IBM Research - Brazil

Black phosphorus is a layered semiconductor that is intensely researched in view of applications in optoelectronics. In this letter, we investigate a multilayer black phosphorus photodetector that is capable of acquiring high-contrast (V > 0.9) images both in the visible (λVIS = 532 nm) as well as in the infrared (λIR = 1550 nm) spectral regime. In a first step, by using photocurrent microscopy, we map the active area of the device and we characterize responsivity and gain. In a second step, by deploying the black phosphorus device as a point-like detector in a confocal microsope setup, we acquire diffraction-limited optical images with submicron resolution. The results demonstrate the usefulness of black phosphorus as an optoelectronic material for hyperspectral imaging applications.

Distributed word representations have recently been proven to be an invaluable resource for NLP. These representations are normally learned using neural networks and capture syntactic and semantic information about words. Informa-tion about word morphology and shape is nor-mally ignored when learning word representa-tions. However, for tasks like part-of-speech tag-ging, intra-word information is extremely use-ful, specially when dealing with morphologically rich languages. In this paper, we propose a deep neural network that learns character-level repre-sentation of words and associate them with usual word representations to perform POS tagging. Using the proposed approach, while avoiding the use of any handcrafted feature, we produce state-of-the-art POS taggers for two languages: En-glish, with 97.32 % accuracy on the Penn Tree-bank WSJ corpus; and Portuguese, with 97.47% accuracy on the Mac-Morpho corpus, where the latter represents an error reduction of 12.2 % on the best previous known result. 1.

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.

The great amounts of energy consumed by large-scale computing and network systems, such as data centers and supercomputers, have been a major source of concern in a society increasingly reliant on information technology. Trying to tackle this issue, the research community and industry have proposed myriad techniques to curb the energy consumed by IT systems. This article surveys techniques and solutions that aim to improve the energy efficiency of computing and network resources. It discusses methods to evaluate and model the energy consumed by these resources, and describes techniques that operate at a distributed system level, trying to improve aspects such as resource allocation, scheduling, and network traffic management. This work aims to review the state of the art on energy efficiency and to foster research on schemes to make network and computing resources more efficient.

Breast cancer (BC) is a deadly disease, killing millions of people every year. Developing automated malignant BC detection system applied on patient's imagery can help dealing with this problem more efficiently, making diagnosis more scalable and less prone to errors. Not less importantly, such kind of research can be extended to other types of cancer, making even more impact to help saving lives. Recent results on BC recognition show that Convolution Neural Networks (CNN) can achieve higher recognition rates than hand-crafted feature descriptors, but the price to pay is an increase in complexity to develop the system, requiring longer training time and specific expertise to fine-tune the architecture of the CNN. DeCAF (or deep) features consist of an in-between solution it is based on reusing a previously trained CNN only as feature vectors, which is then used as input for a classifier trained only for the new classification task. In the light of this, we present an evaluation of DeCaf features for BC recognition, in order to better understand how they compare to the other approaches. The experimental evaluation shows that these features can be a viable alternative to fast development of high-accuracy BC recognition systems, generally achieving better results than traditional hand-crafted textural descriptors and outperforming task-specific CNNs in some cases.

WhatsApp has revolutionized the way people communicate and interact. It is not only cheaper than the traditional Short Message Service (SMS) communication but it also brings a new form of mobile communication: the group chats. Such groups are great forums for collective discussions on a variety of topics. In particular, in events of great social mobilization, such as strikes and electoral campaigns, WhatsApp group chats are very attractive as they facilitate information exchange among interested people. Yet, recent events have raised concerns about the spreading of misinformation in WhatsApp. In this work, we analyze information dissemination within WhatsApp, focusing on publicly accessible political-oriented groups, collecting all shared messages during major social events in Brazil: a national truck drivers' strike and the Brazilian presidential campaign. We analyze the types of content shared within such groups as well as the network structures that emerge from user interactions within and cross-groups. We then deepen our analysis by identifying the presence of misinformation among the shared images using labels provided by journalists and by a proposed automatic procedure based on Google searches. We identify the most important sources of the fake images and analyze how they propagate across WhatsApp groups and from/to other Web platforms.

UNLABELLED: Although aberrant DNA methylation patterning is a hallmark of cancer, the relevance of targeting DNA methyltransferases (DNMT) remains unclear for most tumors. In diffuse large B-cell lymphoma (DLBCL) we observed that chemoresistance is associated with aberrant DNA methylation programming. Prolonged exposure to low-dose DNMT inhibitors (DNMTI) reprogrammed chemoresistant cells to become doxorubicin sensitive without major toxicity in vivo. Nine genes were recurrently hypermethylated in chemoresistant DLBCL. Of these, SMAD1 was a critical contributor, and reactivation was required for chemosensitization. A phase I clinical study was conducted evaluating azacitidine priming followed by standard chemoimmunotherapy in high-risk patients newly diagnosed with DLBCL. The combination was well tolerated and yielded a high rate of complete remission. Pre- and post-azacitidine treatment biopsies confirmed SMAD1 demethylation and chemosensitization, delineating a personalized strategy for the clinical use of DNMTIs. SIGNIFICANCE: The problem of chemoresistant DLBCL remains the most urgent challenge in the clinical management of patients with this disease. We describe a mechanism-based approach toward the rational translation of DNMTIs for the treatment of high-risk DLBCL.

Experimental measurements of the photoresponse in doped multilayer black phosphorus phototransistors show that the photocurrent generation is dominated by thermoelectric and bolometric processes, not by the photovoltaic effect.

century in the Americas began in 2016, with intense circulation in the southeastern states of Brazil, particularly in sylvatic environments near densely populated areas including the metropolitan region of São Paulo city (MRSP) during 2017-2018. Herein, we describe the origin and molecular epidemiology of yellow fever virus (YFV) during this outbreak inferred from 36 full genome sequences taken from individuals who died following infection with zoonotic YFV. Our analysis revealed that these deaths were due to three genetic variants of sylvatic YFV that belong the South American I genotype and that were related to viruses previously isolated in 2017 from other locations in Brazil (Minas Gerais, Espírito Santo, Bahia and Rio de Janeiro states). Each variant represented an independent virus introduction into the MRSP. Phylogeographic and geopositioning analyses suggested that the virus moved around the peri-urban area without detectable human-to-human transmission, and towards the Atlantic rain forest causing human spill-over in nearby cities, yet in the absence of sustained viral transmission in the urban environment.

OBJECTIVES: This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances. METHODS: Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth. RESULTS: Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes. CONCLUSION: Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

Many studies point to the difficulty of scaling existing computer architectures to meet the needs of an exascale system (i.e., capable of executing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="TeX">$10^{18} $</tex-math></inline-formula> floating-point operations per second), consuming no more than 20 MW in power, by around the year 2020. This paper outlines a new architecture, the Active Memory Cube, which reduces the energy of computation significantly by performing computation in the memory module, rather than moving data through large memory hierarchies to the processor core. The architecture leverages a commercially demonstrated 3D memory stack called the Hybrid Memory Cube, placing sophisticated computational elements on the logic layer below its stack of dynamic random-access memory (DRAM) dies. The paper also describes an Active Memory Cube tuned to the requirements of a scientific exascale system. The computational elements have a vector architecture and are capable of performing a comprehensive set of floating-point and integer instructions, predicated operations, and gather-scatter accesses across memory in the Cube. The paper outlines the software infrastructure used to develop applications and to evaluate the architecture, and describes results of experiments on application kernels, along with performance and power projections.

The archives of Flora Medicinal, an ancient pharmaceutical laboratory that supported ethnomedical research in Brazil for more than 30 years, were searched for plants with antimalarial use. Forty plant species indicated to treat malaria were described by Dr. J. Monteiro da Silva (Flora Medicinal leader) and his co-workers. Eight species, Bathysa cuspidata, Cosmos sulphureus, Cecropia hololeuca, Erisma calcaratum, Gomphrena arborescens, Musa paradisiaca, Ocotea odorifera, and Pradosia lactescens, are related as antimalarial for the first time in ethnobotanical studies. Some species, including Mikania glomerata, Melampodium divaricatum, Galipea multiflora, Aspidosperma polyneuron, and Coutarea hexandra, were reported to have activity in malaria patients under clinical observation. In the information obtained, also, there were many details about the appropriate indication of each plant. For example, some plants are indicated to increase others' potency. There are also plants that are traditionally employed for specific symptoms or conditions that often accompany malaria, such as weakness, renal failure or cerebral malaria. Many plants that have been considered to lack activity against malaria due to absence of in vitro activity against Plasmodium can have other mechanisms of action. Thus researchers should observe ethnomedical information before deciding which kind of screening should be used in the search of antimalarial drugs.

Mapping tropical tree species at landscape scales to provide information for ecologists and forest managers is a new challenge for the remote sensing community. For this purpose, detection and delineation of individual tree crowns (ITCs) is a prerequisite. Here, we present a new method of automatic tree crown delineation based only on very high resolution images from WorldView-2 satellite and apply it to a region of the Atlantic rain forest with highly heterogeneous tropical canopy cover – the Santa Genebra forest reserve in Brazil. The method works in successive steps that involve pre-processing, selection of forested pixels, enhancement of borders, detection of pixels in the crown borders, correction of shade in large trees and, finally, segmentation of the tree crowns. Principally, the method uses four techniques: rolling ball algorithm and mathematical morphological operations to enhance the crown borders and ease the extraction of tree crowns; bimodal distribution parameters estimations to identify the shaded pixels in the gaps, borders, and crowns; and focal statistics for the analysis of neighbouring pixels. Crown detection is validated by comparing the delineated ITCs with a sample of ITCs delineated manually by visual interpretation. In addition, to test if the spectra of individual species are conserved in the automatic delineated crowns, we compare the accuracy of species prediction with automatic and manual delineated crowns with known species. We find that our method permits detection of up to 80% of ITCs. The seven species with over 10 crowns identified in the field were mapped with reasonable accuracy (30.5–96%) given that only WorldView-2 bands and texture features were used. Similar classification accuracies were obtained using both automatic and manual delineation, thereby confirming that species’ spectral responses are preserved in the automatic method and thus permitting the recognition of species at the landscape scale. Our method might support tropical forest applications, such as mapping species and canopy characteristics at the landscape scale.

Having dense and regularly sampled data is becoming increasingly important in seismic processing. However, due to physical or financial constraints, seismic data sets can be often undersampled. Occasionally, these data sets may also present bad or dead traces the geoscientist must deal with. Many works have tackled this problem using prestack data and can be classified in three main categories: wave-equation, domain transform, and prediction-error-filter methods. In this letter, we assess the performance of a conditional generative adversarial network for the interpolation problem in poststack seismic data sets. To the best of our knowledge, this is the first work to evaluate a deep learning approach in this context. Quantitative and qualitative evaluations of our experiments indicate that deep networks may present an interesting alternative to classical methods.

High performance computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show that hybrid environments are the natural path to get the best of the on-premise and cloud resources—steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This article brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.

Forest species recognition has been traditionally addressed as a texture classification problem, and explored using standard texture methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ) and Gabor Filters. Deep learning techniques have been a recent focus of research for classification problems, with state-of-the art results for object recognition and other tasks, but are not yet widely used for texture problems. This paper investigates the usage of deep learning techniques, in particular Convolutional Neural Networks (CNN), for texture classification in two forest species datasets - one with macroscopic images and another with microscopic images. Given the higher resolution images of these problems, we present a method that is able to cope with the high-resolution texture images so as to achieve high accuracy and avoid the burden of training and defining an architecture with a large number of free parameters. On the first dataset, the proposed CNN-based method achieves 95.77% of accuracy, compared to state-of-the-art of 97.77%. On the dataset of microscopic images, it achieves 97.32%, beating the best published result of 93.2%.

SUMMARY Cloud computing allows the deployment and delivery of application services for users worldwide. Software as a Service providers with limited upfront budget can take advantage of Cloud computing and lease the required capacity in a pay‐as‐you‐go basis, which also enables flexible and dynamic resource allocation according to service demand. One key challenge potential Cloud customers have before renting resources is to know how their services will behave in a set of resources and the costs involved when growing and shrinking their resource pool. Most of the studies in this area rely on simulation‐based experiments, which consider simplified modeling of applications and computing environment. In order to better predict service's behavior on Cloud platforms, we developed an integrated architecture that is based on both simulation and emulation. The proposed architecture, named EMUSIM, automatically extracts information from application behavior via emulation and then uses this information to generate the corresponding simulation model. We performed experiments using an image processing application as a case study and found that EMUSIM was able to accurately model such application via emulation and use the model to supply information about its potential performance in a Cloud provider. We also discuss our experience using EMUSIM for deploying applications in a real public Cloud provider. EMUSIM is based on an open source software stack and therefore it can be extended for analysis behavior of several other applications. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Abstract The rational design of porous materials for CO 2 capture under realistic process conditions is highly desirable. However, trade‐offs exist among a nanopore's capacity, selectivity, adsorption heat, and stability. In this study, a new generation of anion‐pillared metal‐organic frameworks (MOFs) are reported with customizable cages for benchmark CO 2 capture from flue gas. The optimally designed TIFSIX‐Cu‐TPA exhibits a high CO 2 capacity, excellent CO 2 /N 2 selectivity, high thermal stability, and chemical stability in acid solution and acidic atmosphere, as well as modest adsorption heat for facile regeneration. Additionally, the practical separation performance of the synthesized MOFs is demonstrated by breakthrough experiments under various process conditions. A highly selective separation is achieved at 298–348 K with the impressive CO 2 capacity of 2.1–1.4 mmol g −1 . Importantly, the outstanding performance is sustained under high humidity and over ten repeat process cycles. The molecular mechanism of MOF's CO 2 adsorption is further investigated in situ by CO 2 dosed single crystal structure and theoretical calculations, highlighting two separate binding sites for CO 2 in small and large cages featured with high CO 2 selectivity and loading, respectively. The simultaneous adsorption of CO 2 inside these two types of interconnected cages accounts for the high performance of these newly designed anionic pillar‐caged MOFs.

Automatic document layout analysis is a crucial step in cognitive computing and processes that extract information out of document images, such as specific-domain knowledge database creation, graphs and images understanding, extraction of structured data from tables, and others. Even with the progress observed in this field in the last years, challenges are still open and range from accurately detecting content boxes to classifying them into semantically meaningful classes. With the popularization of mobile devices and cloud-based services, the need for approaches that are both fast and economic in data usage is a reality. In this paper we propose a fast one-dimensional approach for automatic document layout analysis considering text, figures and tables based on convolutional neural networks (CNN). We take advantage of the inherently one-dimensional pattern observed in text and table blocks to reduce the dimension analysis from bi-dimensional documents images to 1D signatures, improving significantly the overall performance: we present considerably faster execution times and more compact data usage with no loss in overall accuracy if compared with a classical bidimensional CNN approach.

Diffuse large B-cell lymphoma (DLBCL) is the most common aggressive form of non-Hodgkin lymphoma with variable biology and clinical behavior. The current classification does not fully explain the biological and clinical heterogeneity of DLBCLs. In this study, we carried out genomewide DNA methylation profiling of 140 DLBCL samples and 10 normal germinal center B cells using the HpaII tiny fragment enrichment by ligation-mediated polymerase chain reaction assay and hybridization to a custom Roche NimbleGen promoter array. We defined methylation disruption as a main epigenetic event in DLBCLs and designed a method for measuring the methylation variability of individual cases. We then used a novel approach for unsupervised hierarchical clustering based on the extent of DNA methylation variability. This approach identified 6 clusters (A-F). The extent of methylation variability was associated with survival outcomes, with significant differences in overall and progression-free survival. The novel clusters are characterized by disruption of specific biological pathways such as cytokine-mediated signaling, ephrin signaling, and pathways associated with apoptosis and cell-cycle regulation. In a subset of patients, we profiled gene expression and genomic variation to investigate their interplay with methylation changes. This study is the first to identify novel epigenetic clusters of DLBCLs and their aberrantly methylated genes, molecular associations, and survival.