Miracle-dz/UniFAR-FTUs huggingface dataset:
AMMU - A Survey of Transformer-based Biomedical Pretrained Language Models

Transformer-based pretrained language models (PLMs) have started a new era in modern natural language processing (NLP). These models combine the power of transformers, transfer learning, and self-supervised learning (SSL). Following the success of these models in the general domain, the biomedical research community has developed various in-domain PLMs starting from BioBERT to the latest BioMegatron and CoderBERT models. We strongly believe there is a need for a survey paper that can provide a comprehensive survey of various transformer-based biomedical pretrained language models (BPLMs). In this survey, we start with a brief overview of foundational concepts like self-supervised learning, embedding layer and transformer encoder layers. We discuss core concepts of transformer-based PLMs like pretraining methods, pretraining tasks, fine-tuning methods, and various embedding types specific to biomedical domain. We introduce a taxonomy for transformer-based BPLMs and then discuss all the models. We discuss various challenges and present possible solutions. We conclude by highlighting some of the open issues which will drive the research community to further improve transformer-based BPLMs.
Different parts of the same elephant: A roadmap to disentangle and connect different perspectives on prosodic prominence

Prosodic prominence is an umbrella term encompassing various related but conceptually and functionally different phenomena such as phonological stress, paralinguistic emphasis, lexical, syntactic, semantic or pragmatic salience, to mention a few. Due to the high interest prominence has received from various disciplines, it has been studied from multiple perspectives (functional, physical, cognitive). It also has been operationalised and annotated across different descriptive levels (syllable, word), based on different scales (categorical, multi-level, continuous), and measured across a large variety of signal domains (acoustic, articulatory, gestural). The present paper offers an overview of the various perspectives involved and defines a preliminary roadmap for a better and more unified understanding of this multi-faceted phenomenon.
RANGE VEGETATION ASSESSMENT IN THE BIG DESERT, UPPER SNAKE RIVER PLAIN, IDAHO 2005

Vegetation data was collected at 305 randomly located sample points between June 1 and July 15, 2005 (206 in the USDI BLM Big Desert Region and 99 in the O’Neal Study area located 3 miles north of McCammon, Idaho). We collected data describing percent cover of grasses and shrubs, dominant weed and shrub species, fuel load, sagebrush age, GAP vegetation class, presence of microbial crust, litter type, forage availability, and photo points. Sample points were stratified by fire, grazing, and rest treatments. A high amount of cheatgrass was found as well as a high amount of bare ground. However, in 2005 forage availability increased from previous years, probably due to increased rainfall.
Synthesizing Modal Transition Systems from Triggered Scenarios

Synthesis of operational behavior models from scenario-based specifications has been extensively studied. The focus has been mainly on either existential or universal interpretations. One noteworthy exception is Live Sequence Charts (LSCs), which provides expressive constructs for conditional universal scenarios and some limited support for nonconditional existential scenarios. In this paper, we propose a scenario-based language that supports both existential and universal interpretations for conditional scenarios. Existing model synthesis techniques use traditional two-valued behavior models, such as Labeled Transition Systems. These are not sufficiently expressive to accommodate specification languages with both existential and universal scenarios. We therefore shift the target of synthesis to Modal Transition Systems (MTS), an extension of labeled Transition Systems that can distinguish between required, unknown, and proscribed behavior to capture the semantics of existential and universal scenarios. Modal Transition Systems support elaboration of behavior models through refinement, which complements an incremental elicitation process suitable for specifying behavior with scenario-based notations. The synthesis algorithm that we define constructs a Modal Transition System that uses refinement to characterize all the Labeled Transition Systems models that satisfy a mixed, conditional existential and universal scenario-based specification. We show how this combination of scenario language, synthesis, and Modal Transition Systems supports behavior model elaboration.
A 1 mA Ultra-Low-Power FHSS TX Front-End Utilizing Direct Modulation With Digital Pre-Distortion

This paper deals with the system and circuit-level aspects of an ultra-low-power robust wireless node for an asymmetric wireless link. A single building block TX front-end for a frequency hopping spread spectrum (FHSS) transmitter implemented in silicon-on-anything (SOA) bipolar technology is presented. It is realized with a directly modulated RF cascoded Colpitts power voltage-controlled oscillator (VCO), a frequency locked loop for center frequency calibration, and a digital pre-distortion algorithm for accurate frequency bins synthesis. The TX front-end draws only 1 mA at -18 dBm output power. By combining digital system techniques for frequency hopping and merging the VCO and the power amplifier (PA), a robust solution is obtained for indoor ultra-low-power wireless links. The proposed pre-distortion concept allows reduction of the hardware complexity, while the combination of a cascode output buffer and a common-collector Colpitts VCO allows us to reduce the complete FHSS front-end to a single building block that directly drives the antenna through a balun. A dedicated digital algorithm on the receiver side reduces the center frequency offset from a maximum value of 8.2 MHz to less than 8 ppm avoiding the use of any crystal on the transmitter side. Precision in the hopping synthesis is obtained by employing a ST-DFT based demodulator with differential encoding and an offset sending technique. The novel FHSS-predistortion concept has been verified by realizing a full wireless link that achieves a bit error rate better than 1.1% at -25 dBm output power while transmitting across an 8 meters indoor non-line-of-sight (NLOS) path.
A Framework for Control System Design Subject to Average Data-Rate Constraints

This paper studies discrete-time control systems subject to average data-rate limits. We focus on a situation where a noisy linear system has been designed assuming transparent feedback and, due to implementation constraints, a source-coding scheme (with unity signal transfer function) has to be deployed in the feedback path. For this situation, and by focusing on a class of source-coding schemes built around entropy coded dithered quantizers, we develop a framework to deal with average data-rate constraints in a tractable manner that combines ideas from both information and control theories. As an illustration of the uses of our framework, we apply it to study the interplay between stability and average data-rates in the considered architecture. It is shown that the proposed class of coding schemes can achieve mean square stability at average data-rates that are, at most, 1.254 bits per sample away from the absolute minimum rate for stability established by Nair and Evans. This rate penalty is compensated by the simplicity of our approach.
HSPF Modeling of Nonpoint Sources in Tickfaw River Watershed

Louisiana has twelve water quality management basins delineated by natural drainage patterns of the state's major river basins. The Tickfaw River watershed in the Lake Pontchartrain basin is especially challenged from rapid population growth, industrial activities, and agricultural use. This watershed is located in southeastern Louisiana with Tickfaw River flowing from the Mississippi state line to Springville at Louisiana Highway 42 then to Lake Maurepas with a total drainage area of 1,896 km2. The elevation of the watershed changes from 0 m above sea level in the south to 130 m in the north. Forests cover 66% of the watershed. Agriculture is the second predominant land use type in the watershed. Outstanding natural resource and secondary contact recreation designated uses are fully supported, but fish and wildlife propagation and primary contact recreation are not supported. There are many suspected sources of impairment, including agriculture, construction, forest management, and industrial sources. The specific objective is to model the Tickfaw river watershed in order to quantify and differentiate the sources of pollution that arise from agriculture, forestry, urban storm water runoff, and other sources making use of a Geographic Information System (GIS), the EPA’s BASINS tools, HSPF for modeling water quantity and quality, and ArcView 3.2.
Regional scale flood modeling and watershed investigation, using NEXRAD rainfall, GIS, and HEC-HMS/RAS: A case study for the San Antonio River Basin, Texas

This research develops a framework for regional scale flood modeling that integrates NEXRAD Level III rainfall, GIS, and a hydrological model (HEC-HMS/RAS). The San Antonio River Basin (about 4000 square miles, 10000 square kilometers) in central Texas, USA, is the domain of the study because it is a region subject to frequent occurrences of severe flash flooding. A major flood in the summer of 2002 is chosen as a case to examine the modeling framework. The model consists of a rainfall-runoff model (HEC-HMS) that converts precipitation excess to overland flow and channel runoff, as well as a hydraulic model (HEC-RAS) that models unsteady state flow through the river channel network based on the HEC-HMS derived hydrographs. HEC-HMS is run on a 4 km × 4 km grid in the domain, a resolution consistent with the resolution of NEXRAD rainfall taken from the local river authority. Watershed parameters are calibrated manually to produce a good simulation of discharge at 12 subbasins. With the calibrated discharge, HEC-RAS is capable of producing floodplain polygons that are comparable to the satellite imagery. The modeling framework presented in this study incorporates a portion of the recently developed GIS tool named Map to Map that has been created on a local scale and extends it to a regional scale. The results of this research will benefit future modeling efforts by providing a tool for hydrological forecasts of flooding on a 6 regional scale. While designed for the San Antonio River Basin, this regional scale model may be used as a prototype for model applications in other areas of the country.
Identity-Preserving Knowledge Distillation for Low-resolution Face Recognition

Low-resolution face recognition (LRFR) has become a challenging problem for modern deep face recognition systems. Existing methods mainly leverage prior information from high-resolution (HR) images by either reconstructing facial details with super-resolution techniques or learning a uniﬁed feature space. To address this issue, this paper proposes a novel approach which enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution (LR) image. A cross-resolution knowledge distillation paradigm is ﬁrst employed as the learning framework. An identity-preserving network, WaveResNet, and a wavelet similarity loss are then designed to capture low-frequency details and boost performance. Finally, an image degradation model is conceived to simulate more realistic LR training data. Consequently, extensive experimental results show that the proposed method consistently outperforms the baseline model and other state-of-the-art methods across a variety of image resolutions.
Informal Insurance in Social Networks

This paper studies informal insurance across networks of individuals. Two characteristics are fundamental to the model developed here: First, informal insurance is a bilateral activity, and rarely involves explicit arrangements across several people. Second, insurance is a social activity, and transfers are often based on norms. In the model studied here, only directly linked agents make transfers to each other, although they are aware of the (aggregate) transfers each makes to third parties. An insurance scheme for the network as a whole is an equilibrium of several interacting bilateral arrangements. This model serves as a starting point for investigating stable insurance networks, in which all agents actually have private incentives to abide by the ongoing insurance arrangement. The resulting analysis shows that network links have two distinct and possibly conflicting roles to play. First, they act as conduits for transfers, and in this way this make better insurance possible. Second, they act as conduits for information, and in this sense they affect the severity of punishments for noncompliance with the scheme. A principal task of this paper is to describe how these two forces interact, and in the process characterize stable networks. The concept of "sparse" networks, in which the removal of certain links increases the number of network components, is crucial in this characterization. As corollaries, we found that both "thickly connected" networks(such as the complete graph) and "thinly connected" networks (such as trees) are likely to be stable, whereas intermediate degrees of connectedness jeopardize stability. Finally, we study in more detail the notion of networks as conduits for transfers, by simply assuming a punishment structure (such as autarky) that is independent of the precise architecture of the tree. This allows us to isolate a bottleneck effect: the presence of certain key agents who act as bridges for several transfe (This abstract was borrowed from another version of this item.)
TOFU: Target-Orienter FUzzer

Program fuzzing---providing randomly constructed inputs to a computer program---has proved to be a powerful way to uncover bugs, find security vulnerabilities, and generate test inputs that increase code coverage. In many applications, however, one is interested in a target-oriented approach-one wants to find an input that causes the program to reach a specific target point in the program. We have created TOFU (for Target-Oriented FUzzer) to address the directed fuzzing problem. TOFU's search is biased according to a distance metric that scores each input according to how close the input's execution trace gets to the target locations. TOFU is also input-structure aware (i.e., the search makes use of a specification of a superset of the program's allowed inputs). Our experiments on xmllint show that TOFU is 28% faster than AFLGo, while reaching 45% more targets. Moreover, both distance-guided search and exploitation of knowledge of the input structure contribute significantly to TOFU's performance.
Monitoring velocity variations in the crust using earthquake doublets: An application to the Calaveras Fault, California

We present a technique that greatly improves the precision in measuring temporal variations of crustal velocities using an earthquake doublet, or pair of microearthquakes that have nearly identical waveforms and the same hypocenter and magnitude but occur on different dates. We compute differences in arrival times between seismograms recorded at the same station in the freqency domain by cross correlation of short windows of signal. A moving-window analysis of the entire seismograms, including the coda, gives δ(t), the difference in arrival times versus running time along the seismogram. The time resolution of the method is an order of magnitude better than the digitization interval. The δ(t) technique is illustrated with a pair of microearthquakes, M = 1.7 and 2.0, that occurred before and after the Coyote Lake, California, earthquake (M = 5.9) of August 6, 1979, and on the same segment of the Calaveras fault that ruptured during the earthquake. The coda wave arrivals for some stations are progressively delayed for the second earthquake in the doublet, so that its seismogram appears as a stretched version of the earlier event. We interpret this systematic variation in δ(t) along the coda as a change in the average S velocity in the upper crust in the time interval between the two doublets. S wave velocities appear to have decreased by 0.2% in an oblong region 5–10 km in radius at the south end of the aftershock zone.
Automatic assignment of biomedical categories: toward a generic approach

MOTIVATION We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent. METHODS In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units. RESULTS AND CONCLUSION Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90% for MeSH to <20% for GO, establishing a new baseline for categorizers based on retrieval methods.
Gene expression Parallelized prediction error estimation for evaluation of high-dimensional models

Summary: There is a multitude of new techniques that promise to extract predictive information in bioinformatics applications. It has been recognized that a first step for validation of the resulting model fits should rely on proper use of resampling techniques. However, this advice is frequently not followed, potential reasons being difficulty of correct implementation and computational demand. This is addressed by the R package peperr, which is designed for reliable prediction error estimation through resampling, potentially accelerated by parallel execution on a compute cluster. Its interface allows easy connection to newly developed model fitting routines. Performance evaluation of the latter is furthermore guided by diagnostic plots, which helps to detect specific problems due to high-dimensional data structures. Availability: http://cran.r-project.org, http://www.imbi. uni-freiburg.
Origins of the calculus of binary relations

The genesis of the calculus of binary relations, which was introduced by A. De Morgan (1860) and was subsequently greatly developed by C.S. Peirce (1933) and E. Schroder (1895), is examined. Its further development, from the perspective of modern model theory, in the 1940s and 1950s is described. <<ETX>>
On the Climatic Impact of Wind Stress

Abstract A climate model is used to study the climatic impact of the stress exerted on the ocean by the atmosphere. When this stress is set to zero everywhere, the climate becomes much colder, with global-mean near-surface air temperature dropping from 14.8° to 6.1°C. The largest temperature decrease occurs in high latitudes, where sea ice advances equatorward to 40° of latitude. Many of these changes are induced by the changes in the oceanic circulation. In particular, with momentum flux set to zero, the meridional transport of buoyancy in the ocean, including that fraction often associated with the buoyancy-driven circulation, essentially vanishes and, hence, so does much of the surface heat flux. Vertical transport of buoyancy in the ocean is also strongly affected. In addition, the model suggests that the flux of momentum to the ocean has a profound indirect influence on the transport of latent heat. However, the total radiative flux entering the planet at low and midlatitudes does not change much. In.
Hydrological analysis for water level projections in Taihu Lake, China

Changes in precipitation can be expected to have a major influence on the frequency and severity of flooding in the Taihu Basin. As part of an integrated assessment of flood risk in the Taihu Basin, this paper addresses the hydrological conditions and the potential impacts of climate change on Taihu Lake water levels. The hydrological system is simulated by using the distributed hydrological model VIC (Variable Infiltration Capacity), with future climate scenarios generated by the regional climate model PRECIS (Providing Regional Climates for Impacts Studies). The results indicate that maximum and mean flood water levels under the A2 and B2 scenarios for 2021–2050 will be higher than the levels during the baseline period (1961–1990), with larger increases under the B2 scenario than for the A2 scenario. The storm return periods leading to extreme water levels in the Taihu Lake in the future is projected to decrease significantly in comparison to the baseline period, implying the potential for the more frequent occurrence of major floods. These results indicate the importance of planning for future flood control in the Taihu Basin.
Mosclust: a software library for discovering significant structures in bio-molecular data

UNLABELLED The R package mosclust (model order selection for clustering problems) implements algorithms based on the concept of stability for discovering significant structures in bio-molecular data. The software library provides stability indices obtained through different data perturbations methods (resampling, random projections, noise injection), as well as statistical tests to assess the significance of multi-level structures singled out from the data. AVAILABILITY http://homes.dsi. unimi.it/~valenti/SW/mosclust/download/mosclust_1. 0.tar. gz. SUPPLEMENTARY INFORMATION http://homes.dsi. unimi.it/~valenti/SW/mosclust.
Simple Graph Coloring Algorithms for Congested Clique and Massively Parallel Computation

We present a very simple randomized partitioning procedure for graph coloring, which leads to simplification or improvements of some recent distributed and parallel coloring algorithms. In particular, we get a simple $(\Delta+1)$ coloring algorithm with round complexity $O(\log^* \Delta)$ in the CONGESTED CLIQUE model of distributed computing. This matches the bound of Parter and Su [DISC'18], which improved on the $O(\log\log \Delta \log^* \Delta)$-round algorithm of Parter [ICALP'18]. Moreover, the same random partitioning leads to a $(\Delta+1)$ coloring algorithm with round complexity $O(\log^* \Delta+ \sqrt{\log\log n})$ in the Massively Parallel Computation (MPC) model with strongly sublinear memory, which is the first sublogarithmic-time algorithm in this regime. This algorithm uses a memory of $O(n^{\alpha})$ per machine, for any desirable constant $\alpha>0$, and a total memory of $\widetilde{O}(m)$, where $m$ is the size of the graph.