Miracle-dz/UniFAR-FTUs huggingface dataset:
THE NUTRITIONAL VALUE AND DIGESTIBILITY OF RICE STRAW ENRICHIED BY OYSTER MUSHROOM ( PLEUROTUS FLORIDA )

ABSTARCT The objective of this study was to evaluate the nutritional values and digestibility of rice straw enriched by oyster mushrooms (Pleurotus florida). The mushrooms spawns were prepared in polythene packets from Agriculture and Natural Resources Research Center, Kerman, Iran. Rice straw were boiled in water bath for 10-15 min and packed in polythene bags and then mixed with the spawns. Protein, fat, ash and organic matters were determined with the procedure recommended by AOAC. The crude fibers and calcium was determined with procedure recommended by Ranganna . Also NDF and ADF were determined by Van Soest method. For evaluation digestibility and nutritional values of enriched rice straws we used A) Laboratory (in vitro) and B) Gas production (in vivo) methods. The GLM procedure of SAS was used for data analysis of variance as completely randomized design and the significant difference among the mean were calculated by T. test multiple range tests. Data from chemical composition showed that DM would be increased by enrichment rice straw. Although OM, NDF, ADF and CF were tended to decrease but also CP was higher than base rice straw significantly. Data for crude ash showed that CASH was higher on enriched rice straw than its base form. These result showed that there were significant differences between dry mater for base and enriched rice straw. Data from this study showed that there were significant differences between dry mater and organic matter between treatments. Also we demonstrated that enrichment by Pleurotus florida could increase DM, OM, OM/DM and Metabolizable energy of rice straw none significantly. Enriching rice straw by Pleurotus florida decreased the averages of effective degradation of dry matter and they were differenced with rates of passage at 2, 4, 6 and 8 hours. We could explain that the enrichment of rice straw with Pleurotus florida could beneficial effect on digestibility, degradability and could increase the nutritional values of rice straws compared to base rice straws.
Measurement-driven Langevin modeling of superparamagnetic tunnel junctions

Superparamagnetic tunnel junctions are important devices for a range of emerging technologies, but most existing compact models capture only their mean switching rates. Capturing qualitatively accurate analog dynamics of these devices will be important as the technology scales up. Here we present results using a one-dimensional overdamped Langevin equation that captures statistical properties of measured time traces, including voltage histograms, drift and diffusion characteristics as measured with Kramers-Moyal coefficients, and dwell times distributions. While common macrospin models are more physically-motivated magnetic models than the Langevin model, we show that for the device measured here, they capture even fewer of the measured experimental behaviors.
EMIE-MAP: Large-Scale Road Surface Reconstruction Based on Explicit Mesh and Implicit Encoding

Road surface reconstruction plays a vital role in autonomous driving systems, enabling road lane perception and high-precision mapping. Recently, neural implicit encoding has achieved remarkable results in scene representation, particularly in the realistic rendering of scene textures. However, it faces challenges in directly representing geometric information for large-scale scenes. To address this, we propose EMIE-MAP, a novel method for large-scale road surface reconstruction based on explicit mesh and implicit encoding. The road geometry is represented using explicit mesh, where each vertex stores implicit encoding representing the color and semantic information. To overcome the difficulty in optimizing road elevation, we introduce a trajectory-based elevation initialization and an elevation residual learning method based on Multi-Layer Perceptron (MLP). Additionally, by employing implicit encoding and multi-camera color MLPs decoding, we achieve separate modeling of scene physical properties and camera characteristics, allowing surround-view reconstruction compatible with different camera models. Our method achieves remarkable road surface reconstruction performance in a variety of real-world challenging scenarios.
Multi-View Active Sensing for Human-Robot Interaction via Hierarchically Connected Tree

Comprehensive perception of human beings is the prerequisite to ensure the safety of human-robot interaction. Currently, prevailing visual sensing approach typically involves a single static camera, resulting in a restricted and occluded field of view. In our work, we develop an active vision system using multiple cameras to dynamically capture multi-source RGB-D data. An integrated human sensing strategy based on a hierarchically connected tree structure is proposed to fuse localized visual information. Constituting the tree model are the nodes representing keypoints and the edges representing keyparts, which are consistently interconnected to preserve the structural constraints during multi-source fusion. Utilizing RGB-D data and HRNet, the 3D positions of keypoints are analytically estimated, and their presence is inferred through a sliding widow of confidence scores. Subsequently, the point clouds of reliable keyparts are extracted by drawing occlusion-resistant masks, enabling fine registration between data clouds and cylindrical model following the hierarchical order. Experimental results demonstrate that our method enhances keypart recognition recall from 69.20% to 90.10%, compared to employing a single static camera. Furthermore, in overcoming challenges related to localized and occluded perception, the robotic arm's obstacle avoidance capabilities are effectively improved.
Randomized, Double-Blinded, Placebo Controlled Study of Neuroprotection With Lidocaine in Cardiac Surgery

Background and Purpose— Cognitive decline after cardiac surgery remains common and diminishes patients' quality of life. Based on experimental and clinical evidence, this study assessed the potential of intravenously administered lidocaine to reduce postoperative cognitive dysfunction after cardiac surgery using cardiopulmonary bypass. Methods— After IRB approval, 277 patients undergoing cardiac surgery were enrolled into this prospective, randomized, double-blinded placebo controlled clinical trial. Subjects were randomized to receive: (1) Lidocaine as a 1 mg/kg bolus followed by a continuous infusion through 48 hours postoperatively, or (2) Placebo bolus and infusion. Cognitive function was assessed preoperatively and again at 6 weeks and 1 year postoperatively. The effect of lidocaine on postoperative cognition was tested using multivariable regression modeling; P<0.05 was considered significant. Results— Among the 241 allocated subjects (Lidocaine: n=114; Placebo: n=127), the incidence of cognitive deficit in the lidocaine group was 45.5% versus 45.7% in the placebo group (P=0.97). Multivariable analysis revealed a significant interaction between treatment group and diabetes, such that diabetic subjects receiving lidocaine were more likely to suffer cognitive decline (P=0.004). Secondary analysis identified total lidocaine dose (mg/kg) as a significant predictor of cognitive decline and also revealed a protective effect of lower dose lidocaine in nondiabetic subjects. Conclusions— Lidocaine administered during and after cardiac surgery does not reduce the high rate of postoperative cognitive dysfunction. Higher doses of lidocaine and diabetic status were independent predictors of cognitive decline. Protective effects of lower dose lidocaine in nondiabetic subjects need to be further evaluated.
Horizons that Gyre and Gimble: A Differential Characterization of Null Hypersurfaces

Motivated by the thermodynamics of black hole solutions conformal to stationary solutions, we study the geometric invariant theory of null hypersurfaces. It is well-known that a null hypersurface in a Lorentzian manifold can be treated as a Carrollian geometry. Additional structure can be added to this geometry by choosing a connection which yields a Carrollian manifold. In the literature various authors have introduced Koszul connections to study the study the physics on these hypersurfaces. In this paper we examine the various Carrollian geometries and their relationship to null hypersurface embeddings. We specify the geometric data required to construct a rigid Carrollian geometry, and we argue that a connection with torsion is the most natural object to study Carrollian manifolds. We then use this connection to develop a hypersurface calculus suitable for a study of intrinsic and extrinsic differential invariants on embedded null hypersurfaces; motivating examples are given, including geometric invariants preserved under conformal transformations.
Compositeness of near-threshold $s$-wave resonances

The near-threshold clustering phenomenon is well understood by the low-energy universality, for shallow bound states below the threshold. Nevertheless, the characteristics of resonances slightly above the threshold still lack thorough elucidation. We introduce a novel probabilistic interpretation scheme for complex compositeness of resonances, in which the resonances with unphysically large decay widths are inherently excluded. Employing this scheme to analyze resonances via the effective range expansion, we demonstrate that near-threshold resonances have small composite fraction, in sharp contrast to shallow bound states below the threshold.
Layer potential operators for transmission problems on extension domains

We use the well-posedness of transmission problems on classes of two-sided Sobolevextension domains to give variational definitions for (boundary) layer potential oper-ators and Neumann-Poincar{\'e} operators. These classes of domains contain Lipschitzdomains, but also domains with fractal boundaries. Our formulation does not involveany measures on the boundary. We discuss basic properties of these operators and usethem to generalize basic results in imaging beyond the Lipschitz case.
Effect of Cryoanalgesia Combined with Intravenous Continuous Analgesia in Thoracotomy Patients

Fifty patients undergoing thoracotomy was studied to compare the effects of cryoanalgesia combined with intravenous continuous analgesia (IVCA). Patients were randomized into two groups: IVCA group and IVCA-cryo group. Subjective pain intensity was assessed on a visual analogue scale at rest (VAS-R) and during movement (VAS-M). Analgesic requirements were evaluated over the 7 days following surgery. Forced vital capacity (FVC) and forced expiratory volume in 1 sec (FEV1) were measured before operation, on the 2nd and 7th postoperative days (POD). We interviewed patients by telephone to evaluate the prevalence of post-thoracotomy pain at the 1st, 3rd, and 6th months postoperatively. No significant differences were observed between the two groups with respect to postoperative pain, analgesic requirements, side effects, respiratory complications, or prevalence of post-thoracotomy pain. However, a significant increase in FVC and FEV1 was observed on the 7th POD in IVCAc-ryo group. The incidence of the post-thoracotomy pain at the 1st, 3rd, and 6th months postoperatively was 68, 60, and 44% in IVCA group, and 88, 68, and 28% in IVCA-cryo group, respectively. Our study showed that cryoanalgesia combined with IVCA effectively restore respiratory function on 7th POD, but that it was not effective at reducing the incidence of post-thoracotomy pain.
Adaptive LPD Radar Waveform Design with Generative Deep Learning

We propose a novel, learning-based method for adaptively generating low probability of detection (LPD) radar waveforms that blend into their operating environment. Our waveforms are designed to follow a distribution that is indistinguishable from the ambient radio frequency (RF) background -- while still being effective at ranging and sensing. To do so, we use an unsupervised, adversarial learning framework; our generator network produces waveforms designed to confuse a critic network, which is optimized to differentiate generated waveforms from the background. To ensure our generated waveforms are still effective for sensing, we introduce and minimize an ambiguity function-based loss on the generated waveforms. We evaluate the performance of our method by comparing the single-pulse detectability of our generated waveforms with traditional LPD waveforms using a separately trained detection neural network. We find that our method can generate LPD waveforms that reduce detectability by up to 90% while simultaneously offering improved ambiguity function (sensing) characteristics. Our framework also provides a mechanism to trade-off detectability and sensing performance.
An in vitro latency system for herpes simplex virus type 2.

An in vitro latency system for herpes simplex virus type 2 (HSV-2) in cultured cells has been developed. Virus replication was suppressed by infection of human foetal lung cells at the supraoptimal temperature of 42 degrees C, and, following transfer of such cell cultures to the normal growth temperature of 37 degrees C, infectious virus was generally undetectable for at least 6 days. HSV-2 was reactivated by intertypic superinfection at 38.5 degrees C with temperature-sensitive mutants of HSV-1, or with human cytomegalovirus, but not by superinfection with adenovirus types 2 or 5. The HSV-1 mutant tsKsyn, which produces only immediate early polypeptides at 38.5 degrees C, was as effective as the late mutant tsIsyn, but tsK which had been irradiated with u.v. light to prevent gene expression did not reactivate HSV-2. The efficiency of reactivation was very high, since 15 to 34% of the theoretical input of infectious HSV-2 particles could be retrieved by superinfection with 0.3 p.f. u. of tsKsyn per cell. Reactivation of latent virus was not induced by cell subculture or by other treatments which alter cell metabolism. The system described here may be important for studies on the molecular basis of HSV latency.
CLASSLA-web: Comparable Web Corpora of South Slavic Languages Enriched with Linguistic and Genre Annotation

This paper presents a collection of highly comparable web corpora of Slovenian, Croatian, Bosnian, Montenegrin, Serbian, Macedonian, and Bulgarian, covering thereby the whole spectrum of official languages in the South Slavic language space. The collection of these corpora comprises a total of 13 billion tokens of texts from 26 million documents. The comparability of the corpora is ensured by a comparable crawling setup and the usage of identical crawling and post-processing technology. All the corpora were linguistically annotated with the state-of-the-art CLASSLA-Stanza linguistic processing pipeline, and enriched with document-level genre information via the Transformer-based multilingual X-GENRE classifier, which further enhances comparability at the level of linguistic annotation and metadata enrichment. The genre-focused analysis of the resulting corpora shows a rather consistent distribution of genres throughout the seven corpora, with variations in the most prominent genre categories being well-explained by the economic strength of each language community. A comparison of the distribution of genre categories across the corpora indicates that web corpora from less developed countries primarily consist of news articles. Conversely, web corpora from economically more developed countries exhibit a smaller proportion of news content, with a greater presence of promotional and opinionated texts.
IPv4 address allocation and the BGP routing table evolution

The IP address consumption and the global routing table size are two of the vital parameters of the Internet growth. In this paper we quantitatively characterize the IPv4 address allocations made over the past six years and the global BGP routing table size changes during the same period of time. About 63,000 address blocks have been allocated since the beginning of the Internet, of which about 18,000 address blocks were allocated during our study period, from November 1997 to August 2004. Among these 18,000 allocations, 90% of them started being announced into the BGP routing table within 75 days after the allocation, while 8% of them has not been used up to now. Among all the address blocks that have ever been used, 45% of them were split into fragments smaller than the original allocated blocks; without these fragementations, the current BGP table would have been about half of its current size. Furthermore, we found that the evolution of BGP routing table consists of both the appearance of new prefixes and the disappearance of old prefixes. While the change of the BGP routing table size only reflects the combined results of the two processes, the dynamics of either process is much higher than that of the BGP table size. Finally, we classify routing prefixes into covering and covered ones, and examine their evolution separately. For the covered prefixes, which account for almost half of the BGP table size, we infer their practical motives such as multihoming, load balancing, and traffic engineering, etc., via a classification method.
Full-Duplex MU-MIMO Systems with Coarse Quantization: How Many Bits Do We Need?

This paper investigates full-duplex (FD) multi-user multiple-input multiple-output (MU-MIMO) system design with coarse quantization. We first analyze the impact of self-interference (SI) on quantization in FD single-input single-output systems. The analysis elucidates that the minimum required number of analog-to-digital converter (ADC) bits is logarithmically proportional to the ratio of total received power to the received power of desired signals. Motivated by this, we design a FD MIMO beamforming method that effectively manages the SI. Dividing a spectral efficiency maximization beamforming problem into two sub-problems for alternating optimization, we address the first by optimizing the precoder: obtaining a generalized eigenvalue problem from the first-order optimality condition, where the principal eigenvector is the optimal stationary solution, and adopting a power iteration method to identify this eigenvector. Subsequently, a quantization-aware minimum mean square error combiner is computed for the derived precoder. Through numerical studies, we observe that the proposed beamformer reduces the minimum required number of ADC bits for achieving higher spectral efficiency than that of half-duplex (HD) systems, compared to FD benchmarks. The overall analysis shows that, unlike with quantized HD systems, more than 6 bits are required for the ADC to fully realize the potential of the quantized FD system.
Boundary Layer Estimates in Stochastic Homogenization

We prove quantitative decay estimates for the boundary layer corrector in stochastic homogenization in the case of a half-space boundary. Our estimates are of optimal order and show that the gradient of the boundary layer corrector features nearly fluctuation-order decay; its expected value decays even one order faster. As a corollary, we deduce estimates on the accuracy of the representative volume element method for the computation of effective coefficients: our understanding of the decay of boundary layers enables us to improve the order of convergence of the RVE method for $d\geq 3$.
Nanoscale brittle-to-ductile transition of the C15 CaAl$_2$ Laves phase

The influence of temperature on the deformation behaviour of the C15 CaAl$_2$ Laves phase, a key constituent for enhancing the mechanical properties of Mg alloys up to service temperatures of 200 {\deg}C, remains largely unexplored. This study presents, for the first time, the nanoscale brittle-to-ductile transition (BDT) of this intermetallic phase through in situ testing including nanoindentation, scratch testing, and micropillar splitting conducted at elevated temperatures. By correlating observations from these techniques, changes in deformation of CaAl$_2$ were identified in relation to temperature. High-temperature nanoindentation quantitatively determined the temperature range for the BDT, and revealed that CaAl$_2$ undergoes a BDT at ~0.55T$_m$, exhibiting an intermediate region of microplasticity. A noticeable decrease in nanoindentation hardness was observed at ~450-500 {\deg}C, accompanied by an increase in residual indent size, while indentation cracking was not observed above 300 {\deg}C. Results from high-temperature micropillar splitting revealed cracking and brittle pillar splitting up to 300 {\deg}C, with an increase in apparent fracture toughness from 0.9 $\pm$ 0.1 MPa$\cdot\sqrt m$ to 2.8 $\pm$ 0.3 MPa$\cdot\sqrt m$, and subsequent crack-free plastic deformation from 400 {\deg}C. Transmission electron microscopy analysis of the deformed material from nanoindentation revealed that the BDT of CaAl$_2$ may be attributed to enhanced dislocation plasticity with increasing temperature.
Preventing Eviction-Caused Homelessness through ML-Informed Distribution of Rental Assistance

Rental assistance programs provide individuals with financial assistance to prevent housing instabilities caused by evictions and avert homelessness. Since these programs operate under resource constraints, they must decide who to prioritize. Typically, funding is distributed by a reactive allocation process that does not systematically consider risk of future homelessness. We partnered with Anonymous County (PA) to explore a proactive and preventative allocation approach that prioritizes individuals facing eviction based on their risk of future homelessness. Our ML models, trained on state and county administrative data accurately identify at-risk individuals, outperforming simpler prioritization approaches by at least 20% while meeting our equity and fairness goals across race and gender. Furthermore, our approach would reach 28% of individuals who are overlooked by the current process and end up homeless. Beyond improvements to the rental assistance program in Anonymous County, this study can inform the development of evidence-based decision support tools in similar contexts, including lessons about data needs, model design, evaluation, and field validation.
Automatic information extraction from Employment Tribunal judgements using large language models

Court transcripts and judgments are rich repositories of legal knowledge, detailing the intricacies of cases and the rationale behind judicial decisions. The extraction of key information from these documents provides a concise overview of a case, crucial for both legal experts and the public. With the advent of large language models (LLMs), automatic information extraction has become increasingly feasible and efficient. This paper presents a comprehensive study on the application of GPT-4, a large language model, for automatic information extraction from UK Employment Tribunal (UKET) cases. We meticulously evaluated GPT-4's performance in extracting critical information with a manual verification process to ensure the accuracy and relevance of the extracted data. Our research is structured around two primary extraction tasks: the first involves a general extraction of eight key aspects that hold significance for both legal specialists and the general public, including the facts of the case, the claims made, references to legal statutes, references to precedents, general case outcomes and corresponding labels, detailed order and remedies and reasons for the decision. The second task is more focused, aimed at analysing three of those extracted features, namely facts, claims and outcomes, in order to facilitate the development of a tool capable of predicting the outcome of employment law disputes. Through our analysis, we demonstrate that LLMs like GPT-4 can obtain high accuracy in legal information extraction, highlighting the potential of LLMs in revolutionising the way legal information is processed and utilised, offering significant implications for legal research and practice.