Miracle-dz/UniFAR-FTUs huggingface dataset:
From Byzantine Replication to Blockchain: Consensus is Only the Beginning

The popularization of blockchains leads to a resurgence of interest in Byzantine Fault-Tolerant (BFT) state machine replication protocols. However, much of the work on this topic focuses on the underlying consensus protocols, with emphasis on their lack of scalability, leaving other subtle limitations unaddressed. These limitations are related to the effects of maintaining a durable blockchain instead of a write-ahead log and the requirement for reconfiguring the set of replicas in a decentralized way. We demonstrate these limitations using a digital coin blockchain application and BFT-SMaRt, a popular BFT replication library. We show how they can be addressed both at a conceptual level, in a protocol-agnostic way, and by implementing SMaRtChain, a blockchain platform based on BFT-SMaRt. SMaRtChain improves the performance of our digital coin application by a factor of eight when compared with a naive implementation on top of BFT-SMaRt. Moreover, SMaRtChain achieves a throughput 8x and 33x better than Tendermint and Hyperledger Fabric, respectively, when ensuring strong durability on its blockchain.
TRP: Trained Rank Pruning for Efficient Deep Neural Networks

To enable DNNs on edge devices like mobile phones, low-rank approximation has been widely adopted because of its solid theoretical rationale and efficient implementations. Several previous works attempted to directly approximate a pre-trained model by low-rank decomposition; however, small approximation errors in parameters can ripple over a large prediction loss. As a result, performance usually drops significantly and a sophisticated effort on fine-tuning is required to recover accuracy. Apparently, it is not optimal to separate low-rank approximation from training. Unlike previous works, this paper integrates low rank approximation and regularization into the training process. We propose Trained Rank Pruning (TRP), which alternates between low rank approximation and training. TRP maintains the capacity of the original network while imposing low-rank constraints during training. A nuclear regularization optimized by stochastic sub-gradient descent is utilized to further promote low rank in TRP. The TRP trained network inherently has a low-rank structure, and is approximated with negligible performance loss, thus eliminating the fine-tuning process after low rank decomposition. The proposed method is comprehensively evaluated on CIFAR-10 and ImageNet, outperforming previous compression methods using low rank approximation.
Long-term demographic fluctuations in an orchid species driven by weather: implications for conservation planning

Summary 1 Management decisions are increasingly based on matrix models intended to predict the long-term fate of endangered species. However, certain elements of these models, such as life-state transition probabilities (vital rates), are difficult to parameterize and their values may vary depending on external conditions such as weather. Details of how weather might influence population performance are rare, yet necessary to assess the effects of global climate change on a species’ distribution. 2 Based on a 26-year data set of a population of Himantoglossum hircinum in a nature reserve in Germany, variations of life-history traits and vital rates were studied. Matrix analysis was used to identify the most important life-state transitions for population growth. Multiple linear regression was used to quantify the response of population traits and vital rates to changing weather conditions. 3 Population size increased exponentially and density effects could not be observed. Flowering plants and large plants had the highest and second highest reproductive value, respectively. The population's finite rate of increase fluctuated strongly among years; life-history traits varied strongly and were interlinked, thereby violating the assumptions of matrix modelling in a population viability analysis. 4 Some vital rates and the population growth rate showed a trend over the total period. A certain and sometimes large amount of that variability could be attributed to variability of weather conditions, with warmer winter conditions favouring population performance. Prediction of population size was fairly accurate within a time frame of 10 years, but size class structure was not. 5 Synthesis and applications. Matrix modelling proved to be unreliable for predicting long-term population dynamics, despite the long-term data set used for matrix construction. This can be explained by weather-dependent variability of vital rates driving population dynamics. A minimum study period of 4 years is necessary to produce relevant information for model development. Our study emphasizes the need for critical evaluation of management decisions based only on single short-term studies and for studies covering longer time intervals than 2–3 years.
MLSUM: The Multilingual Summarization Corpus

We present MLSUM, the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, Spanish, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.
Seabirds, an identification guide

This is the definitive book on the seabirds of the world. It covers virtually all the known species, including sea ducks, loons, and grebes. Each species is beautifully illustrated in color, with different plumages, postures, and views shown to provide visual information not previously available in any book. The clearly written species descriptions include every detail needed for precise identification: plumage (adult breeding, adult nonbreeding, subadult, immature, and juvenile), flight habits and jizz, distribution and movements, similar species, subspecies, alternate names, and other important characteristics. Line drawings and identification keys amplify points made in the text and on the color plates. Color range maps show the distribution of each species throughout the world. The introduction gives a brief survey of the world's seabirds, describing in detail and illustrating the major identification characters that distinguish the different groups of seabirds.
Essays on Education and Political Economy

In this dissertation, I use the tools of applied microeconomics to study the provision of public services. The first chapter focuses on the provision of public education in Brazil. The second and third chapters study the provision of policing and education, respectively, in the United States. In the first chapter, which is joint work with Diana Moreira and Laura Trucco, we study how a change in the political party of the mayor in Brazil affects the provision of public education. We find that students in municipalities with a new party in office have significantly lower test scores. We also find that school personnel (headmaster and teacher) turnover is higher in municipalities with a new party in power, but only in schools that are controlled by the municipal government. We then provide evidence that political turnover negatively impacts student outcomes through political discretion of the municipal government over the education bureaucracy. In the second chapter, which is joint work with Frederik Schwerter, we study the relationship between fatal police use of force and trust in the police in the US. We find evidence that minority attitudes toward the police are negatively correlated with police use of force against minorities. White attitudes toward the police do not display such a relationship with respect to police use of force against whites (or minorities). We conclude that only minority attitudes toward the police respond to police use of force against their in-group; this response is targeted towards policing institutions and does not "spill over" to attitudes towards other formal or informal institutions.
Population dynamics of a cavernicolous nematode community in a chemoautotrophic groundwater system

Movile Cave harbors a completely isolated groundwater ecosystem. Although it lacks any input of photoautotrophically based food, it is inhabited by a rich invertebrate community. Chemoautotrophic microbial mats float at the surface of the thermomineral cave water, which is rich in sulfide and methane. The mats are mainly inhabited by dense populations of five bacterivorous nematode species and one predacious copepod species. As extensive investigations of these mats are hampered by the fragility and isolation of this ecosystem, we developed a model system allowing long‐term studies of their meiofaunal communities. Using the original cave water (same aquifer), we carried out experiments in a laboratory basin that allowed us to simulate the labile oxic and sulfidic conditions of Movile Cave. A few weeks after the start of the experiment, floating microbial mats developed within hypoxic enclosures and were quickly colonized by highly adapted organisms, including a meiofaunal community dominated by five nematode species (Poikilolaimus sp., Monhystrella sp., Panagrolaimus c.f. thienemanni, Udonchus tenuicaudatus, and Chronogaster troglodytes). The faunal composition of samples from the original mat material was identical. In the laboratory, peak densities of 193.2 nematodes cm−2 were reached after three months. By monitoring the nematode community structure over one year, we observed a sequential shift in the relative importance of the nematode taxa, in which those that were fast growing and r‐selected (P. c.f. thienemanni, Poikilolaimus sp.) were gradually displaced by more K‐selected species (C. troglodytes, U. tenuicaudatus). Similar oscillations were determined in samples of native mat material, apparently reflecting a cyclic succession characterized by decreasing microbial food availability.
Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?

Despite the success of language models using neural networks, it remains unclear to what extent neural models have the generalization ability to perform inferences. In this paper, we introduce a method for evaluating whether neural models can learn systematicity of monotonicity inference in natural language, namely, the regularity for performing arbitrary inferences with generalization on composition. We consider four aspects of monotonicity inferences and test whether the models can systematically interpret lexical and logical phenomena on different training/test splits. A series of experiments show that three neural models systematically draw inferences on unseen combinations of lexical and logical phenomena when the syntactic structures of the sentences are similar between the training and test sets. However, the performance of the models significantly decreases when the structures are slightly changed in the test set while retaining all vocabularies and constituents already appearing in the training set. This indicates that the generalization ability of neural models is limited to cases where the syntactic structures are nearly the same as those in the training set.
SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification

The use of offensive language is a major problem in social media which has led to an abundance of research in detecting content such as hate speech, cyberbulling, and cyber-aggression. There have been several attempts to consolidate and categorize these efforts. Recently, the OLID dataset used at SemEval-2019 proposed a hierarchical three-level annotation taxonomy which addresses different types of offensive language as well as important information such as the target of such content. The categorization provides meaningful and important information for understanding offensive language. However, the OLID dataset is limited in size, especially for some of the low-level categories, which included only a few hundred instances, thus making it challenging to train robust deep learning models. Here, we address this limitation by creating the largest available dataset for this task, SOLID. SOLID contains over nine million English tweets labeled in a semi-supervised manner. We further demonstrate experimentally that using SOLID along with OLID yields improved performance on the OLID test set for two different models, especially for the lower levels of the taxonomy. Finally, we perform analysis of the models' performance on easy and hard examples of offensive language using data annotated in a semi-supervised way.
Look at the First Sentence: Position Bias in Question Answering

Many extractive question answering models are trained to predict start and end positions of answers. The choice of predicting answers as positions is mainly due to its simplicity and effectiveness. In this study, we hypothesize that when the distribution of the answer positions is highly skewed in the training set (e.g., answers lie only in the k-th sentence of each passage), QA models predicting answers as positions learn spurious positional cues and fail to give answers in different positions. We first illustrate this position bias in popular extractive QA models such as BiDAF and BERT and thoroughly examine how position bias propagates through each layer of BERT. To safely deliver position information without position bias, we train models with various de-biasing methods including entropy regularization and bias ensembling. Among them, we found that using the prior distribution of answer positions as a bias model is very effective at reducing position bias recovering the performance of BERT from 35.24% to 81.17% when trained on a biased SQuAD dataset.
Fully-Dynamic Coresets

With input sizes becoming massive, coresets -- small yet representative summary of the input -- are relevant more than ever. A weighted set $C_w$ that is a subset of the input is an $\varepsilon$-coreset if the cost of any feasible solution $S$ with respect to $C_w$ is within $[1 {\pm} \varepsilon]$ of the cost of $S$ with respect to the original input. We give a very general technique to compute coresets in the fully-dynamic setting where input points can be added or deleted. Given a static $\varepsilon$-coreset algorithm that runs in time $t(n, \varepsilon, \lambda)$ and computes a coreset of size $s(n, \varepsilon, \lambda)$, where $n$ is the number of input points and $1 {-}\lambda$ is the success probability, we give a fully-dynamic algorithm that computes an $\varepsilon$-coreset with worst-case update time $O((\log n) \cdot t(s(n, \varepsilon/\log n, \lambda/n), \varepsilon/\log n, \lambda/n) )$ (this bound is stated informally), where the success probability is $1{-}\lambda$. Our technique is a fully-dynamic analog of the merge-and-reduce technique that applies to insertion-only setting. Although our space usage is $O(n)$, we work in the presence of an adaptive adversary, and we show that $\Omega(n)$ space is required when adversary is adaptive. As a consequence, we get fully-dynamic $\varepsilon$-coreset algorithms for $k$-median and $k$-means with worst-case update time $O(\varepsilon^{-2}k^2\log^5 n \log^3 k)$ and coreset size $O(\varepsilon^{-2}k\log n \log^2 k)$ ignoring $\log \log n$ and $\log(1/\varepsilon)$ factors and assuming that $\varepsilon, \lambda = \Omega(1/$poly$(n))$. These are the first fully-dynamic algorithms for $k$-median and $k$-means with worst-case update times $O($poly$(k, \log n, \varepsilon^{-1}))$. We also give conditional lower bound on update/query time for any fully-dynamic $(4 - \delta)$-approximation algorithm for $k$-means.
Phylogeny and evolutionary perspective of Opisthokonta protists

Lysine biosynthesis occurs in two ways: the diaminopimelate (DAP) pathway and the a-aminoadipate (AAA) pathway. The former is present in eubacteria, plants, and algae, whereas the latter was understood to be almost exclusive to fungi. The recent finding of the aaminoadipate reductase (AAR) gene, one of the core genes of the AAA pathway, in the marine protist Corallochytrium limacisporum was, therefore, believed to be a molecular synapomorphy of fungi and C. limacisporum. To test this hypothesis, we undertook a broader search for the AAR gene in eukaryotes, and also analyzed the distribution of the lysA gene, a core gene of the DAP pathway. We show that the evolutionary history of both genes, AAR and lysA, is much more complex than previously believed. Furthermore, the AAR gene is present in several unicellular opisthokonts, thus rebutting the theory that its presence is a molecular synapomorphy between C. limacisporum and fungi. AAR gene seems to be exclusive of Excavata and Unikonts, whereas the lysA gene is present in several unrelated taxa within all major eukaryotic lineages, indicating a role for several lateral gene transfer (LGT) events. Our data imply that the choanoflagellate Monosiga brevicollis and the ‘‘choanozoan’’ Capsaspora owczarzaki acquired their lysA copies from a proteobacterial ancestor. Overall, these observations represent new evidence that the role of LGT in the evolutionary history of eukaryotes may have been more significant than previously thought.
PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking

We propose the task of outline-conditioned story generation: given an outline as a set of phrases that describe key characters and events to appear in a story, the task is to generate a coherent narrative that is consistent with the provided outline. This task is challenging as the input only provides a rough sketch of the plot, and thus, models need to generate a story by weaving through the key points provided in the outline. This requires the model to keep track of the dynamic states of the latent plot, conditioning on the input outline while generating the full story. We present PlotMachines, a neural narrative model that learns to transform an outline into a coherent story by tracking the dynamic plot states. In addition, we enrich PlotMachines with high-level discourse structure so that the model can learn different styles of writing corresponding to different parts of the narrative. Comprehensive experiments over three fiction and non-fiction datasets demonstrate that recently introduced large-scale language models, such as GPT-2 and Grover, despite their impressive generation performance, are not sufficient in generating coherent narratives for the given outline, and dynamic plot state tracking is important for composing narratives with tighter, more consistent plots.
Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing

We propose the use of a sequence-to-sequence paraphraser for automatic machine translation evaluation. The paraphraser takes a human reference as input and then force-decodes and scores an MT system output. We propose training the aforementioned paraphraser as a multilingual NMT system, treating paraphrasing as a zero-shot "language pair" (e.g., Russian to Russian). We denote our paraphraser "unbiased" because the mode of our model's output probability is centered around a copy of the input sequence, which in our case represent the best case scenario where the MT system output matches a human reference. Our method is simple and intuitive, and our single model (trained in 39 languages) outperforms or statistically ties with all prior metrics on the WMT19 segment-level shared metrics task in all languages, excluding Gujarati where the model had no training data. We also explore using our model conditioned on the source instead of the reference, and find that it outperforms every quality estimation as a metric system from the WMT19 shared task on quality estimation by a statistically significant margin in every language pair.
Vertical distribution of HMXBs in NGC 55: Constraining their centre of mass velocity

Abstract We analyse the vertical distribution of High Mass X-ray Binaries (HMXBs) in NGC 55, the nearest edge-on galaxy to the Milky Way. Our analysis reveals significant spatial offsets of HMXBs from the star forming regions, greater than those observed in the SMC and the LMC but similar with the Milky Way. The spatial offsets can be explained by a momentum kick the X-ray binaries receive during the formation of the compact object. The difference between the scale height of the vertical distribution of HMXBs and the vertical distribution of star-forming activity is 0.48±0.04 kpc. The centre-of-mass velocity of the distribution of HMXBs in NGC 55 is moving at a velocity of 52.4±11.4 km s−1, greater than the corresponding velocity of HMXBs in the SMC and LMC, but consistent with velocities of Milky Way HMXBs.
Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection

Detecting the user’s intent and finding the corresponding slots among the utterance’s words are important tasks in natural language understanding. Their interconnected nature makes their joint modeling a standard part of training such models. Moreover, data scarceness and specialized vocabularies pose additional challenges. Recently, the advances in pre-trained language models, namely contextualized models such as ELMo and BERT have revolutionized the field by tapping the potential of training very large models with just a few steps of fine-tuning on a task-specific dataset. Here, we leverage such models, and we design a novel architecture on top of them. Moreover, we propose an intent pooling attention mechanism, and we reinforce the slot filling task by fusing intent distributions, word features, and token representations. The experimental results on standard datasets show that our model outperforms both the current non-BERT state of the art as well as stronger BERT-based baselines.
Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak

As the novel coronavirus spreads across the world, concerns regarding the spreading of misinformation about it are also growing. Here we estimate the prevalence of links to low-credibility information on Twitter during the outbreak, and the role of bots in spreading these links. We find that the combined volume of tweets linking to low-credibility information is comparable to the volume of New York Times articles and CDC links. Content analysis reveals a politicization of the pandemic. The majority of this content spreads via retweets. Social bots are involved in both posting and amplifying low-credibility information, although the majority of volume is generated by likely humans. Some of these accounts appear to amplify low-credibility sources in a coordinated fashion.
Independent Core Rotation in Massive Filaments in Orion

We present high-angular-resolution Atacama Large Millimeter/submillimeter Array images of N2H+ (1–0) that have been combined with those from the Nobeyama telescope toward the Orion Molecular Cloud (OMC)-2 and OMC-3 filamentary regions. The filaments (with typical widths of ∼0.1 pc) and dense cores are resolved. The measured 2D velocity gradients of cores are between 1.3 and 16.7 km s−1 pc−1, corresponding to a specific angular momentum (J/M) between 0.0012 and 0.016 pc km s−1. With respect to the core size R, the specific angular momentum follows a power law J/M ∝ R1.52±0.14. The ratio (β) between the rotational energy and gravitational energy ranges from 0.00041 to 0.094, indicating insignificant support from rotation against gravitational collapse. We further focus on the alignment between the cores’ rotational axes, which is defined to be perpendicular to the direction of the velocity gradient (θG), and the direction of elongation of filaments (θf) in this massive star-forming region. The distribution of the angle between θf and θG was found to be random, i.e., the cores’ rotational axes have no discernible correlation with the elongation of their hosting filament. This implies that, in terms of angular momentum, the cores have evolved to be dynamically independent from their natal filaments.