Miracle-dz/UniFAR-FTUs huggingface dataset:
Desirable Properties of Heterogeneous Quorum Systems

Byzantine quorum systems provide higher throughput than proof-of-work and incur modestenergy consumption. Further, their modern incarnations incorporatepersonalizedand heterogeneous trust. Thus, they are emerging as an appealing candidate for global ﬁnan-cial infrastructure. However, since their quorums are not uniform across processes anymore, the properties that they should maintain to support abstractions such as reliable broadcast and consensus are not well-understood. In this paper, we ﬁrst see a general model of heterogeneous quorum systems where each participant can declare its own quorums, and capture their properties. It has been shown that the two properties quorum intersection and availability are necessary. In this paper, we prove that they are not suf-ﬁcient. We then deﬁne the notion of quorum inclusion, and show that the three conditions together are suﬃcient: we present reliable broadcast and consensus protocols, and prove their correctness for quorum systems that provide the three properties.
C3: Compositional Counterfactual Constrastive Learning for Video-grounded Dialogues

Video-grounded dialogue systems aim to integrate video understanding and dialogue understanding to generate responses that are relevant to both the dialogue and video context. Most existing approaches employ deep learning models and have achieved remarkable performance, given the relatively small datasets available. However, the results are partly accomplished by exploiting biases in the datasets rather than developing multimodal reasoning, resulting in limited generalization. In this paper, we propose a novel approach of Compositional Counterfactual Contrastive Learning ( C 3 ) to develop contrastive training between factual and counterfactual samples in video-grounded dialogues. Speciﬁcally, we design factual/counterfactual sampling based on the temporal steps in videos and tokens in dialogues and propose contrastive loss functions that exploit object-level or action-level variance. Different from prior approaches, we focus on contrastive hidden state representations among compositional output tokens to optimize the representation space in a generation setting. We achieved promising performance gains on the Audio-Visual Scene-Aware Dialogues (AVSD) benchmark and showed the beneﬁts of our approach in grounding video and dialogue context.
FLORÍSTICA E DISTRIBUIÇÃO GEOGRÁFICA DAS SAMAMBAIAS E LICÓFITA DO ENGENHO ANIMOSO (AMARAJI, PERNAMBUCO, BRASIL)

Considerando a perda e fragmentacao da Floresta Atlântica Nordestina, a realizacao de inventarios floristicos e essencial para quaisquer acoes conservacionistas desta regiao biogeografica. Assim, o presente estudo teve como objetivo inventariar as especies de samambaias e licofitas em fragmentos florestais pertencentes ao Engenho Animoso (Amaraji, Pernambuco, Brasil), apresentando tambem informacoes relacionadas aos aspectos ecologicos, a distribuicao geografica mundial e entre os dominios fitogeograficos brasileiros, e a raridade (regional) das especies. Para o inventario floristico, foram realizadas cinco excursoes nas quais os fragmentos foram explorados dando enfase aos ambientes preferenciais de ocorrencia das samambaias e licofitas. Os processos de coleta, identificacao, herborizacao e as analises da distribuicao geografica e raridade das especies seguiram a literatura especializada. Para os quatro fragmentos florestais do Engenho Animoso, foram registradas 28 especies de samambaias e apenas uma especie de licofita, distribuidas em 21 generos e 14 familias, sendo Pteridaceae, Polypodiaceae e Thelypteridaceae, com quatro especies cada, as mais representativas. Em relacao aos aspectos ecologicos, foi observado um predominio de especies herbaceas (ca. 90%) e terricolas (ca. 65%). A analise da distribuicao geografica mundial evidenciou o predominio de especies americanas (18 spp.) seguido pelas pantropicais (sete spp.). Das especies estudadas, 23 ocorrem no dominio da Amazonia (ca. 79%), 19 no Cerrado (ca. 65%), 15 no Pantanal (ca. 52%), cinco na Caatinga (ca. 17%) e apenas uma no dominio do Pampa (ca. 3%). Apenas a especie Hecistopteris pumila (Spreng.) J. Sm. e considerada rara na Floresta Atlântica Nordestina. Os dados obtidos indicam que a area estudada detem uma riqueza razoavel de samambaias e licofita em relacao ao cenario da Floresta Atlântica Nordestina, demonstrando assim a real necessidade de efetivas acoes de conservacao e preservacao para esta.
Microtremor and Resistivity Studies for Evaluating Ground Conditions in Canakkale

SUMMARY In this study, we determined electrical and dynamic properties of surface geology by electrical resitivity at 42 stations and microtremor measurements at 46 stations. Interpretation of direct current resitivity data presents that a resistive structure (~150 ohm-m) exists between Canakkale strait and Sar cay basin, extending towards western part of the study area. The resistivity values decrease towards the eastern side. On the other hand, according to microtremor measurements, predominant periods were determined in the range of 0.15-1 sec. and the period with the value between 0.5 and 1 sec. , which points out soft alluvial layers in the middle of the Sar cay basin. Additionally, small period values were observed at the northern side, where topography is high and consist of older geological unit than alluvium, and this indicates relatively compact zones.
Seasonal characteristics of the relationship between daily precipitation intensity and surface temperature

[1] Past studies have argued that the intensity of extreme precipitation events should increase exponentially with temperature. This argument is based on the principle that the atmospheric moisture holding capacity increases according to the Clausius-Clapeyron equation and on the expectation that precipitation formation should follow accordingly. We test the latter assumption by investigating to what extent a relation with temperature can be observed intraseasonally in present-day climate. For this purpose, we use observed and simulated daily surface temperature and precipitation over Europe. In winter a general increase in precipitation intensity is indeed observed, while in summer we find a decrease in precipitation intensity with increasing temperature. We interpret these findings by making use of model results where we can distinguish separate precipitation types and investigate the moisture content in the atmosphere. In winter, the Clausius-Clapeyron relationship sets a limit to the increase in the large-scale precipitation with increasing temperature. Conversely, in summer the availability of moisture, and not the atmosphere's capacity to hold this moisture, is the dominant factor at the daily timescale. For convective precipitation, we find a peak like structure which is similar for all subregions, independent of the mean temperature, contrary to large-scale precipitation which has a more monotonic dependence on temperature.
Constraint propagation: models, techniques, implementation

This dissertation presents the design of a propagation-based constraint solver. The design is based on models that span several levels of abstraction, ranging from a mathematical foundation, to a high-level implementation architecture, to concrete data structures and algorithms. This principled design approach results in a wellunderstood, correct, modular, and efficient implementation. The core of the developed architecture is the propagation kernel. It provides the propagation infrastructure and is thus crucial for correctness and efficiency of the solver. Based on a mathematical model as well as a careful design of the employed algorithms and data structures, the presented architecture results in an efficient and domain-independent kernel. Constraints are realized by propagators, and implementing a propagator is a challenging, error-prone, and time-consuming task. A practically useful solver must however provide a comprehensive propagator library. This dissertation introduces two techniques for automatically deriving correct and efficient propagators. Views generalize variables and are used to derive propagators from existing propagators. For constraints over set variables, propagators are derived from formal constraint specifications. The presented techniques are the basis of Gecode, a production-quality, highly efficient, and widely deployed constraint solver. Gecode is the empirical evidence for success and relevance of the principled design approach of this dissertation.
Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning

Text matching is a fundamental technique in both information retrieval and natural language processing. Text matching tasks share the same paradigm that determines the relationship between two given texts. Evidently, the relationships vary from task to task, e.g. relevance in document retrieval, semantic alignment in para-phrase identification and answerable judgment in question answering. However, the essential signals for text matching remain in a finite scope, i.e. exact matching, semantic matching, and inference matching. A good text matching model can capture and aggregate these signals to achieve competitive performance across different tasks, while recent state-of-the-art neural text matching models, e.g. pre-trained language models (PLMs), are hard to do it. It is because the end-to-end supervised learning on task-specific dataset makes model overemphasize the data sample bias and task-specific signals instead of the essential matching signals, which ruins the generalization of model to different tasks. To overcome this problem, we adopt a specialization-generalization training strategy and refer to it as Match-Prompt. In specialization stage, descriptions of different matching tasks are mapped to only a few prompt to-kens. In generalization stage, text matching model explores the essential matching signals by being trained on diverse multiple matching tasks. High diverse matching tasks avoid model fitting the data sample bias on a specific task, so that model can focus on learning the essential matching signals. Meanwhile, the prompt tokens obtained in the first step are added to the corresponding tasks to help the model distinguish different task-specific matching signals. In this paper, we consider five common text matching tasks, including document retrieval, open-domain question answering, retrieval-based dialogue, paraphrase identification, and natural language inference. Experimental results on eighteen public datasets show that Match-Prompt can significantly improve multi-task generalization capability of PLMs in text matching, and yield better in-domain multi-task, out-of-domain multi-task and new task adaptation performance than task-specific model.
Partially Eager Update Propagation and Freshness-Based Read Relaxation for Replicated Internet Services

We propose an Edge-write architecture which performs eager update propagation for update requests for the corresponding secondary server, whereas it lazily propagates updates from other secondary servers. Our architecture resolves consistency problems caused by read/update decoupling in the conventional lazy update propagation-based system. It also improves overall scalability by alleviating the performance bottleneck at the primary server in compensation for increased but bounded response time. Such relaxed consistency management enables a read request to choose whether to read the replicated data immediately or to refresh it. We use the age of a local data copy as the freshness factor so that a secondary server can make a decision for freshness control independently. As a result, our freshness-controlled edge-write architecture benefits by adjusting a tradeoff between the response time and the correctness of data.
A global perspective on the Scottish Caledonides

ABSTRACT The Scottish Caledonides constitute less than 10% of the length of the Caledonian-Appalachian orogen, the rocks of which define one major margin of the Laurentian craton in Neoproterozoic-Palaeozoic times. Scotland was located, however, in a critical position at the tip of a major cratonic promontory bounded by the Caledonian and Appalachian segments of that margin. Isotopic dates from minerals and rocks collected in the Scottish Highlands have been regarded for 40 years as indicating a Neoproterozoic history of compressive orogenesis that is absent in N America and Greenland. They have therefore been taken by some authors to indicate an origin exotic to Laurentia for rocks of the Northern and Grampian Highlands S and E of the Moine thrust belt. An alternative explanation is that the Neoproterozoic rocks in the Scottish Highlands are all related to the two-stage ‘breakout’ of a discrete rift-bounded Laurentian continent from the core of the Rodinian supercontinent, believed to have assembled at the end of the Mesoproterozoic. Traditional reconstructions of the late Neoproterozoic–Early Palaeozoic Earth oppose the proto-Caledonian/Appalachian margin of Laurentia and the W African craton of the newly assembled Gondwanaland. However, consideration of the global inventory of late Precambrian rifted margins, their relation to Grenvillian orogenic belts and of scale, leads to the hypothesis that the conjugate was the proto-Andean margin of S America. Recent recognition that the Cambrian and Lower Ordovician strata of the northwestern Argentine Precordillera and their underlying Grenvillian basement are unquestionably of Laurentian derivation, while not definitive, does point in this direction. If correct, this means that even the presence of Neoproterozoic orogenesis need not imply an exotic origin, as Neoproterozoic orogens are widespread in S America. Traditional models show an Early Ordovician lapetus ocean basin approximately 4500 km wide, but the remarkably synchronous Ordovician collision of arcs and other terranes with the Laurentian and Gondwanan cratons from Argentina to the British Isles, suggests that this premise may be incorrect. The Appalachian–Caledonian orogen may rather have resulted from close and complex tectonic interaction between Laurentia and Gondwana, involving intervening volcanic arcs and other terranes. The interaction may have taken place during a clockwise transit of Laurentia around the proto-Andean margin to its late Caledonian–Scandian collision with Baltica, and the final suturing of Pangaea at the close of the Palaeozoic era. A modern analogue may be the interaction between Australia and Asia, involving intervening volcanic arcs and other terranes of the western Pacific Ocean basin, from ~ 50 Ma through the Present, and into the future.
Two-dimensional detection of underground contamination and buried objects using cross-well radar

Traditional approaches for locating and characterizing contaminated sites rely on invasive techniques which require drilling, testing, and sampling. These techniques provide the most direct access to the subsurface, but they are generally expensive and only provide measurements at points in a three dimensional surface. Furthermore, invasive techniques in polluted areas can promote further spread of contaminants. Development of non-invasive techniques that offer rapid and relatively inexpensive characterization is, therefore, necessary to detect and monitor plumes and sources of contaminants. Non-invasive techniques are also required for locating buried objects, such as landmines and unexploded ordnances. The use of cross well radar (CWR) as a non-invasive technique that has proven to be a reliable technology for detection of target objects that exhibit significant contrast of dielectric properties in saturated soils. Its application to detection of heterogeneously distributed phases in unsaturated soils under variable flow conditions has yet to be developed. This paper addresses the development of 2D flow and electromagnetic (EM) soilBed setup to further assess and enhance CWR technology for the detection of Dense Non-Aqueous Phase Liquids (DNAPLs) contamination and other target elements in variably-saturated soils subjected to transient flow conditions. Loop antennas have been developed and tested for this purpose. Transmission and reflection measurements are evaluated to determine the antenna's reliability and optimize their performance in the 2D electromagnetic field. The measurements indicate that a 2D EM boundary condition may be imposed by placing two parallel perfectly-reflecting metal plates along one of the dimensions of the soilBed setup. Transmission and reflection characteristics of the antennas vary with their method of construction. Results show a reliable and reproducible response from the loop antennas, but suggest some wave leakage and indicate that their design must be optimized. Measured variations in the transmission, reflection and impedance in the presence and absence of a buried object suggest that the 2D EM soilBed setup using loop antennas can be aplied to detect target elements in subsurface environments subjected to flow conditions. Future work addresses the assessment of CWR technology as a non-invasive method for detection and monitoring of heterogeneously-distributed target objects in subsurface environments.
AI-driven Hypernetwork of Organic Chemistry: Network Statistics and Applications in Reaction Classification

Rapid discovery of new reactions and molecules in recent years has been facilitated by the advancements in high throughput screening, accessibility to a much more complex chemical design space, and the development of accurate molecular modeling frameworks. A holistic study of the growing chemistry literature is, therefore, required that focuses on understanding the recent trends and extrapolating them into possible future trajectories. To this end, several network theory-based studies have been reported that use a directed graph representation of chemical reactions. Here, we perform a study based on representing chemical reactions as hypergraphs where the hyperedges represent chemical reactions and nodes represent the participating molecules. We use a standard reactions dataset to construct a hypernetwork and report its statistics such as degree distributions, average path length, assortativity or degree correlations, PageRank centrality, and graph-based clusters (or communities). We also compute each statistic for an equivalent directed graph representation of reactions to draw parallels and highlight differences between the two. To demonstrate the AI applicability of hypergraph reaction representation, we generate dense hypergraph embeddings and use them in the reaction classiﬁcation problem. We conclude that the hypernetwork representation is ﬂexible, preserves reaction context, and uncovers hidden insights that are otherwise not apparent in a traditional directed graph representation of chemical reactions.
Burling graphs revisited - Part 1 New characterizations

The Burling sequence is a sequence of triangle-free graphs of increasing chromatic number. Each of them is isomorphic to the intersection graph of a set of axis-parallel boxes in R. These graphs were also proved to have other geometrical representations: intersection graphs of line segments in the plane, and intersection graphs of frames, where a frame is the boundary of an axis-aligned rectangle in the plane. We call Burling graph every graph that is an induced subgraph of some graph in the Burling sequence. We give five new equivalent ways to define Burling graphs. Three of them are geometrical, one is of a more graph-theoretical flavour and one is more axiomatic.
LH*RS – A Highly-Available Scalable Distributed Data Structure

LH*RS is a high-availability scalable distributed data structure (SDDS). An LH*RS file is hash partitioned over the distributed RAM of a multicomputer, e.g., a network of PCs, and supports the unavailability of any of its k ≥ 1 server nodes. The value of k transparently grows with the file to offset the reliability decline. Only the number of the storage nodes potentially limits the file growth. The high-availability management uses a novel parity calculus that we have developed, based on the Reed-Salomon erasure correcting coding. The resulting parity storage overhead is about the minimal ever possible. The parity encoding and decoding are faster than for any other candidate coding we are aware of. We present our scheme and its performance analysis, including experiments with a prototype implementation on Wintel PCs. The capabilities of LH*RS offer new perspectives to data intensive applications, including the emerging ones of grids and of P2P computing.
Universal Flying Objects: Modular Multirotor System for Flight of Rigid Objects

In this article, we introduce Universal Flying Objects—a modular aerial robotic platform for transforming a rigid object into a multirotor robot. To achieve this, we develop flight modules, in the form of a control module and propelling modules that can be affixed to an object. The object, or payload, serves as the airframe of the vehicle. The modular design produces a highly versatile platform as it is reconfigurable by the addition or removal of flight modules, adjustment of the modules’ arrangement, or change of payloads. To facilitate the flight control, we propose an inertial measurement unit (IMU)-based estimation strategy for rapid computation of the robot's configuration. When combined with the adaptive geometric controller for further refinement of uncertain parameters, stable flights are accomplished with minimal manual intervention or tuning required by a user. To this end, we demonstrate hovering and trajectory tracking flights through various robot configurations with different dummy payloads, weighing $\approx$200–800 g, using four to eight propelling modules. The results reveal that stable flights are attainable thanks to the proposed IMU-based estimation method. The flight performance is markedly improved over time through the adaptive scheme, with position errors of a few centimeters after the parameter convergence.
Neural Interpretation of Generic Source Code

Can a generic (Python) program be executed statement-by-statement by neural networks composed according to the source code? We formulate the Abstract Neural Execution Problem and introduce Neural Interpretation, the ﬁrst neural model that abstractly executes generic source code, where every variable has a vector encoding, and every function executes a neural network. Neural Interpretation is a model of computers with a compiler architecture, which can assemble neural layers “programmed” by partial source code. Neural Interpretation can be trained with ﬂexible learning objectives. We demonstrate white-box execution without concrete inputs for variable misuse localization and repair.
How to Own the Internet in Your Spare Time

The ability of attackers to rapidly gain control of vast numbers of Internet hosts poses an immense risk to the overall security of the Internet. Once subverted, these hosts can not only be used to launch massive denial of service floods, but also to steal or corrupt great quantities of sensitive information, and confuse and disrupt use of the network in more subtle ways. We present an analysis of the magnitude of the threat. We begin with a mathematical model derived from empirical data of the spread of Code Red I in July, 2001. We discuss techniques subsequently employed for achieving greater virulence by Code Red II and Nimda. In this context, we develop and evaluate several new, highly virulent possible techniques: hit-list scanning (which creates a Warhol worm), permutation scanning (which enables self-coordinating scanning), and use of Internet-sized hit-lists (which creates a flash worm).