efficient model captures the constraints that the input may be much too large to fit in main memory for processing, and that system performance is optimized by sequential access to the data in storage. Thus, in the pass-efficient model of computation, an algorithm may make a constant number of sequential passes over read-only input while using a small amount of random access memory. The resources to be optimized are memory, number of passes, and per element processing time. We give pass-efficient algorithms for clustering and finding structure in large amounts of data. Our algorithms have the property that the number of passes allotted is an input parameter to the algorithm. We answer questions regarding the intrinsic tradeoffs between the number of passes used by a pass-efficient algorithm and the amount of random access memory required. Our algorithms use adaptive sampling techniques that are quite general and can be used to solve many massive data set problems. The first family of clustering problems that we consider is learning mixtures of distributions. In these problems, we are given samples drawn according to a probability distribution known as a mixture of distributions, and must reconstruct the density function of the original mixture. Our algorithms show tradeoffs between the number of passes and the amount of memory required: if the algorithm makes a few extra passes, the amount of memory required drops off sharply. We also prove lower bounds on the amount of memory needed by any e-pass randomized algorithm, thus showing that our tradeoff is nearly tight. The second family of clustering problems that we consider is the combinatorial optimization problem of facility location and related problems. Our pass-efficient algorithms for this problem exhibit the same sharp tradeoffs as our algorithm for learning mixtures of distributions. We also give clustering algorithms that are not in the streaming model for partitioning a graph to approximately minimize certain natural objective functions.
ROBUST DEPTH ESTIMATION FROM OPTICAL FLOW

A robust method for obtaining dense depth-maps from a sequence of noisy intensity images (obtained by a moving camera is presented. Controlled camera motion is exploited to reduce the complexity of the optical flow solution to closed-form. It is well known that optical flow computations suffer from high noise sensitivity. Tensor-product smoothing splines are employed to construct a 3-D continuous approximation to the time-varying intensity data which serves to reduce the effects of noise and restore the properties of continuous optical flow. Spatial and temporal derivative estimates obtained from the 3-D spline approximation are used to compute the depth. Unreliable depth estimates are discarded based on the temporal rate of change of intensity. The effectiveness of the smoothing spline approximation coupled with the efficiency of tensor products and the closed form solution for depth leads to a robust method for obtaining dense depth maps with low computational cost. Experimental results are presented which demonstrate the robustness of the method applied to real image data. These experiments indicate an average relative error of 3.7 percent in the depth measurement is obtainable using a sequence of seven 8-bit quantized images. Suggestions for further improvements in the quality of the depth maps by adjusting the parameters of the smoothing spline are discussed.
Top-emitting OLED using praseodymium oxide coated platinum as hole injectors

Praseodymium oxide (Pr/sub 2/O/sub 3/) coated platinum (Pt) was investigated as a composite hole-injection layer for "top-emitting" organic light-emitting diodes (OLEDs) based on copper (II) phthalocyanine-N, N'-diphenyl-N, N' bis(3-methylphenyl-1, 1'-biphenyl-4, 4'-diamine-tris-8-hydroxyquinoline aluminum. Aluminum was used as the current-carrying and reflecting anode electrode underneath the composite hole-injection layer. The resulting radiation pattern was found to be highly non-Lambertian. With 1-nm Pr/sub 2/O/sub 3/ on 2-nm Pt, a luminance of /spl sim/1400 cd/m/sup 2/ in the normal direction was obtained. When the intensity was integrated over all angles, it was determined that these OLEDs emitted 30% more radiation than their conventional "bottom-emitting" counterparts. An external quantum efficiency of 1.32% and a power efficiency of 1.1 lm/W were obtained at 100 cd/m/sup 2/. The difference between top- and bottom-emitting diodes is explained in terms of microcavity effects.
Characteristics of Random Nets of Analog Neuron-Like Elements

The dynamic behavior of randomly connected analog neuron-like elements that process pulse-frequency modulated signals is investigated from the macroscopic point of view. By extracting two statistical parameters, the macroscopic state equations are derived in terms of these parameters under some hypotheses on the stochastics of microscopic states. It is shown that a random net of statistically symmetric structure is monostable or bistable, and the stability criteria are explicitly given. Random nets consisting of many different classes of elements are also analyzed. Special attention is paid to nets of randomly connected excitatory and inhibitory elements. It is shown that a stable oscillation exists in such a net? in contrast with the fact that no stable oscillations exist in a net of statistically symmetric structure even if negative as well as positive synaptic weights are permitted at a time. The results are checked by computer-simulated experiments.
A RISC-V Matrix Multiplier Using Systolic Arrays

A RISC-V MATRIX MULTIPLIER USING SYSTOLIC ARRAYS by Miao Wang Many modern day applications can be solved with the usage of machine learning, which involves training a computer to learn on large amounts of data without direct programmer guidance. Conventional computers typically use normal general purpose central processing units, though more specialized tasks may take advantage of more parallel hardware such as graphics processing units. In the pursuit of increased performance to facilitate increasingly more complex machine learning models, researchers in both academia and industry look towards field-programmable gate arrays and application specific integrated circuits for their needs. Various implementations, both theoretical and practical, exist across a wide variety of designs. A custom design, using systolic arrays and built on the existing RISC-V Instruction Set Architecture, will be used to accelerate matrix calculations, with example performance on the MNIST dataset measured.
Signal enumeration and direction-of-arrival estimation for coherent signals in correlated noise with unknown parameters

We are concerned with signal enumeration and direction of arrival (DOA) estimation for coherent signals in correlated noise. The weighted spatial smoothing (WSS) method proposed by Tan and Oh (1997) for estimating the DOA of coherent signals in a class of unknown correlated noise requires good knowledge about the number of signals in the environment concerned. With an insight into their method, we propose an efficient signal enumeration and DOA estimation procedure based on WSS for coherent signals in the class of unknown correlated noise that Tan and Oh addressed. Fairly extensive simulations indicate that our method is very effective and reliable.
An Initial Study of Properties of Monolithic Silicon Dioxide Parts Produced by the Ceramic on-Demand Extrusion Process

AN INITIAL STUDY OF PROPERTIES OF MONOLITHIC SILICON DIOXIDE PARTS PRODUCED BY THE CERAMIC ON-DEMAND EXTRUSION PROCESS by Sam Sujit Choppala The ceramic on-demand extrusion (CODE) process is an additive manufacturing process for technical ceramics, which features an oil bath and partial drying to print stronger green bodies due to better preservation of the printed specimen structure and reduction of uneven part evaporation. A 3D printer capable of performing the CODE process is built with subsystems including motion, extrusion, and heating, and is controlled through LinuxCNC. The CODE printer and process is tested with silicon dioxide as the technical ceramic. High strength silicon dioxide parts are essential in various present-day industries such as dental, medical, and semiconductor. A 55vol% solids loading slurry feedstock is fed into the extruder and is printed in the shape of ASTM A-sized bars using specific printing parameters. The printed green bodies are subject to post-processing including drying in a humidity chamber and debinding and sintering in a sintering furnace. The density was measured at various sintering schedules for the sintered test specimens. Flexural strength values were obtained by performing a 3-point bending test according to the ASTM C1161 standard, and the mean strength values are reported. Both the density and flexural strength values are lower with printing using CODE when compared to other current additive and conventional manufacturing methods by approximately 32% and 77%, respectively. However, this can be attributed due to defects within the printed parts including porosity due to air pockets and insufficient viscosity when pre-processing the slurry and can be minimized to produce better results.
Part I-Smith form and common divisor of polynomial matrices

Polynomial matrices play an important part in linear system calculations. New computational procedures are given for calculation of the Smith normal form and the greatest common right divisor of polynomial matrices. It is shown how suitable transformation matrices can be determined for the calculation of the Smith normal form, and how a set of polynomial matrix multipliers can be calculated for the greatest common right divisor problem. Neither of these algorithms relies on explicit calculation of the greate3t common divisor of polynomials. Limited numerical experience has shown that the3e algorithms are both fast and accurate.
Diabetic rats with high levels of endogenous dopamine do not show retinal vascular pathology

Purpose Limited research exists on the time course of long-term retinal and cerebral deficits in diabetic rodents. Previously, we examined short term (4–8 weeks) deficits in the Goto-Kakizaki (GK) rat model of Type II diabetes. Here, we investigated the long-term (1–8 months) temporal appearance of functional deficits (retinal, cognitive, and motor), retinal vascular pathology, and retinal dopamine levels in the GK rat. Methods In GK rats and Wistar controls, retinal neuronal function (electroretinogram), cognitive function (Y-maze), and motor function (rotarod) were measured at 1, 2, 4, 6, and 8 months of age. In addition, we evaluated retinal vascular function (functional hyperemia) and glucose and insulin tolerance. Retinas from rats euthanized at ≥8 months were assessed for vascular pathology. Dopamine and DOPAC levels were measured via HPLC in retinas from rats euthanized at 1, 2, 8, and 12 months. Results Goto-Kakizaki rats exhibited significant glucose intolerance beginning at 4 weeks and worsening over time (p < 0.001). GK rats also showed significant delays in flicker and oscillatory potential implicit times (p < 0.05 to p < 0.001) beginning at 1 month. Cognitive deficits were observed beginning at 6 months (p < 0.05), but no motor deficits. GK rats showed no deficits in functional hyperemia and no increase in acellular retinal capillaries. Dopamine levels were twice as high in GK vs. Wistar retinas at 1, 2, 8, and 12 months (p < 0.001). Conclusion As shown previously, retinal deficits were detectable prior to cognitive deficits in GK rats. While retinal neuronal function was compromised, retinal vascular pathology was not observed, even at 12+ months. High endogenous levels of dopamine in the GK rat may be acting as an anti-angiogenic and providing protection against vascular pathology.
Pixel features series fusion for precise facial components localisation in probabilistic framework

Abstract A robust and precise scheme for detecting faces and locating the facial components in images at the presence of varying facial contexts as well as complex backgrounds is presented. The system is based on the estimation of the pixel-wised colour distribution of facial components and geometrical information of faces. Probability maps for facial elements are constructed using Gaussian mixture model (GMM) based on the chroma and luma character of facial components. Face candidates are generated based on AdaBoost detection algorithm, and the local skin patch is extracted to generate skin probability map based on GMM. A series of fusion strategy on probability maps is then designed to construct eye, mouth and skin binary maps for verifying each face candidate and locating its facial components, taking facial geometry into consideration. Morphological operators are used for post-processing. Experiments show that more accurate detection results can be obtained as compared to other state-of-the-art methods.
Newly detected, virulent Toxoplasma gondii COUG strain causing fatal steatitis and toxoplasmosis in southern sea otters (Enhydra lutris nereis)

From February 2020 to March 2022, four southern sea otters (Enhydra lutris nereis) stranded in California with severe protozoal steatitis and systemic toxoplasmosis. Three of the infected otters stranded within 26 km of each other, and all four animals died during periods of increased rainfall-driven surface water runoff. High parasite burdens were observed in all tissues except the central nervous system, and toxoplasmosis with severe protozoal steatitis was the primary cause of death for all cases. This lesion pattern differs substantially from all prior reports of toxoplasmosis in sea otters. All cases were T. gondii-positive via serology, immunohistochemistry, and PCR. Multilocus sequence typing at 13 loci revealed that all were infected with the same strain of T. gondii, previously characterized as an atypical and rare genotype in North America (TgCgCa1, or COUG). The COUG genotype was first isolated from mountain lions in British Columbia, Canada during investigation of a waterborne outbreak of toxoplasmosis in humans. This genotype has not been previously reported from sea otters, nor any aquatic species. All prior T. gondii strains obtained from >140 southern sea otters represent Type II or Type X strains, or variants of these genotypes. Archival necropsy data (>1,000 animals over 24 years) were negative for prior cases of severe T. gondii-associated steatitis prior to the cases described herein, and no sublethal COUG T. gondii infections have been previously indentified in sea otters. According to prior studies, the T. gondii COUG genotype is highly virulent in mice and is unusual among T. gondii isolates in eliciting a Type I interferon response in murine and human cells in vitro; this unusual immunomodulatory response could explain the apparent high virulence of this atypical T. gondii strain. Our findings reveal a novel and concerning lesion pattern for sea otters with toxoplasmosis. Due to high zoonotic potential and the risk of infection via shared marine food resources, these findings may also indicate potential health threats for other animals and humans.
Stochastic stability of the continuous-time extended Kalman filter

The error behaviour of the extended Kalman filter is analysed. It is proved that the estimation error remains bounded if the system satisfies a detectability condition and both the initial estimation error and the disturbing noise terms are small enough. Moreover, some selected cases with both bounded and unbounded estimation error are demonstrated by numerical simulations.
Ancient glass classification based on random forest algorithm and decision tree model

The texture change of ancient glass depends on its chemical composition and preservation environment, and it is highly susceptible to weathering by the burial environment. In this paper, a classification model based on random forest algorithm is established to solve the problem of analyzing the classification laws of high potassium glass and lead-barium glass; a decision tree model based on decision tree is established to solve the problem of subclass differentiation of glass categories and analyzing their rationality and sensitivity. The reasonableness of the model and the accuracy of the data are tested by the plain Bayesian algorithm.