Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoder-decoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.
Multilingual Topic Models for Unaligned Text

We develop the multilingual topic model for unaligned text (MuTo), a probabilistic model of text that is designed to analyze corpora composed of documents in two languages. From these documents, MuTo uses stochastic EM to simultaneously discover both a matching between the languages and multilingual latent topics. We demonstrate that MuTo is able to find shared topics on real-world multilingual corpora, successfully pairing related documents across languages. MuTo provides a new framework for creating multilingual topic models without needing carefully curated parallel corpora and allows applications built using the topic model formalism to be applied to a much wider class of corpora.
Peripheral Developing Odontoma or Peripheral Ameloblastic Fibro-Odontoma Erupting to Oral Cavity Case Report

The appearance of mixed odontogenic tumors into the oral cavity is a rare event. It is considered that some mixed tumors are only a stage in the complete development of a hamartomatous formation such as ameloblastic fibroodontoma and odontoma. Both pathologies share in common cellular elements which at one point makes them indistinguishable from each other. We present the case of a 21 year old patient who showed a mandibular growth whose histological elements present characteristics of both pathologies. The treatment was surgical excision of the lesion. There were no complications or recurrences to periodic reevaluation.
L'ilot génomique pks chez Escherichia coli : structure-fonction de la protéine ClbP et études épidémiologiques

L’ilot genomique pks de Escherichia coli et d’autres Enterobacteriaceae code des synthases depolycetides et de peptides non ribosomaux qui permettent l’assemblage d’un compose hybride polycetidepeptidenon ribosomal putative. Ce compose nomme colibactine induit des cassures double-brin de l’ADN descellules eucaryotes.La machinerie enzymatique codee par l’ilot pks comporte une proteine essentielle ClbP, atypique dansce type d’ilot. Nous avons montre que ClbP possede une partie N-terminale catalytique et periplasmique, et unepartie C-terminale associee a la membrane cytoplasmique. La structure cristalline de ClbP et des experiences demutagenese ont revele un site actif a serine et des caracteristiques structurales originales, qui sont compatiblesavec une activite peptidase, confirmee par des analyses biochimiques. Dix homologues de ClbP ont ete identifiesin silico dans des ilots genomiques de synthases de peptides non ribosomaux d’especes bacteriennes proches eteloignees. Tous les homologues testes ont presente une promiscuite fonctionnelle avec ClbP. ClbP est donc leprototype d’une nouvelle sous-famille de peptidases, qui sont probablement impliquees dans la maturation decomposes peptidiques non ribosomaux.Par ailleurs, nous avons realise deux etudes epidemiologiques sur la prevalence de l’ilot pks dansl’espece E. coli dans deux contextes physiopathologiques, l’urosepsis et les cancers coliques et rectaux. L’ilotpks etait significativement associe aux souches issues d’urosepsis compare a des souches commensales, et auxsouches issues de biopsies de tumeurs coliques compare a des souches commensales ou issues de biopsies detumeurs rectales, de diverticuloses et de lesions ileales de maladie de Crohn.
Effect of HDL level on thrombus resolution in pulmonary embolism

Pulmonary embolism (PE) is a major cause of death during admissions to the emergency department. Besides mortality and morbidity that occur in the acute phase, the second problem faced by patients with PE is chronic thromboembolic pulmonary hypertension (CTEPH), which is found to be more frequent than what was formerly believed. A recent study showed that the incidence of CTEPH was 3.8% within 2 years after the acute attack (1). Another study showed that the 3-year mortality was 90% in patients with the average pulmonary arterial pressure of more than 30 mm Hg (2).
Modeling Images using Transformed Indian Buffet Processes

Latent feature models are attractive for image modeling, since images generally contain multiple objects. However, many latent feature models ignore that objects can appear at different locations or require pre-segmentation of images. While the transformed Indian buffet process (tIBP) provides a method for modeling transformation-invariant features in unsegmented binary images, its current form is inappropriate for real images because of its computational cost and modeling assumptions. We combine the tIBP with likelihoods appropriate for real images and develop an efficient inference, using the cross-correlation between images and features, that is theoretically and empirically faster than existing inference techniques. Our method discovers reasonable components and achieve effective image reconstruction in natural images.
Applying Machine Learning Techniques to Analyze the Pedestrian Applying Machine Learning Techniques to Analyze the Pedestrian and Bicycle Crashes at the Macroscopic Level and Bicycle Crashes at the Macroscopic Level

This thesis presents different data mining/machine learning techniques to analyze the vulnerable road users’ (i.e., pedestrian and bicycle) crashes by developing crash prediction models at macro-level. In this study, we developed data mining approach (i.e., decision tree regression (DTR) models) for both pedestrian and bicycle crash counts. To author knowledge, this is the first application of DTR models in the growing traffic safety literature at macro-level. The empirical analysis is based on the Statewide Traffic Analysis Zones (STAZ) level crash count data for both pedestrian and bicycle from the state of Florida for the year of 2010 to 2012. The model results highlight the most significant predictor variables for pedestrian and bicycle crash count in terms of three broad categories: traffic, roadway, and socio demographic characteristics. Furthermore, spatial predictor variables of neighboring STAZ were utilized along with the targeted STAZ variables in order to improve the prediction accuracy of both DTR models. The DTR model considering spatial predictor variables (spatial DTR model) were compared without considering spatial predictor variables (aspatial DTR model) and the models comparison results clearly found that spatial DTR model is superior model compared to aspatial DTR model in terms of prediction accuracy. Finally, this study contributed to the safety literature by applying three ensemble techniques (Bagging, Random Forest, and Boosting) in order to improve the prediction accuracy of weak learner (DTR models) for macro-level crash count. The model’s estimation result revealed that all the ensemble technique performed better than the DTR model and the gradient boosting technique outperformed other competing ensemble technique in macro-level crash prediction model.
Strong Secrecy for Cooperative Broadcast Channels

A broadcast channel (BC) where the decoders cooperate via a one-sided link is considered. One common and two private messages are transmitted and the private message to the cooperative user should be kept secret from the cooperation-aided user. The secrecy level is measured in terms of strong secrecy, i.e., a vanishing information leakage. An inner bound on the capacity region is derived by using a channel-resolvability-based code that double-bins the codebook of the secret message, and by using a likelihood encoder to choose the transmitted codeword. The inner bound is shown to be tight for semi-deterministic and physically degraded BCs, and the results are compared with those of the corresponding BCs without a secrecy constraint. Blackwell and Gaussian BC examples illustrate the impact of secrecy on the rate regions. Unlike the case without secrecy, where sharing information about both private messages via the cooperative link is optimal, our protocol conveys parts of the common and non-confidential messages only. This restriction reduces the transmission rates more than the usual rate loss due to secrecy requirements. An example that illustrates this loss is provided.
Enhanced optimization with composite objectives and novelty pulsation

An important benefit of multi-objective search is that it maintains a diverse population of candidates, which helps in deceptive problems in particular. Not all diversity is useful, however: candidates that optimize only one objective while ignoring others are rarely helpful. A recent solution is to replace the original objectives by their linear combinations, thus focusing the search on the most useful tradeoffs between objectives. To compensate for the loss of diversity, this transformation is accompanied by a selection mechanism that favors novelty. This paper improves this approach further by introducing novelty pulsation, i.e. a systematic method to alternate between novelty selection and local optimization. In the highly deceptive problem of discovering minimal sorting networks, it finds state-of-the-art solutions significantly faster than before. In fact, our method so far has established a new world record for the 20-lines sorting network with 91comparators. In the real-world problem of stock trading, it discovers solutions that generalize significantly better on unseen data. Composite Novelty Pulsation is therefore a promising approach to solving deceptive real-world problems through multi-objective optimization.
The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives

Visual narrative is often a combination of explicit information and judicious omissions, relying on the viewer to supply missing details. In comics, most movements in time and space are hidden in the gutters between panels. To follow the story, readers logically connect panels together by inferring unseen actions through a process called closure. While computers can now describe the content of natural images, in this paper we examine whether they can understand the closure-driven narratives conveyed by stylized artwork and dialogue in comic book panels. We collect a dataset, COMICS, that consists of over 1.2 million panels (120 GB) paired with automatic textbox transcriptions. An in-depth analysis of COMICS demonstrates that neither text nor image alone can tell a comic book story, so a computer must understand both modalities to keep up with the plot. We introduce three cloze-style tasks that ask models to predict narrative and character-centric aspects of a panel given n preceding panels as context. Various deep neural architectures underperform human baselines on these tasks, suggesting that COMICS contains fundamental challenges for both vision and language.
Lifetime-Based Memory Management for Distributed Data Processing Systems

In-memory caching of intermediate data and eager combining of data in shuffle buffers have been shown to be very effective in minimizing the re-computation and I/O cost in distributed data processing systems like Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap, which may quickly saturate the garbage collector, especially when handling a large dataset, and hence would limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects, and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca, a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. An extensive experimental study using both synthetic and real datasets shows that, in comparing to Spark, Deca is able to 1) reduce the garbage collection time by up to 99.9%, 2) to achieve up to 22.7x speed up in terms of execution time in cases without data spilling and 41.6x speedup in cases with data spilling, and 3) to consume up to 46.6% less memory.
The interplay between genome and network evolution in eukaryotes

We assume that the functional relations that a protein engages in, influences it’s evolutionary dynamics. An extreme case is a functional module in which all components are strongly dependent on each other, for example a protein complex or metabolic pathway. If a protein’s function is only relevant on the level of the module, selection will act on module level as well. This leads to distinct phylogenetic patterns: either the module is completely present or completely absent. In other words, the functional module is also an evolutionary module. Surprisingly, we find that most functional modules do not behave like evolutionary modules. In order to remove any biases resulting from erroneous module definitions, we filter our functional modules using high-throughput protein interaction data as well as by cross-comparing different module datasets. We observe only a minor increase in evolutionary modularity. Without strong module-level selection, how is the modular structure of the cellular organization maintained? To answer this question, we represent the molecular machinery as a network of interacting proteins and infer how this network may have evolved. We use an existing model in which networks grow through gene duplication followed by subfunctionalization, to simulate network evolution. Networks that are produced by this model, have a modular structure that is similar to that of real protein interaction networks. We increase the biological relevance of this model by incorporating gene loss. We find that in this extended model, modularity is more easily established and maintained. Importantly, we find that if duplication of existing interactions is the only source of new connections, networks tend to break up. This strongly suggests that gain of completely novel interactions is pivotal in network evolution. In addition to comparing purely topological characteristics of model networks with those of real large-scale protein interaction networks, we study how proteins of different ages are connected in different networks. Previous studies report that protein tend to interact with priteins of a similar age and we demonstrate that this can be partially explained by the process of gene duplcation followed by subfunctionalization. In conclusion, we find that the organization of proteins in specific modules is not conserved in evolution: functional modules do not necessarily behave as evolutionary modules. Moreover, we find that functional modules can spontaneously arise from basic, local evolutionary processes, such as gene duplication.
Self-supervised learning of predictive segmentation models from video

The ability to anticipate future events is a key component of intelligence. Video prediction has been studied in recent years as a means to provide machines with this ability. Predictive models of the environment hold promise for allowing the transfer of recent reinforcement learning successes to many real-world contexts, by decreasing the number of interactions needed with the real world. Video prediction has been studied in recent years as a particular case of such predictive models, with broad applications in robotics and navigation systems. While RGB frames are easy to acquire and hold a lot of information, they are extremely challenging to predict, and cannot be directly interpreted by downstream applications. Here we introduce the novel tasks of predicting semantic and instance segmentation of future frames. The abstract feature spaces we consider are better suited for recursive prediction and allow us to develop models which convincingly predict segmentations up to half a second into the future. Predictions are more easily interpretable by downstream algorithms and remain rich, spatially detailed and easy to obtain, relying on state-of-the-art segmentation methods. We first focus on the task of semantic segmentation, for which we propose a discriminative approach based on adversarial training. Then, we introduce the novel task of predicting future semantic segmentation, and develop an autoregressive convolutional neural network to address it. Finally, we extend our method to the more challenging problem of predicting future instance segmentation, which additionally segments out individual objects. To deal with a varying number of output labels per image, we develop a predictive model in the space of high-level convolutional image features of the Mask R-CNN instance segmentation model. We are able to produce visually pleasing segmentations at a high resolution for complex scenes involving a large number of instances, and with convincing accuracy up to half a second ahead.
Benchmarking Distributed Stream Data Processing Systems

The need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) with highly diverging capabilities and performance characteristics. While first initiatives try to compare the systems for simple workloads, there is a clear gap of detailed analyses of the systems' performance characteristics. In this paper, we propose a framework for benchmarking distributed stream processing engines. We use our suite to evaluate the performance of three widely used SDPSs in detail, namely Apache Storm, Apache Spark, and Apache Flink. Our evaluation focuses in particular on measuring the throughput and latency of windowed operations, which are the basic type of operations in stream analytics. For this benchmark, we design workloads based on real-life, industrial use-cases inspired by the online gaming industry. The contribution of our work is threefold. First, we give a definition of latency and throughput for stateful operators. Second, we carefully separate the system under test and driver, in order to correctly represent the open world model of typical stream processing deployments and can, therefore, measure system performance under realistic conditions. Third, we build the first benchmarking framework to define and test the sustainable performance of streaming systems. Our detailed evaluation highlights the individual characteristics and use-cases of each system.
uFLIP: Understanding Flash IO Patterns

Does the advent of flash devices constitute a radical change for secondary storage? How should database systems adapt to this new form of secondary storage? Before we can answer these questions, we need to fully understand the performance characteristics of flash devices. More specifically, we want to establish what kind of IOs should be favored (or avoided) when designing algorithms and architectures for flash-based systems. In this paper, we focus on flash IO patterns, that capture relevant distribution of IOs in time and space, and our goal is to quantify their performance. We define uFLIP, a benchmark for measuring the response time of flash IO patterns. We also present a benchmarking methodology which takes into account the particular characteristics of flash devices. Finally, we present the results obtained by measuring eleven flash devices, and derive a set of design hints that should drive the development of flash-based systems on current devices.
Origem do Homo sapiens e sua chegada às Américas : uma contribuição da antropologia molecular

Since the end of the 80s, the study of DNA markers has contributed enormously to a better understanding of anthropological questions. Currently, two issues have been hotly debated: the first involves a model for the origin of modern humans, and the possible assimilation of archaic lineages by Homo sapiens populations which had left Africa. The second involves the time and mode of the peopling of the Americas, the last continent colonized by humans. A study involving the sequencing of 50 autosomal loci was performed in a sample of 12 American Indians, totaling about 50,000 base pairs per individual. These data were analyzed in conjunction with previously published sequences from African and Asian individuals. Using approximate Bayesian computation and coalescent simulations, the relative probability of three models of human evolution was assessed for the first time. A model of African origin with replacement received the highest relative probability (98%), much higher than that associated to assimilation or multiregional models. The favored evolutionary scenario can explain not only the recent ancestry for mitochondrial DNA and the Y chromosome, but also the existence of deep lineages in autosomal loci. Regarding the peopling of the Americas, the data indicate the lack of a bottleneck effect and suggest a recent time for its settlement, up to 13,350 years ago. To obtain a more accurate scenario for the peopling of the Americas, 53 mitochondrial genomes from Native American individuals belonging to the five main mitochondrial haplogroups (A-D, X) were sequenced and were analyzed together with 191 genomes available in public databases. The analysis indicated a peopling of the continent after the last glacial maximum at approximately 18,000 years ago, associated to a strong population expansion (100 fold), and with an entry in the continent through the Pacific coast. Both datasets strongly suggest the nonexistence of a marked founder effect during the settlement of the Americas and the peopling of the continent after the last glacial maximum. The discrepancies between the two calculated times for the entry in the continent could reflect the effects of a secondary migration, but the simplest explanation would be related to factors such as sampling design and to uncertainties intrinsic to some of the parameters.