pwc-archive/papers-with-abstracts huggingface dataset:
Prospector: a mobile app for high-throughput NIRS phenotyping
Authors: Trevor W. Rife, Chaney Courtney, Jenna Hershberger, Brandon Shaver, Michael A. Gore, Mitchell Neilsen, Jesse A. Poland
Date: 2021-04-14 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Quality traits are some of the most important and time-consuming phenotypes to evaluate in plant breeding programs. These traits are often evaluated late in the breeding pipeline due to their cost, resulting in the potential advancement of many lines that are not suitable for release. Near-infrared spectroscopy (NIRS) is a non-destructive tool that can rapidly increase the speed at which quality traits are evaluated. However, most spectrometers are non-portable or prohibitively expensive. Recent advancements have led to the development of consumer-targeted, inexpensive spectrometers with demonstrated potential for breeding applications. Unfortunately, the mobile applications for these spectrometers are not designed to rapidly collect organized samples at the scale necessary for breeding programs. To that end, we developed Prospector, a mobile application that connects with LinkSquare portable NIR spectrometers and allows breeders to efficiently capture NIR data. In this report, we outline the core functionality of the app and how it can easily be integrated into breeding workflows as well as the opportunities for further development. Prospector and other high throughput phenotyping tools and technologies are required for plant breeders to develop the next generation of improved varieties necessary to feed a growing global population.
Fabula Entropy Indexing: Objective Measures of Story Coherence
Authors: Louis Castricato, Spencer Frazier, Jonathan Balloch, Mark Riedl
Date: 2021-03-23 00:00:00
Proceeding: NAACL (NUSE) 2021 6
Tasks: Question Answering, Story Generation

Automated story generation remains a difficult area of research because it lacks strong objective measures. Generated stories may be linguistically sound, but in many cases suffer poor narrative coherence required for a compelling, logically-sound story. To address this, we present Fabula Entropy Indexing (FEI), an evaluation method to assess story coherence by measuring the degree to which human participants agree with each other when answering true/false questions about stories. We devise two theoretically grounded measures of reader question-answering entropy, the entropy of world coherence (EWC), and the entropy of transitional coherence (ETC), focusing on global and local coherence, respectively. We evaluate these metrics by testing them on human-written stories and comparing against the same stories that have been corrupted to introduce incoherencies. We show that in these controlled studies, our entropy indices provide a reliable objective measure of story coherence.
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages
Authors: El Moatez Billah Nagoudi, Wei-Rui Chen, Muhammad Abdul-Mageed, Hasan Cavusogl
Date: 2021-04-04 00:00:00
Proceeding: NAACL (AmericasNLP) 2021 6
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Transformer language models have become fundamental components of natural language processing based pipelines. Although several Transformer models have been introduced to serve many languages, there is a shortage of models pre-trained for low-resource and Indigenous languages. In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build IndCorpus--a new dataset for ten Indigenous languages and Spanish. We also present the application of IndT5 to machine translation by investigating different approaches to translate between Spanish and the Indigenous languages as part of our contribution to the AmericasNLP 2021 Shared Task on Open Machine Translation. IndT5 and IndCorpus are publicly available for research
Maximum Entropy Auto-Encoding
Authors: Paul M Baggenstoss
Date: 2021-04-13 00:00:00
Tasks: Image Reconstruction

In this paper, it is shown that an auto-encoder using optimal reconstruction significantly outperforms a conventional auto-encoder. Optimal reconstruction uses the conditional mean of the input given the features, under a maximum entropy prior distribution. The optimal reconstruction network, which is called deterministic projected belied network (D-PBN), resembles a standard reconstruction network, but with special non-linearities that mist be iteratively solved. The method, which can be seen as a generalization of maximum entropy image reconstruction, extends to multiple layers. In experiments, mean square reconstruction error reduced by up to a factor of two. The performance improvement diminishes for deeper networks, or for input data with unconstrained values (Gaussian assumption).
Coupled Training of Sequence-to-Sequence Models for Accented Speech Recognition
Authors: Vinit Unni, Nitish Joshi, Preethi Jyothi
Date: 2020-05-14 00:00:00
Tasks: Accented Speech Recognition, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

Accented speech poses significant challenges for state-of-the-art automatic speech recognition (ASR) systems. Accent is a property of speech that lasts throughout an utterance in varying degrees of strength. This makes it hard to isolate the influence of accent on individual speech sounds. We propose coupled training for encoder-decoder ASR models that acts on pairs of utterances corresponding to the same text spoken by speakers with different accents. This training regime introduces an L2 loss between the attention-weighted representations corresponding to pairs of utterances with the same text, thus acting as a regularizer and encouraging representations from the encoder to be more accent-invariant. We focus on recognizing accented English samples from the Mozilla Common Voice corpus. We obtain significant error rate reductions on accented samples from a large set of diverse accents using coupled training. We also show consistent improvements in performance on heavily accented samples (as determined by a standalone accent classifier).
Disentangling the socio-ecological drivers behind illegal fishing in a small-scale fishery managed by a TURF system
Authors: Silvia de Juan, Maria Dulce Subida, Andres Ospina-Alvarez, Ainara Aguilar, Miriam Fernandez
Date: 2020-12-15 00:00:00
Tasks: Management

A substantial increase in illegal extraction of the benthic resources in central Chile is likely driven by an interplay of numerous socio-economic local factors that threatens the success of the fisheries management areas (MA) system. To assess this problem, the exploitation state of a commercially important benthic resource (i.e., keyhole limpet) in the MAs was related with socio-economic drivers of the small-scale fisheries. The potential drivers of illegal extraction included rebound effect of fishing effort displacement by MAs, level of enforcement, distance to surveillance authorities, wave exposure and land-based access to the MA, and alternative economic activities in the fishing village. The exploitation state of limpets was assessed by the proportion of the catch that is below the minimum legal size, with high proportions indicating a poor state, and by the relative median size of limpets fished within the MAs in comparison with neighbouring OA areas, with larger relative sizes in the MA indicating a good state. A Bayesian-Belief Network approach was adopted to assess the effects of potential drivers of illegal fishing on the status of the benthic resource in the MAs. Results evidenced the absence of a direct link between the level of enforcement and the status of the resource, with other socio-economic (e.g., alternative economic activities in the village) and context variables (e.g., fishing effort or distance to surveillance authorities) playing important roles. Scenario analysis explored variables that are susceptible to be managed, evidencing that BBN is a powerful approach to explore the role of multiple external drivers, and their impact on marine resources, in complex small-scale fisheries.
Automatic Detection of Everyday Social Behaviours and Environments from Verbatim Transcripts of Daily Conversations
Authors: Kristina Y. Yordanova, Burcu Demiray, Matthias R. Mehl, Mike Martin
Date: 2019-07-22 00:00:00

Coding in social sciences is a process that involves the categorisation of qualitative or quantitative data in order to facilitate further analysis. Coding is usually a manual process that involves a lot of effort and time to produce codes with high validity and interrater reliability. Although automated methods for quantitative data analysis are largely used in social sciences, there are only a few attempts at automatically or semi- automatically coding the data collected in qualitative studies. To address this problem, in this work we propose an approach for automated coding of social behaviours and environments based on verbatim transcriptions of everyday conversations. To evaluate the approach, we analysed the transcripts from three datasets containing recordings of everyday conversations from: (1) young healthy adults (German transcriptions), (2) elderly healthy adults (German transcriptions), and (3) young healthy adults (English transcriptions). The results show that it is possible to automatically code the social behaviours and environments based on verbatim transcripts of the recorded conversations. This could reduce the time and effort researchers need to assign accurate codes to transcribed conversations.
Index Terms—social behaviour analysis, natural language pro- cessing, automated coding
Calibrating the Nonlinear Matter Power Spectrum: Requirements for Future Weak Lensing Surveys
Authors: Dragan Huterer, Masahiro Takada
Date: 2004-12-06 00:00:00
Tasks: Clustering

Uncertainties in predicting the nonlinear clustering of matter are among the most serious theoretical systematics facing the upcoming wide-field weak gravitational lensing surveys. We estimate the accuracy with which the matter power spectrum will need to be calibrated in order not to contribute appreciably to the error budget for future weak lensing surveys. We consider the random statistical errors and the systematic biases in P(k), as well as some estimates based on current N-body simulations. While the power spectrum on relevant scales (0.1 < k/h Mpc^{-1} < 10) is currently calibrated with N-body simulations to about 5-10%, in the future it will have to be calibrated to about 1-2% accuracy, depending on the specifications of the survey. Encouragingly, we find that even the worst-case error that mimics the effect of cosmological parameters needs to be calibrated to no better than about 0.5-1%. These goals require a suite of high resolution N-body simulations on a relatively fine grid in cosmological parameter space, and should be achievable in the near future.
Magnification-Temperature Correlation: the Dark Side of ISW Measurements
Authors: Marilena LoVerde, Lam Hui, Enrique Gaztanaga
Date: 2006-11-16 00:00:00

Integrated Sachs-Wolfe (ISW) measurements, which involve cross-correlating the CMB with the foreground large-scale structure (e.g. galaxies/quasars), have proven to be an interesting probe of dark energy. We show that magnification bias, which is the inevitable modulation of the foreground number counts by gravitational lensing, alters both the shape and amplitude of the observed ISW signal. This is true especially at high redshifts because (1) the intrinsic galaxy-temperature signal diminishes greatly back in the matter dominated era, (2) the lensing efficiency increases with redshift and (3) the number count slope generally steepens with redshift in a magnitude limited sample. At z >~ 2, the magnification-temperature correlation dominates over the intrinsic galaxy-temperature correlation and causes the observed ISW signal to increase with z, despite dark energy subdominance -- a result of the fact that magnification probes structures between the observer and the sources. Ignoring magnification bias can then lead to erroneous conclusions about dark energy. While the lensing modulation opens up an interesting high z window for ISW measurements, high z measurements are not expected to add much new information to low z ones if dark energy is the cosmological constant. This is because lensing introduces significant covariance across redshifts. The most compelling reason to pursue high z ISW measurements is to look for a potential surprise such as early dark energy domination or the signature of modified gravity. We conclude with a discussion of existing measurements, the highest z of which is at the margin of being sensitive to magnification bias. We also develop a formalism which might be of general interest: to predict biases in estimating parameters when certain physical effects are ignored in interpreting data.
Systematic Bias in Cosmic Shear: Beyond the Fisher Matrix
Authors: Adam Amara, Alexandre Refregier
Date: 2007-10-26 00:00:00
Tasks: parameter estimation, Survey

We describe a method for computing the biases that systematic signals introduce in parameter estimation using a simple extension of the Fisher matrix formalism. This allows us to calculate the offset of the best fit parameters relative to the fiducial model, in addition to the usual statistical error ellipse. As an application, we study the impact that residual systematics in tomographic weak lensing measurements. In particular we explore three different types of shape measurement systematics: (i) additive systematic with no redshift evolution; (ii) additive systematic with redshift evolution; and (iii) multiplicative systematic. In each case, we consider a wide range of scale dependence and redshift evolution of the systematics signal. For a future DUNE-like full sky survey, we find that, for cases with mild redshift evolution, the variance of the additive systematic signal should be kept below 10^-7 to ensure biases on cosmological parameters that are sub-dominant to the statistical errors. For the multiplicative systematics, which depends on the lensing signal, we find the multiplicative calibration m0 needs to be controlled to an accuracy better than 10^-3. We find that the impact of systematics can be underestimated if their assumes redshift dependence is too simplistic. We provide simple scaling relations to extend these requirements to any survey geometry and discuss the impact of our results for current and future weak lensing surveys.
Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
Authors: Shuquan Ye; Dongdong Chen; Songfang Han; Ziyu Wan; Jing Liao
Date: 2021-02-09 00:00:00
Tasks: Point Cloud Super Resolution, point cloud upsampling

Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the existing methods regard point cloud upsampling of different scale factors as independent tasks. Thus, the methods need to train a specific model for each scale factor, which is both inefficient and impractical for storage and computation in real applications. To address this limitation, in this work, we propose a novel method called ``Meta-PU" to firstly support point cloud upsampling of arbitrary scale factors with a single model. In the Meta-PU method, besides the backbone network consisting of residual graph convolution (RGC) blocks, a meta-subnetwork is learned to adjust the weights of the RGC blocks dynamically, and a farthest sampling block is adopted to sample different numbers of points. Together, these two blocks enable our Meta-PU to continuously upsample the point cloud with arbitrary scale factors by using only a single model. In addition, the experiments reveal that training on multiple scales simultaneously is beneficial to each other. Thus, Meta-PU even outperforms the existing methods trained for a specific scale factor only.
Breast cancer image classification on WSI with spatial correlations
Authors: Ye Jiandong, Luo Yihao, Zhu Chuang, Liu Fang, Zhang Yue
Date: 2019-05-12 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

As common cancer, breast cancer kills thousands of women every year. It's significant to provide doctors computer-aided diagnosis (CAD) to ease their workload as well as improve detection quality. Patch-level CNNs are usually used to classify the breast tissue slice, and the CNNs classify each patch independently ignoring the spatial correlations, resulting in wrong isolated label map. However, the probability distribution of cancer type is related to their adjacent patches. In this paper, we propose a framework integrating CNN and filter algorithm aimed at extracting spatial information and improving the performance of the classification. The network was trained on a breast cancer dataset provided by ICIAR18. For 4-class classification, compared to CNN methods without using spatial correlations, the proposed method achieved about 10% improvement on accuracy over the validation dataset and get smoother probability maps. Our experiments also show that larger kernel size gets better performance.
Alignment Enhancement Network for Fine-grained Visual Categorization
Authors: Yutao Hu
Date: 2021-03-01 00:00:00
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Categorization

Fine-grained visual categorization (FGVC) aims to automatically recognize objects from different sub-ordinate categories. Despite attracting considerable attention from both academia and industry, it remains a challenging task due to subtle visual differences among different classes. Cross-layer feature aggregation and crossimage pairwise learning become prevailing in improving the performance of FGVC by extracting discriminative class-specific features. However, they are still inefficient to fully use the cross-layer information based on the simple aggregation strategy, while existing pairwise learning methods also fail to explore long-range interactions between different images. To address these problems, we propose a novel Alignment Enhancement Network (AENet), including two-level alignments, Cross-layer Alignment (CLA) and Cross-image Alignment (CIA). The CLA module exploits the cross-layer relationship between low-level spatial information and highlevel semantic information, which contributes to cross-layer feature aggregation to improve the capacity of feature representation for input images. The new CIA module is further introduced to produce the aligned feature map, which can enhance the relevant information as well as suppress the irrelevant information across the whole spatial region. Our method is based on an underlying assumption that the aligned feature map should be closer to the inputs of CIA when they belong to the same category. Accordingly, we establish Semantic Affinity Loss to supervise the feature alignment within each CIA block. Experimental results on four challenging datasets show that the proposed AENet achieves the state-of-the-art results over prior arts.
Disentangled Self-Supervision in Sequential Recommenders
Authors: Jianxin Ma, Chang Zhou, Hongxia Yang, Peng Cui, Xin Wang, Wenwu Zhu
Date: 2020-08-23 00:00:00
Tasks: Disentanglement

To learn a sequential recommender, the existing methods typically adopt the sequence-to-item (seq2item) training strategy, which supervises a sequence model with a user’s next behavior as the label and the user’s past behaviors as the input. The seq2item strategy, however, is myopic and usually produces non-diverse recommendation lists. In this paper, we study the problem of mining extra signals for supervision by looking at the longer-term future. There exist two challenges: i) reconstructing a future sequence containing many behaviors is exponentially harder than reconstructing a single next behavior, which can lead to difficulty in convergence, and ii) the sequence of all future behaviors can involve many intentions, not all of which may be predictable from the sequence of earlier behaviors. To address these challenges, we propose a sequence-to-sequence (seq2seq) training strategy based on latent self-supervision and disentanglement. Specifically, we perform self-supervision in the latent space, i.e., reconstructing the representation of the future sequence as a whole, instead of reconstructing the items in the future sequence individually. We also disentangle the intentions behind any given sequence of behaviors and construct seq2seq training samples using only pairs of sub-sequences that involve a shared intention. Results on real-world benchmarks and synthetic data demonstrate the improvement brought by seq2seq training.
A Masked Segmental Language Model for Unsupervised Natural Language Segmentation
Authors: C. M. Downey, Fei Xia, Gina-Anne Levow, Shane Steinert-Threlkeld
Date: 2021-04-16 00:00:00
Proceeding: NAACL (SIGMORPHON) 2022 7
Tasks: Language Modeling, Language Modelling, Segmentation

Segmentation remains an important preprocessing step both in languages where "words" or other important syntactic/semantic units (like morphemes) are not clearly delineated by white space, as well as when dealing with continuous speech data, where there is often no meaningful pause between words. Near-perfect supervised methods have been developed for use in resource-rich languages such as Chinese, but many of the world's languages are both morphologically complex, and have no large dataset of "gold" segmentations into meaningful units. To solve this problem, we propose a new type of Segmental Language Model (Sun and Deng, 2018; Kawakami et al., 2019; Wang et al., 2021) for use in both unsupervised and lightly supervised segmentation tasks. We introduce a Masked Segmental Language Model (MSLM) built on a span-masking transformer architecture, harnessing the power of a bi-directional masked modeling context and attention. In a series of experiments, our model consistently outperforms Recurrent SLMs on Chinese (PKU Corpus) in segmentation quality, and performs similarly to the Recurrent model on English (PTB). We conclude by discussing the different challenges posed in segmenting phonemic-type writing systems.
Contrastive Learning with Stronger Augmentations
Authors: Xiao Wang, Guo-Jun Qi
Date: 2021-04-15 00:00:00
Tasks: Contrastive Learning, Representation Learning, Retrieval, Self-Supervised Image Classification, Self-Supervised Learning

Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods have benefited from various data augmentations that are carefully designated to maintain their identities so that the images transformed from the same instance can still be retrieved. However, those carefully designed transformations limited us to further explore the novel patterns exposed by other transformations. Meanwhile, as found in our experiments, the strong augmentations distorted the images' structures, resulting in difficult retrieval. Thus, we propose a general framework called Contrastive Learning with Stronger Augmentations~(CLSA) to complement current contrastive learning approaches. Here, the distribution divergence between the weakly and strongly augmented images over the representation bank is adopted to supervise the retrieval of strongly augmented queries from a pool of instances. Experiments on the ImageNet dataset and downstream datasets showed the information from the strongly augmented images can significantly boost the performance. For example, CLSA achieves top-1 accuracy of 76.2% on ImageNet with a standard ResNet-50 architecture with a single-layer classifier fine-tuned, which is almost the same level as 76.5% of supervised results. The code and pre-trained models are available in https://github.com/maple-research-lab/CLSA.