1,653 research outputs found
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Average word embeddings are a common baseline for more sophisticated sentence
embedding techniques. However, they typically fall short of the performances of
more complex models such as InferSent. Here, we generalize the concept of
average word embeddings to power mean word embeddings. We show that the
concatenation of different types of power mean word embeddings considerably
closes the gap to state-of-the-art methods monolingually and substantially
outperforms these more complex techniques cross-lingually. In addition, our
proposed method outperforms different recently proposed baselines such as SIF
and Sent2Vec by a solid margin, thus constituting a much harder-to-beat
monolingual baseline. Our data and code are publicly available.Comment: Experiments/plots added: Normalization + Figure 1 (dimensionality vs.
performance
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation
Evaluation of cross-lingual encoders is usually performed either via
zero-shot cross-lingual transfer in supervised downstream tasks or via
unsupervised cross-lingual textual similarity. In this paper, we concern
ourselves with reference-free machine translation (MT) evaluation where we
directly compare source texts to (sometimes low-quality) system translations,
which represents a natural adversarial setup for multilingual encoders.
Reference-free evaluation holds the promise of web-scale comparison of MT
systems. We systematically investigate a range of metrics based on
state-of-the-art cross-lingual semantic representations obtained with
pretrained M-BERT and LASER. We find that they perform poorly as semantic
encoders for reference-free MT evaluation and identify their two key
limitations, namely, (a) a semantic mismatch between representations of mutual
translations and, more prominently, (b) the inability to punish
"translationese", i.e., low-quality literal translations. We propose two
partial remedies: (1) post-hoc re-alignment of the vector spaces and (2)
coupling of semantic-similarity based metrics with target-side language
modeling. In segment-level MT evaluation, our best metric surpasses
reference-based BLEU by 5.7 correlation points.Comment: ACL2020 Camera Ready (v3: several small fixes, e.g., Unicode errors
Comprehensive Treatment Algorithms of the Swiss Peritoneal Cancer Group for Peritoneal Cancer of Gastrointestinal Origin
Peritoneal cancer (PC) is a dire finding, yet in selected patients, long-term survival is possible. Complete cytoreductive surgery (CRS) together with combination immunochemotherapy is essential to achieve cure. Hyperthermic intraperitoneal chemotherapy (HIPEC) and pressurized intraperitoneal aerosol chemotherapy (PIPAC) are increasingly added to the multimodal treatment. The Swiss Peritoneal Cancer Group (SPCG) is an interdisciplinary group of expert clinicians. It has developed comprehensive treatment algorithms for patients with PC from pseudomyxoma peritonei, peritoneal mesothelioma, gastric, and colorectal origin. They include multimodal neoadjuvant treatment, surgical resection, and palliative care. The indication for and results of CRS HIPEC and PIPAC are discussed in light of the current literature. Institutional volume and clinical expertise required to achieve best outcomes are underlined, while inclusion of patients considered for CRS HIPEC and PIPAC in a clinical registry is strongly advised. The present recommendations are in line with current international guidelines and provide the first comprehensive treatment proposal for patients with PC including intraperitoneal chemotherapy. The SPCG comprehensive treatment algorithms provide evidence-based guidance for the multimodal care of patients with PC of gastrointestinal origin that were endorsed by all Swiss clinicians routinely involved in the multimodal care of these challenging patients
Common clonal origin of conventional T cells and induced regulatory T cells in breast cancer patients
Regulatory CD4+ T cells (Treg) prevent tumor clearance by conventional T cells (Tconv) comprising a major obstacle of cancer immune-surveillance. Hitherto, the mechanisms of Treg repertoire formation in human cancers remain largely unclear. Here, we analyze Treg clonal origin in breast cancer patients using T-Cell Receptor and single-cell transcriptome sequencing. While Treg in peripheral blood and breast tumors are clonally distinct, Tconv clones, including tumor-antigen reactive effectors (Teff), are detected in both compartments. Tumor-infiltrating CD4+ cells accumulate into distinct transcriptome clusters, including early activated Tconv, uncommitted Teff, Th1 Teff, suppressive Treg and pro-tumorigenic Treg. Trajectory analysis suggests early activated Tconv differentiation either into Th1 Teff or into suppressive and pro-tumorigenic Treg. Importantly, Tconv, activated Tconv and Treg share highly-expanded clones contributing up to 65% of intratumoral Treg. Here we show that Treg in human breast cancer may considerably stem from antigen-experienced Tconv converting into secondary induced Treg through intratumoral activation
The Forest Observation System, building a global reference dataset for remote sensing of forest biomass
International audienceForest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (aGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. aGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world's forests. all plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities
Glutaredoxin regulation of primary root growth is associated with early drought stress tolerance in pearl millet
Seedling root traits impact plant establishment under challenging environments. Pearl millet is one of the most heat and drought tolerant cereal crops that provides a vital food source across the sub-Saharan Sahel region. Pearl millet’s early root system features a single fast-growing primary root which we hypothesize is an adaptation to the Sahelian climate. Using crop modeling, we demonstrate that early drought stress is an important constraint in agrosystems in the Sahel where pearl millet was domesticated. Furthermore, we show that increased pearl millet primary root growth is correlated with increased early water stress tolerance in field conditions. Genetics including genome-wide association study and quantitative trait loci (QTL) approaches identify genomic regions controlling this key root trait. Combining gene expression data, re-sequencing and re-annotation of one of these genomic regions identified a glutaredoxin-encoding gene PgGRXC9 as the candidate stress resilience root growth regulator. Functional characterization of its closest Arabidopsis homolog AtROXY19 revealed a novel role for this glutaredoxin (GRX) gene clade in regulating cell elongation. In summary, our study suggests a conserved function for GRX genes in conferring root cell elongation and enhancing resilience of pearl millet to its Sahelian environment
Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an
Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis
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