CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals

Most previous studies integrate cognitive language processing signals (e.g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly concatenating word embeddings with cognitive features, ignoring the gap between the two modalities (i.e., textual vs. cognitive) and noise in cognitive features. In this paper, we propose a CogAlign approach to these issues, which learns to align textual neural representations to cognitive features. In CogAlign, we use a shared encoder equipped with a modality discriminator to alternatively encode textual and cognitive inputs to capture their differences and commonalities. Additionally, a text-aware attention mechanism is proposed to detect task-related information and to avoid using noise in cognitive features. Experimental results on three NLP tasks, namely named entity recognition, sentiment analysis and relation extraction, show that CogAlign achieves significant improvements with multiple cognitive features over state-of-the-art models on public datasets. Moreover, our model is able to transfer cognitive information to other datasets that do not have any cognitive processing signals

Robust Inference for the Stepped Wedge Design

Based on a permutation argument, we derive a closed form expression for an estimate of the treatment effect, along with its standard error, in a stepped wedge design. We show that these estimates are robust to misspecification of both the mean and covariance structure of the underlying data-generating mechanism, thereby providing a robust approach to inference for the treatment effect in stepped wedge designs. We use simulations to evaluate the type I error and power of the proposed estimate and to compare the performance of the proposed estimate to the optimal estimate when the correct model specification is known. The limitations, possible extensions, and open problems regarding the method are discussed

Crosslinked fluoropolymers exhibiting superior high-temperature energy density and charge-discharge efficiency

Superior high-temperature discharged energy densities in comparison to those of the current dielectric polymers have been demonstrated in the crosslinked fluoropolymers

Vertical Stress and Deformation Characteristics of Roadside Backfilling Body in Gob-Side Entry for Thick Coal Seams with Different Pre-Split Angles

Retained gob-side entry (RGE) is a significant improvement for fully-mechanized longwall mining. The environment of surrounding rock directly affects its stability. Roadside backfilling body (RBB), a man-made structure in RGE plays the most important role in successful application of the technology. In the field, however, the vertical deformation of RBB is large during the panel extraction, which leads to malfunction of the RGE. In order to solve the problem, roof pre-split is employed. According to geological conditions as well as the physical modeling of roof behavior and deformation of surrounding rock, the support resistance of RBB is calculated. The environment of surrounding rock, vertical stress and vertical deformation of the RBB in the RGE with different roof pre-split angles are analyzed using FLAC3D software. With the increase of roof pre-split angle, the vertical stresses both in the coal wall and RBB are minimum, and the vertical deformation of RBB also decreases from 110.51 mm to 6.1 mm. Therefore, based on the results of numerical modeling and field observation, roof pre-split angle of 90° is more beneficial to the maintenance of the RGE

Calculation and system of support resistance of shield for contugous-multiple coal seams with coordinated mining

Under the coordinated mining mode of close-multiple coal seams, due to the mining influence between coal seams, the roof structural characteristics are different after each coal seam extraction, so the calculation methods of support resistance shield of each coal seam are also different. In order to provide ideas for the setting load of shield determination in each coal seam, the calculation methods of the support capacity of shield in each coal seam is given by comprehensive use of theoretical analysis, system development and field measurement. The results show that: ①The voussoir beam, given load of loose body and voussoir beam with given load of loose body balance roof structure models after each coal seam extraction are established. Voussoir beam balance roof structure model is applicable to the coal seams extractions that are not affected by mining or are less affected by mining. Given load of loose body balance roof structure model is applicable to the coal seams extractions with a single roof stratum and is affected by the upper coal seam extraction. Voussoir beam with given load of loose body balance roof structure model is applicable to the coal seams extractions with multi-rock strata and within has a thick and hard lithology. At the same time, affected by the extraction of the upper coal seam, the rock stratum can still maintain continuity and integrity. ②The “overburden breaking and load evaluation system for close-multiple coal seams extraction” suitable for Kailuan Group is developed, and the recommended selection results of setting load of shield in each coal seam are put forward. Through the field measurement of support capacity of shield, the load utilization rate of shield in each coal seam is generally low and the load margin of shield is too large after using the empirical selection results of the setting load of shield. After adopting the recommended selection results of the setting load of shield in each coal seam, the load utilization rate of shield in each coal seam is significantly improved and the load margin of shield is significantly reduced

Effects of Wettability and Minerals on Residual Oil Distributions Based on Digital Rock and Machine Learning

AbstractThe wettability of mineral surfaces has significant impacts on transport mechanisms of two-phase flow, distribution characteristics of fluids, and the formation mechanisms of residual oil during water flooding. However, few studies have investigated such effects of mineral type and its surface wettability on rock properties in the literature. To unravel the dependence of hydrodynamics on wettability and minerals distribution, we designed a new experimental procedure that combined the multiphase flow experiments with a CT scan and QEMSCAN to obtain 3D digital models with multiple minerals and fluids. With the aid of QEMSCAN, six mineral components and two fluids in sandstones were segmented from the CT data based on the histogram threshold and watershed methods. Then, a mineral surface analysis algorithm was proposed to extract the mineral surface and classify its mineral categories. The in situ contact angle and pore occupancy were calculated to reveal the wettability variation of mineral surface and distribution characteristics of fluids. According to the shape features of the oil phase, the self-organizing map (SOM) method, one of the machine learning methods, was used to classify the residual oil into five types, namely, network, cluster, film, isolated, and droplet oil. The results indicate that each mineral’s contribution to the mineral surface is not proportional to its relative content. Feldspar, quartz, and clay are the main minerals in the studied sandstones and play a controlling role in the wettability variation. Different wettability samples show various characteristics of pore occupancy. The water flooding front of the weakly water-wet to intermediate-wet sample is uniform, and oil is effectively displaced in all pores with a long oil production period. The water-wet sample demonstrates severe fingering, with a high pore occupancy change rate in large pores and a short oil production period. The residual oil patterns gradually evolve from networks to clusters, isolated, and films due to the effects of snap-off and wettability inversion. This paper reveals the effects of wettability of mineral surface on the distribution characteristics and formation mechanisms of residual oil, which offers us an in-deep understanding of the impacts of wettability and minerals on multiphase flow and helps us make good schemes to improve oil recovery

NEW LATE JURASSIC PALEOMAGNETIC RESULTS FROM SHARILYN FORMATION, SOUTHERN MONGOLIA, AMURIA BLOCK, AND THEIR IMPLICATIONS FOR THE TECTONIC EVOLUTION OF THE MONGOL–OKHOTSK SUTURE

The Amuria block occupies the eastern part of the Central Asian Orogenic Belt between the Siberia craton and the North China block (NCB) and bears important information to understand the evolution of the MongolOkhotsk suture and the amalgamation of East Asia. However, the paleomagnetic database of Amuria remains very poor.The Amuria block occupies the eastern part of the Central Asian Orogenic Belt between the Siberia craton and the North China block (NCB) and bears important information to understand the evolution of the MongolOkhotsk suture and the amalgamation of East Asia

Dynamic Budget Throttling in Repeated Second-Price Auctions

Throttling is one of the most popular budget control methods in today's online advertising markets. When a budget-constrained advertiser employs throttling, she can choose whether or not to participate in an auction after the advertising platform recommends a bid. This paper focuses on the dynamic budget throttling process in repeated second-price auctions from a theoretical view. An essential feature of the underlying problem is that the advertiser does not know the distribution of the highest competing bid upon entering the market. To model the difficulty of eliminating such uncertainty, we consider two different information structures. The advertiser could obtain the highest competing bid in each round with full-information feedback. Meanwhile, with partial information feedback, the advertiser could only have access to the highest competing bid in the auctions she participates in. We propose the OGD-CB algorithm, which involves simultaneous distribution learning and revenue optimization. In both settings, we demonstrate that this algorithm guarantees an $O(\sqrt{T\log T})$ regret with probability $1 - O(1/T)$ relative to the fluid adaptive throttling benchmark. By proving a lower bound of $\Omega(\sqrt{T})$ on the minimal regret for even the hindsight optimum, we establish the near optimality of our algorithm. Finally, we compare the fluid optimum of throttling to that of pacing, another widely adopted budget control method. The numerical relationship of these benchmarks sheds new light on the understanding of different online algorithms for revenue maximization under budget constraints.Comment: 29 pages, 1 tabl