Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Emerging Approaches to DNA Data Storage: Challenges and Prospects

With the total amount of worldwide data skyrocketing, the global data storage demand is predicted to grow to 1.75 × 1014GB by 2025. Traditional storage methods have difficulties keeping pace given that current storage media have a maximum density of 103GB/mm3. As such, data production will far exceed the capacity of currently available storage methods. The costs of maintaining and transferring data, as well as the limited lifespans and significant data losses associated with current technologies also demand advanced solutions for information storage. Nature offers a powerful alternative through the storage of information that defines living organisms in unique orders of four bases (A, T, C, G) located in molecules called deoxyribonucleic acid (DNA). DNA molecules as information carriers have many advantages over traditional storage media. Their high storage density, potentially low maintenance cost, ease of synthesis, and chemical modification make them an ideal alternative for information storage. To this end, rapid progress has been made over the past decade by exploiting user-defined DNA materials to encode information. In this review, we discuss the most recent advances of DNA-based data storage with a major focus on the challenges that remain in this promising field, including the current intrinsic low speed in data writing and reading and the high cost per byte stored. Alternatively, data storage relying on DNA nanostructures (as opposed to DNA sequence) as well as on other combinations of nanomaterials and biomolecules are proposed with promising technological and economic advantages. In summarizing the advances that have been made and underlining the challenges that remain, we provide a roadmap for the ongoing research in this rapidly growing field, which will enable the development of technological solutions to the global demand for superior storage methodologies

From unlabelled tweets to Twitter-specific opinion words

In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification

Determination of the Carrier-Envelope Phase of Few-Cycle Laser Pulses with Terahertz-Emission Spectroscopy

The availability of few-cycle optical pulses opens a window to physical phenomena occurring on the attosecond time scale. In order to take full advantage of such pulses, it is crucial to measure and stabilise their carrier-envelope (CE) phase, i.e., the phase difference between the carrier wave and the envelope function. We introduce a novel approach to determine the CE phase by down-conversion of the laser light to the terahertz (THz) frequency range via plasma generation in ambient air, an isotropic medium where optical rectification (down-conversion) in the forward direction is only possible if the inversion symmetry is broken by electrical or optical means. We show that few-cycle pulses directly produce a spatial charge asymmetry in the plasma. The asymmetry, associated with THz emission, depends on the CE phase, which allows for a determination of the phase by measurement of the amplitude and polarity of the THz pulse

First records of two mealybug species in Brazil and new potential pests of papaya and coffee

Five mealybug (Hemiptera: Pseudococcidae) plant pest species: Dysmicoccus grassii (Leonardi), Ferrisia malvastra (McDaniel), Ferrisia virgata (Cockerell), Phenacoccus tucumanus Granara de Willink, and Pseudococcus elisae Borchsenius are recorded for the first time in the state of Espírito Santo, Brazil. These are the first records of D. grassii in Brazil, from papaya (Carica papaya, Caricaceae), and from coffee (Coffea canephora, Rubiaceae). Ferrisia malvastra is also newly recorded in Brazil, where it was found on Bidens pilosa (Asteraceae). Ferrisia virgata was collected from an unidentified weed and Phenacoccus tucumanus from Citrus sp. (Rutaceae). Plotococcus capixaba Kondo was found on pitanga (Eugenia cf. pitanga, Myrtaceae) and Pseudococcus elisae on Coffea canephora, which are new host records for these mealybugs

Refining Kidney Survival in 383 Genetically Characterized Patients With Nephronophthisis

Introduction: Nephronophthisis (NPH) comprises a group of rare disorders accounting for up to 10% of end-stage kidney disease (ESKD) in children. Prediction of kidney prognosis poses a major challenge. We assessed differences in kidney survival, impact of variant type, and the association of clinical characteristics with declining kidney function. Methods: Data was obtained from 3 independent sources, namely the network for early onset cystic kidney diseases clinical registry (n = 105), an online survey sent out to the European Reference Network for Rare Kidney Diseases (n = 60), and a literature search (n = 218). Results: A total of 383 individuals were available for analysis: 116 NPHP1, 101 NPHP3, 81 NPHP4 and 85 NPHP11/TMEM67 patients. Kidney survival differed between the 4 cohorts with a highly variable median age at onset of ESKD as follows: NPHP3, 4.0 years (interquartile range 0.3–12.0); NPHP1, 13.5 years (interquartile range 10.5–16.5); NPHP4, 16.0 years (interquartile range 11.0–25.0); and NPHP11/TMEM67, 19.0 years (interquartile range 8.7–28.0). Kidney survival was significantly associated with the underlying variant type for NPHP1, NPHP3, and NPHP4. Multivariate analysis for the NPHP1 cohort revealed growth retardation (hazard ratio 3.5) and angiotensin-converting enzyme inhibitor (ACEI) treatment (hazard ratio 2.8) as 2 independent factors associated with an earlier onset of ESKD, whereas arterial hypertension was linked to an accelerated glomerular filtration rate (GFR) decline. Conclusion: The presented data will enable clinicians to better estimate kidney prognosis of distinct patients with NPH and thereby allow personalized counseling

Imaging of kidney cysts and cystic kidney diseases in children: an international working group consensus statement

Kidney cysts can manifest as focal disease (simple and complex kidney cysts), affect a whole kidney (eg, multicystic dysplastic kidney or cystic dysplasia), or manifest as bilateral cystic disease (eg, autosomal recessive polycystic kidney disease [ARPKD] or autosomal dominant polycystic kidney disease [ADPKD]). In children, as opposed to adults, a larger proportion of kidney cysts are due to genetic diseases (eg, HNF1B nephropathy, various ciliopathies, and tuberous sclerosis complex), and fewer patients have simple cysts or acquired cystic kidney disease. The purpose of this consensus statement is to provide clinical guidance on standardization of imaging tests to evaluate kidney cysts in children. A committee of international experts in pediatric nephrology, pediatric radiology, pediatric US, and adult nephrology prepared systematic literature reviews and formulated recommendations at a consensus meeting. The final statement was endorsed by the European Society of Pediatric Radiology, the European Federation of Societies for Ultrasound in Medicine and Biology, the European Society of Pediatric Nephrology, and reviewed by the European Reference Network for Rare Kidney Diseases. Main recommendations are as follows: US is the method of choice when assessing pediatric kidney cysts, with selected indications for MRI and contrast-enhanced US. CT should be avoided whenever possible because of ionizing radiation. Renal US yields essential diagnostic information in many cases. In patients with ARPKD or other ciliopathies, abdominal US is needed for diagnosis and screening of portal hypertension. US is usually sufficient for follow-up kidney imaging, but MRI can be valuable for clinical trials in patients with ADPKD or in older children with tuberous sclerosis complex to evaluate both kidney cysts and angiomyolipomas

A risk-based approach to cumulative effect assessments for marine management

Marine ecosystems are increasingly threatened by the cumulative effects of multiple human pressures. Cumulative effect assessments (CEAs) are needed to inform environmental policy and guide ecosystem-based management. Yet, CEAs are inherently complex and seldom linked to real-world management processes. Therefore we propose entrenching CEAs in a risk management process, comprising the steps of risk identification, risk analysis and risk evaluation. We provide guidance to operationalize a risk-based approach to CEAs by describing for each step guiding principles and desired outcomes, scientific challenges and practical solutions. We reviewed the treatment of uncertainty in CEAs and the contribution of different tools and data sources to the implementation of a risk based approach to CEAs. We show that a risk-based approach to CEAs decreases complexity, allows for the transparent treatment of uncertainty and streamlines the uptake of scientific outcomes into the science-policy interface. Hence, its adoption can help bridging the gap between science and decision-making in ecosystem-based management

Erwartungsbildung über den Wahlausgang und ihr Einfluss auf die Wahlentscheidung

Erwartungen über den Wahlausgang haben einen festen Platz sowohl in Rational-Choice-Theorien des Wählerverhaltens als auch in stärker sozialpsychologisch orientierten Ansätzen. Die Bildung von Erwartungen und ihr Einfluss auf die Wahlentscheidung ist dabei jedoch ein noch relativ unerforschtes Gebiet. In diesem Beitrag werden anhand von Wahlstudien für Belgien, Österreich und Deutschland verschiedene Fragen der Erwartungsbildung und ihrer Auswirkungen untersucht. Zunächst wird die Qualität der Gesamterwartungen analysiert und verschiedene Faktoren identifiziert, die einen systematischen Einfluss auf die Erwartungsbildung haben. Im zweiten Schritt wenden wir uns den Einzelerwartungen über verschiedene Parteien und Koalitionen zu und finden eine moderate Verzerrung zugunsten der präferierten Parteien und Koalitionen. Dabei kann gezeigt werden, dass der Effekt des Wunschdenkens mit dem politischen Wissen und dem Bildungsgrad abnimmt. Schließlich werden in einem letzten Schritt zwei unterschiedliche Logiken für die Auswirkungen von Erwartungen getestet, das rationale Kalkül des koalitionsstrategischen Wählens zur Vermeidung der Stimmenvergeudung sowie der sozialpsychologisch begründete Bandwagon-Effekt. Das Ausmaß an politischem Wissen scheint dabei eine zentrale vermittelnde Variable zwischen den beiden Logiken zu sein

Metal Bioavailability in the Sava River Water

Metals present one of the major contamination problems for freshwater systems, such as the Sava River, due to their high toxicity, persistence, and tendency to accumulate in sediment and living organisms. The comprehensive assessment of the metal bioavailability in the Sava River encompassed the analyses of dissolved and DGT-labile metal species of nine metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in the river water, as well as the evaluation of the accumulation of five metals (Cd, Cu, Fe, Mn, and Zn) in three organs (liver, gills, and gastrointestinal tissue) of the bioindicator organism, fish species European chub (Squalius cephalus L.).This survey was conducted mainly during the year 2006, in two sampling campaigns, in April/May and September, as periods representative for chub spawning and post-spawning. Additionally, metal concentrations were determined in the intestinal parasites acanthocephalans, which are known for their high affinity for metal accumulation. Metallothionein concentrations were also determined in three chub organs, as a commonly applied biomarker of metal exposure. Based on the metal concentrations in the river water, the Sava River was defined as weakly contaminated and mainly comparable with unpolluted rivers, which enabled the analyses of physiological variability of metal and metallothionein concentrations in the chub organs, as well as the establishment of their constitutive levels