Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of energy detection thresholds and error probability for amplitude-modulated short-range communication radios

In this thesis, novel data and channel estimation methods are proposed and analyzed for lowcomplexity short-range communication (SRC) radios. Low complexity is challenging to achieve especially in very wideband or millimeter-wave SRC radios where phase recovery and energy capture from numerous multipaths easily become a bottleneck for system design. A specific type of transceiver is selected using pulse amplitude modulation (PAM) at the transmitter and energy detection (ED) at the receiver, and it is thus called an ED-PAM system. Nonnegative PAM alphabets allow using an ED structure which enables a phase-unaware detection method for avoiding complicated phase recovery at the receiver. Moreover, the ED-PAM approach results in a simple multipath energy capture, and only one real decision variable, whose dimension is independent of the symbol alphabet size, is needed. In comparison with optimal phase-aware detection, the appealing simplicity of suboptimal EDPAM systems is achieved at the cost of the need for a higher transmitted signal energy or shorter link distance for obtaining a sufficient signal-to-noise ratio (SNR) at the receiver, as ED-PAM systems are more vulnerable to the effects of noise and interference. On the other hand, the consequences of requiring a higher SNR may not be severe in the type of SRC scenarios where a sufficient received SNR is readily available due to a short link distance. Furthermore, significant interference can be avoided by signal design. However, what has slowed down the development of ED-PAM systems is that efficient symbol decision threshold estimation and related error probability analysis in multipath fading channels have remained as unsolved problems. Based on the above observations, this thesis contributes to the state-of-the-art of the design and analysis for ED-PAM systems as follows. Firstly, a closed-form near-optimal decision threshold selection method, which adapts to a time-varying channel gain and enables an arbitrary choice of the PAM alphabet size and an integer time-bandwidth product of the receiver filters, is proposed. Secondly, two blind estimation schemes of the parameters for the threshold estimation are introduced. Thirdly, analytical error probability evaluation in frequency-selective multipath fading channels is addressed. Special attention is given to lognormal fading channels, which are typically used to model very wideband SRC multipath channels. Finally, analytical error probability evaluation with nonideal parameter estimation is presented. The results can be used in designing low-complexity transceivers for very wideband and millimeter-wave wireless SRC devices of the future.
Mapping Changes in Land Cover Composition and Pattern for Comparing Mediterranean Rangeland Restoration Alternatives

The limited amount of studies addressing the long‐term effectiveness of restoration actions to combat land degradation is a constraint for current landscape restoration planning and implementation. Remote sensing data provide a unique opportunity for gathering information on the spatial and temporal variability of restoration processes and may contribute to fill this knowledge gap. The aim of this study is to compare different management actions undertaken in Mediterranean degraded rangelands (i.e. dense pine plantations, thinned pine plantations, protection and passive management) by analysing landscape composition and pattern change assessed from object‐based classification of aerial photograph of 1954, 1977 and 2006. Results show a clear link between landscape composition and structure and the different restoration actions, allowing comparison between their spatial and temporal dynamics. All restoration actions promoted an increase in woody vegetation cover compared with non‐restored areas. However, only protection and passive management favoured the restoration of dense, complex and well‐connected patches of native Mediterranean maquis. The proposed methodology is discussed with respect to its applicability for future Mediterranean landscape restoration planning in a wider geographic and ecological perspective. Copyright © 2015 John Wiley & Sons, Ltd.
Enhancing TCP's Loss Recovery Using Limited Transmit

Mass spectrometer leak detector and associated high vacuum pump and trace gas leak sample inlet connected so that the leak sample is fed to the outlet of the vacuum pump, backwards through the high vacuum pump and into the spectrometer. The invention is carried out through apparatus comprising a diffusion pump with a mass spectrometer connected to the pump inlet and a trace gas inlet connected to the diffusion pump foreline.
The remarkable Re–Os chronometer in molybdenite: how and why it works

The Re–Os (rhenium–osmium) chronometer applied to molybdenite (MoS2) is now demonstrated to be remarkably robust, surviving intense deformation and high‐grade thermal metamorphism. Successful dating of molybdenite is dependent on proper preparation of the mineral separate and analysis of a critical quantity of molybdenite, unique to each sample, such that recognized spatial decoupling of 187Re parent and 187Os daughter within individual molybdenite crystals is overcome. Highly precise, accurate and reproducible age results are derived through isotope dilution and negative thermal ion mass spectrometry (ID‐NTIMS). Spatial decoupling of parent–daughter precludes use of the laser ablation ICP‐MS microanalytical technique for Re–Os dating of molybdenite. The use of a reference or control sample is necessary to establish laboratory credibility and for interlaboratory comparisons. The Rb–Sr, K–Ar and 40Ar/39Ar chronometers are susceptible to chemical and thermal disturbance, particularly in terranes that have experienced subsequent episodes of hydrothermal/magmatic activity, and therefore should not be used as a basis for establishing accuracy in Re–Os dating of molybdenite, as has been done in the past. Re–Os ages for molybdenite are almost always in agreement with observed geological relationships and, when available, with zircon and titanite U–Pb ages. For terranes experiencing multiple episodes of metamorphism and deformation, molybdenite is not complicated by overgrowths as is common for some minerals used in U–Pb dating (e.g. zircon, monazite, xenotime), nor are Re and Os mobilized beyond the margins of individual crystals during solid‐state recrystallization. Moreover, inheritance of older molybdenite cores, incorporation of common Os, and radiogenic Os loss are exceedingly rare, whereas inheritance, common Pb and Pb loss are common complications in U–Pb dating techniques. Therefore, molybdenite ages may serve as point‐in‐time markers for age comparisons.
A Fast and Lightweight Network for Low-Light Image Enhancement

Low-light images often suffer from severe noise, low brightness, low contrast, and color deviation. While several low-light image enhancement methods have been proposed, there remains a lack of efﬁcient methods that can simultaneously solve all of these problems. In this paper, we introduce FLW-Net, a Fast and LightWeight Network for low-light image enhancement that signiﬁcantly improves processing speed and overall effect. To achieve efﬁcient low-light image enhancement, we recognize the challenges of the lack of an absolute reference and the need for a large receptive ﬁeld to obtain global contrast. Therefore, we propose an efﬁcient global feature information extraction component and design loss functions based on relative information to overcome these challenges. Finally, we conduct comparative experiments to demonstrate the effectiveness of the proposed method, and the results con-ﬁrm that FLW-Net can signiﬁcantly reduce the complexity of supervised low-light image enhancement networks while improving processing effect. Code is available at https://github.com/hitzhangyu/FLW-Net
Gradient Descent Ascent for Min-Max Problems on Riemannian Manifold

In the paper, we study a class of useful non-convex minimax optimization problems on the Riemanian manifold and propose a class of Riemanian gradient descent ascent algorithms to solve these minimax problems. Specifically, we propose a new Riemannian gradient descent ascent (RGDA) algorithm for the deterministic minimax optimization. Moreover, we prove that the RGDA has a sample complexity of $O(\kappa^2\epsilon^{-2})$ for finding an $\epsilon$-stationary point of the nonconvex strongly-concave minimax problems, where $\kappa$ denotes the condition number. At the same time, we introduce a Riemannian stochastic gradient descent ascent (RSGDA) algorithm for the stochastic minimax optimization. In the theoretical analysis, we prove that the RSGDA can achieve a sample complexity of $O(\kappa^4\epsilon^{-4})$. To further reduce the sample complexity, we propose a novel momentum variance-reduced Riemannian stochastic gradient descent ascent (MVR-RSGDA) algorithm based on a new momentum variance-reduced technique of STORM. We prove that the MVR-RSGDA algorithm achieves a lower sample complexity of $\tilde{O}(\kappa^{4}\epsilon^{-3})$ without large batches, which reaches near the best known sample complexity for its Euclidean counterparts. This is the first study of the minimax optimization over the Riemannian manifold. Extensive experimental results on the robust deep neural networks training over Stiefel manifold demonstrate the efficiency of our proposed algorithms.
antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation

Abstract Microorganisms produce small bioactive compounds as part of their secondary or specialised metabolism. Often, such metabolites have antimicrobial, anticancer, antifungal, antiviral or other bio-activities and thus play an important role for applications in medicine and agriculture. In the past decade, genome mining has become a widely-used method to explore, access, and analyse the available biodiversity of these compounds. Since 2011, the ‘antibiotics and secondary metabolite analysis shell—antiSMASH’ (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free to use web server and as a standalone tool under an OSI-approved open source licence. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in archaea, bacteria, and fungi. Here, we present the updated version 7 of antiSMASH. antiSMASH 7 increases the number of supported cluster types from 71 to 81, as well as containing improvements in the areas of chemical structure prediction, enzymatic assembly-line visualisation and gene cluster regulation.
Large Language Models are Zero-Shot Clinical Information Extractors

We show that large language models , such as GPT-3 [1], perform well at zero-shot information extraction from clinical text despite not being trained speciﬁcally for the clinical domain. We present several examples showing how to use these models as tools for the diverse tasks of (i) concept disambiguation, (ii) evidence extraction, (iii) coreference resolution, and (iv) concept extraction, all on clinical text . The key to good performance is the use of simple task-speciﬁc programs that map from the language model outputs to the label space of the task. We refer to these programs as resolvers , a generalization of the verbalizer which deﬁnes a mapping between output tokens and a discrete label space [2]. We show in our examples that good resolvers share common components (e.g., “safety checks” that ensure the language model outputs faithfully match the input data), and that the common patterns across tasks make resolvers lightweight and easy to create. To better evaluate these systems, we also introduce two new datasets for benchmarking zero-shot clinical information extraction based on manual relabeling of the CASI dataset [3] with labels for new tasks. On the clinical extraction tasks we studied, the GPT-3 + resolver systems signiﬁcantly outperform existing zero-and few-shot baselines.
14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

Deep learning using convolutional neural networks (CNN) gives state-of-the-art accuracy on many computer vision tasks (e.g. object detection, recognition, segmentation). Convolutions account for over 90% of the processing in CNNs for both inference/testing and training, and fully convolutional networks are increasingly being used. To achieve state-of-the-art accuracy requires CNNs with not only a larger number of layers, but also millions of filters weights, and varying shapes (i.e. filter sizes, number of filters, number of channels) as shown in Fig. 14.5. 1. For instance, AlexNet [1] uses 2.3 million weights (4.6MB of storage) and requires 666 million MACs per 227×227 image (13kMACs/pixel). VGG16 [2] uses 14.7 million weights (29.4MB of storage) and requires 15.3 billion MACs per 224×224 image (306kMACs/pixel). The large number of filter weights and channels results in substantial data movement, which consumes significant energy.
Large-Scale Atmospheric Forcing of Recent Trends toward Early Snowmelt Runoff in California

Abstract Since the late 1940s, snowmelt and runoff have come increasingly early in the water year in many basins in northern and central California. This subtle trend is most pronounced in moderate-altitude basins, which are sensitive to changes in mean winter temperatures. Such basins have broad areas in which winter temperatures are near enough to freezing that small increases result initially in the formation of less snow and eventually in early snowmelt. In moderate-altitude basins of California, a declining fraction of the annual runoff has come in April–June. This decline has been compensated by increased fractions of runoff at other, mostly earlier, times in the water year. Weather stations in central California, including the central Sierra Nevada, have shown trends toward warmer winters since the 1940s. A series of regression analyses indicate that runoff timing responds equally to the observed decadal-scale trends in winter temperature and interannual temperature variations of the same magnitude.
Impact of new aircraft observations Mode‐S MRAR in a mesoscale NWP model

The impact of recently available high‐resolution Mode‐S Meteorological Routine Air Report (MRAR) wind and temperature observations is evaluated in the mesoscale numerical weather prediction (NWP) model Aire Limitée Adaptation dynamique Développement InterNational (ALADIN). Data available from the airspace communicating with the Ljubljana Airport in Slovenia are assimilated by using the three‐dimensional variational assimilation procedure on top of all other observations assimilated operationally. A data selection method based on aircraft type was shown to be important for the first application of the new observations in ALADIN. The evaluation of Mode‐S MRAR impact included both winter and summer periods. In both seasons a clear improvement of wind and temperature forecasts was found for in the short forecast range, 1–3 h. The impact in the 24 h forecast range depends on season, with a consistent positive improvement of the boundary layer temperature forecasts obtained for the stable anticyclonic winter situations. In summer, the impact was mixed and it was found to be sensitive to the multivariate aspects of the moisture analysis. Overall presented results suggest that the new aircraft‐derived observations Mode‐S MRAR have a significant potential for mesoscale NWP and improved data assimilation modeling.
How Permeability Depends on Stress and Pore Pressure in Coalbeds: A New Model

In naturally fractured formations, such as coal, permeability is sensitive to changes in stress or pore pressure (i.e., effective stress). This paper presents a new theoretical model for calculating pore volume compressibility and permeability in coals as a function of effective stress and matrix shrinkage, using a single equation. The equation is appropriate for uniaxial strain conditions, as expected in a reservoir. The model predicts how permeability changes as pressure is decreased (i.e., drawdown). Pore volume compressibility is derived in this theory from fundamental reservoir parameters. It is not constant, as often assumed. Pore volume compressibility is high in coals because porosity is so small. A rebound in permeability can occur at lower drawdown pressures for the highest modulus and matrix shrinkage values. We have also history matched rates from a {open_quotes}boomer{close_quotes} well in the fairway of the San Juan basin using various stress-dependent permeability functions. The best fit stress-permeability function is then compared with the new theory.
Fluctuations of precipitated electron intensity in flickering auroral arcs

This paper reports on electron spectra associated with two aurorae observed by ground-based television. One auroral arc was observed to flicker, large variations in the precipitated electron energy occurring on a time scale of 114 ms. The major variations occur at the higher energies of the 0.5--20 keV range covered by the detectors. Changes in the particle flux occur primarily in the pitch angle range 0/sup 0/ to 60/sup 0/. Analysis of the video data shows a larger variation in intensity along the lower border of the arc in keeping with the results of the electron spectra. The second arc was not observed to flicker, and the associated electron spectra and video data show no large variations in precipitated electron energy or video intensity modulation. While pitch angle distributions tend to be field-aligned in the first arc, the distributions in the second arc are nearly isotropic or peaked from 60/sup 0/ to 90/sup 0/ in the downward hemisphere.
Deep seismic soundings along Hirapur‐Mandla profile, central India

Summary. The crustal depth section along Hirapur-Mandla profile has been computed in two steps from Deep Seismic Sounding (DSS) data. The shallow section up to the crystalline basement is derived by inverting first arrival refraction travel times. The upper Vindhyan sediments (velocity 4.5 km s-’) have a maximum thickness of about 1.5 km at Bakshaho. The lower Vindhyan sediments (velocity 5.4 km s-’) were deposited north of NarmadaSon lineament between Katangi and Narsinghgarh in a graben developed in crystalline basement. The thickness of the lower Vindhyans increases from north to south towards Katangi and the depth to the basement reaches 5.5 km near Jabera. The depth to the Moho boundary varies from 39.5 km near Tikaria to 45 km at Narsinghgarh. The narrow block between Katangi and Jabalpur forms a horst feature which represents the Narmada-Son lineament forming the southern boundary of the Vindhyan basin. Two-dimensional ray tracing was performed generating travel time curves from various shot points which were matched with observed travel time data.
Beachgoers' Demands vs. Blue Flag Aims in South Africa

ABSTRACT Lucrezi, S. and Saayman, M. , 2015. Beachgoers' demands vs. Blue Flag aims in South Africa. The Blue Flag is a beach award aimed to preserve features of the beach environment. While it is assumed that the Blue Flag attracts tourism, this depends on factors such as the socio-demographic background of beachgoers, their perception of beach attributes, and their knowledge and perception of the Blue Flag. This research used structural equation modeling (SEM) to investigate relationships between perceived beach attributes (beach selection criteria, deterrent factors of the beach, and beach quality rating) and beachgoers' knowledge and perception of the Blue Flag award on three Blue Flag and three non-Blue Flag beaches in South Africa. A single statistically significant relationship emerged between beach quality rating and evaluation (both positive and negative views, which are not necessarily mutually exclusive) of the Blue Flag award by beachgoers. The data highlight the importance of generating bottom-up information to assist potential, occasional, and running Blue Flag managers in South Africa, particularly in regard to proper education and promotion of the Blue Flag award.
Anomalous sound velocities in polycrystalline MgO under non‐hydrostatic compression

Brillouin scattering from polycrystalline MgO (periclase) non‐hydrostatically compressed to, and decompressed from, 60 GPa at room temperature documents shear‐ and compressional‐wave velocities ∼20% lower than values measured under hydrostatic compression. Calculations reveal that wave velocities can be lowered due to the elastic effects of non‐hydrostatic stresses, but by only a few percent. Neither these elastic effects nor preferred orientation can account for the reduction in the sound velocity.
Exposing Cross-Lingual Lexical Knowledge from Multilingual Sentence Encoders

Pretrained multilingual language models (LMs) can be successfully transformed into multilingual sentence encoders (SEs; e.g., L A BSE, X MPNET) via additional ﬁne-tuning or model distillation on parallel data. However, it remains uncertain how to best leverage their knowledge to represent sub-sentence lexical items (i.e., words and phrases) in cross-lingual lexical tasks. In this work, we probe these SEs for the amount of cross-lingual lexical knowledge stored in their parameters, and compare them against the original multilingual LMs. We also devise a novel method to expose this knowledge by additionally ﬁne-tuning multilingual models through inexpensive contrastive learning procedure, requiring only a small amount of word translation pairs. We evaluate our method on bilingual lexical induction (BLI), cross-lingual lexical semantic similarity, and cross-lingual entity linking, and report substantial gains on standard benchmarks (e.g., +10 Precision@1 points in BLI), validating that the SEs such as L A BSE can be ‘rewired’ into effective cross-lingual lexical encoders. Moreover, we show that resulting representations can be successfully interpolated with static embeddings from cross-lingual word embedding spaces to further boost the performance in lexical tasks. In sum, our approach provides an effective tool for exposing and harnessing multilingual lexical knowledge ‘hidden’ in multilingual sentence encoders.
ERNIE-DOC: The Retrospective Long-Document Modeling Transformer

Transformers are not suited for processing long document input due to its quadratically increasing memory and time consumption. Simply truncating a long document or applying the sparse attention mechanism will incur the context fragmentation problem or inferior modeling capability with comparable model size. In this paper, we propose ERNIE-D OC , a document-level language pretraining model based on Recurrence Transform-ers (Dai et al., 2019). Two well-designed techniques, namely the retrospective feed mechanism and the enhanced recurrence mechanism enable ERNIE-D OC with much longer effective context length to capture the contextual information of a whole document. We pretrain ERNIE-D OC to explicitly learn the relationship among segments with an additional document-aware segment reordering ob-jective. Various experiments on both English and Chinese document-level tasks are conducted. ERNIE-D OC achieves SOTA language modeling result of 16.8 ppl on WikiText-103 and outperforms competitive pretraining models on most language understanding tasks such as text classiﬁcation, question answering by a large margin.