pwc-archive/papers-with-abstracts huggingface dataset:
Sum-Rate Maximization in Distributed Intelligent Reflecting Surfaces-Aided mmWave Communications
Authors: Yue Xiu, Wei Sun, Jiao Wu, Guan Gui, Ning Wei, Zhongpei Zhang
Date: 2021-01-18 00:00:00

In this paper, we focus on the sum-rate optimization in a multi-user millimeter-wave (mmWave) system with distributed intelligent reflecting surfaces (D-IRSs), where a base station (BS) communicates with users via multiple IRSs. The BS transmit beamforming, IRS switch vector, and phase shifts of the IRS are jointly optimized to maximize the sum-rate under minimum user rate, unit-modulus, and transmit power constraints. To solve the resulting non-convex optimization problem, we develop an efficient alternating optimization (AO) algorithm. Specifically, the non-convex problem is converted into three subproblems, which are solved alternatively. The solution to transmit beamforming at the BS and the phase shifts at the IRS are derived by using the successive convex approximation (SCA)-based algorithm, and a greedy algorithm is proposed to design the IRS switch vector. The complexity of the proposed AO algorithm is analyzed theoretically. Numerical results show that the D-IRSs-aided scheme can significantly improve the sum-rate and energy efficiency performance.
Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands
Authors: Ricardo Martinez-Garcia, Ciro Cabal, Justin M. Calabrese, Emilio Hernández-García, Corina E. Tarnita, Cristóbal López, Juan A. Bonachela
Date: 2021-01-18 00:00:00

Self-organized spatial patterns of vegetation are frequent in drylands and, because pattern shape correlates with water availability, they have been suggested as important indicators of ecosystem health. However, the mechanisms underlying pattern emergence remain unclear. Some theories hypothesize that patterns could result from a water-mediated scale-dependent feedback (SDF) whereby interactions favoring plant growth dominate at short distances and growth-inhibitory interactions dominate in the long range. However, we know little about how the presence of a focal plant affects the fitness of its neighbors as a function of the inter-individual distance, which is expected to be highly ecosystem-dependent. This lack of empirical knowledge and system dependency challenge the relevance of SDF as a unifying theory for vegetation pattern formation. Assuming that plant interactions are always inhibitory and only their intensity is scale-dependent, alternative theories also recover the typical vegetation patterns observed in nature. Importantly, although these alternative hypotheses lead to visually indistinguishable patterns, they predict contrasting desertification dynamics, which questions the potential use of vegetation patterns as ecosystem-state indicators. To help resolve this issue, we first review existing theories for vegetation self-organization and their conflicting predictions about desertification dynamics. Second, we discuss potential empirical tests via manipulative experiments to identify pattern-forming mechanisms and link them to specific desertification dynamics. A comprehensive view of models, the mechanisms they intend to capture, and experiments to test them in the field will help to better understand both how patterns emerge and improve predictions on the fate of the ecosystems where they form.
Federated Learning Based Proactive Handover in Millimeter-wave Vehicular Networks
Authors: Kaiqiang Qi, Tingting Liu, Chenyang Yang
Date: 2021-01-18 00:00:00
Tasks: Federated Learning

Proactive handover can avoid frequent handovers and reduce handover delay, which plays an important role in maintaining the quality of service (QoS) for mobile users in millimeter-wave vehicular networks. To reduce the communication cost of training the learning model for proactive handover, we propose a federated learning (FL) framework. The proposed FL framework can accommodate the limited storage capacity of each user, increase the number of users who participate in the FL, and adapt to the dynamic mobility pattern. Simulation results validate the effectiveness of the proposed FL framework. Compared to reactive handover schemes, the proposed handover scheme can reduce unnecessary handovers and improve the QoS of users simultaneously.
Estimation of the Frequency of Occurrence of Italian Phonemes in Text
Authors: Javi Arango, Alec DeCaprio, Sunwoo Baik, Luca De Nardis, Stefanie Shattuck-Hufnagel, Maria Gabriella Di Benedetto
Date: 2021-01-14 00:00:00

The purpose of this project was to derive a reliable estimate of the frequency of occurrence of the 30 phonemes - plus consonant geminated counterparts - of the Italian language, based on four selected written texts. Since no comparable dataset was found in previous literature, the present analysis may serve as a reference in future studies. Four textual sources were considered: Come si fa una tesi di laurea: le materie umanistiche by Umberto Eco, I promessi sposi by Alessandro Manzoni, a recent article in Corriere della Sera (a popular daily Italian newspaper), and In altre parole by Jhumpa Lahiri. The sources were chosen to represent varied genres, subject matter, time periods, and writing styles. Results of the analysis, which also included an analysis of variance, showed that, for all four sources, the frequencies of occurrence reached relatively stable values after about 6,000 phonemes (approx. 1,250 words), varying by <0.025%. Estimated frequencies are provided for each single source and as an average across sources.
Lecture notes on high-dimensional data
Authors: Sven-Ake Wegner
Date: 2021-01-14 00:00:00

These are lecture notes based on the first part of a course on 'Mathematical Data Science', which I taught to final year BSc students in the UK in 2019-2020. Topics include: concentration of measure in high dimensions; Gaussian random vectors in high dimensions; random projections; separation/disentangling of Gaussian data. A revised version has been published as part of the textbook [Mathematical Introduction to Data Science, Springer, Berlin, Heidelberg, 2024, https://link.springer.com/book/10.1007/978-3-662-69426-8].
Least resolved trees for two-colored best match graphs
Authors: David Schaller, Manuela Geiß, Marc Hellmuth, Peter F. Stadler
Date: 2021-01-18 00:00:00
Tasks: Vocal Bursts Valence Prediction

2-colored best match graphs (2-BMGs) form a subclass of sink-free bi-transitive graphs that appears in phylogenetic combinatorics. There, 2-BMGs describe evolutionarily most closely related genes between a pair of species. They are explained by a unique least resolved tree (LRT). Introducing the concept of support vertices we derive an $O(|V|+|E|\log^2|V|)$-time algorithm to recognize 2-BMGs and to construct its LRT. The approach can be extended to also recognize binary-explainable 2-BMGs with the same complexity. An empirical comparison emphasizes the efficiency of the new algorithm.
GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery
Authors: Takuma Yagi, Takumi Nishiyasu, Kunimasa Kawasaki, Moe Matsuki, Yoichi Sato
Date: 2021-01-18 00:00:00
Tasks: Object, Object Discovery

People spend an enormous amount of time and effort looking for lost objects. To help remind people of the location of lost objects, various computational systems that provide information on their locations have been developed. However, prior systems for assisting people in finding objects require users to register the target objects in advance. This requirement imposes a cumbersome burden on the users, and the system cannot help remind them of unexpectedly lost objects. We propose GO-Finder ("Generic Object Finder"), a registration-free wearable camera based system for assisting people in finding an arbitrary number of objects based on two key features: automatic discovery of hand-held objects and image-based candidate selection. Given a video taken from a wearable camera, Go-Finder automatically detects and groups hand-held objects to form a visual timeline of the objects. Users can retrieve the last appearance of the object by browsing the timeline through a smartphone app. We conducted a user study to investigate how users benefit from using GO-Finder and confirmed improved accuracy and reduced mental load regarding the object search task by providing clear visual cues on object locations.
Magnification Generalization for Histopathology Image Embedding
Authors: Milad Sikaroudi, Benyamin Ghojogh, Fakhri Karray, Mark Crowley, H. R. Tizhoosh
Date: 2021-01-18 00:00:00
Tasks: Breast Cancer Histology Image Classification, Classification Of Breast Cancer Histology Images, Domain Adaptation, Domain Generalization, Histopathological Image Classification, Meta-Learning

Histopathology image embedding is an active research area in computer vision. Most of the embedding models exclusively concentrate on a specific magnification level. However, a useful task in histopathology embedding is to train an embedding space regardless of the magnification level. Two main approaches for tackling this goal are domain adaptation and domain generalization, where the target magnification levels may or may not be introduced to the model in training, respectively. Although magnification adaptation is a well-studied topic in the literature, this paper, to the best of our knowledge, is the first work on magnification generalization for histopathology image embedding. We use an episodic trainable domain generalization technique for magnification generalization, namely Model Agnostic Learning of Semantic Features (MASF), which works based on the Model Agnostic Meta-Learning (MAML) concept. Our experimental results on a breast cancer histopathology dataset with four different magnification levels show the proposed method's effectiveness for magnification generalization.
MONAH: Multi-Modal Narratives for Humans to analyze conversations
Authors: Joshua Y. Kim, Greyson Y. Kim, Chunfeng Liu, Rafael A. Calvo, Silas C. R. Taylor, Kalina Yacef
Date: 2021-01-18 00:00:00
Proceeding: EACL 2021 2
Tasks: Emotion Recognition in Conversation, Feature Engineering, Text Generation

In conversational analyses, humans manually weave multimodal information into the transcripts, which is significantly time-consuming. We introduce a system that automatically expands the verbatim transcripts of video-recorded conversations using multimodal data streams. This system uses a set of preprocessing rules to weave multimodal annotations into the verbatim transcripts and promote interpretability. Our feature engineering contributions are two-fold: firstly, we identify the range of multimodal features relevant to detect rapport-building; secondly, we expand the range of multimodal annotations and show that the expansion leads to statistically significant improvements in detecting rapport-building.
Automatic punctuation restoration with BERT models
Authors: Attila Nagy, Bence Bial, Judit Ács
Date: 2021-01-18 00:00:00
Tasks: Punctuation Restoration

We present an approach for automatic punctuation restoration with BERT models for English and Hungarian. For English, we conduct our experiments on Ted Talks, a commonly used benchmark for punctuation restoration, while for Hungarian we evaluate our models on the Szeged Treebank dataset. Our best models achieve a macro-averaged $F_1$-score of 79.8 in English and 82.2 in Hungarian. Our code is publicly available.
Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning
Authors: Austin W. Hanjie, Victor Zhong, Karthik Narasimhan
Date: 2021-01-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL), Zero-shot Generalization

We investigate the use of natural language to drive the generalization of control policies and introduce the new multi-task environment Messenger with free-form text manuals describing the environment dynamics. Unlike previous work, Messenger does not assume prior knowledge connecting text and state observations $-$ the control policy must simultaneously ground the game manual to entity symbols and dynamics in the environment. We develop a new model, EMMA (Entity Mapper with Multi-modal Attention) which uses an entity-conditioned attention module that allows for selective focus over relevant descriptions in the manual for each entity in the environment. EMMA is end-to-end differentiable and learns a latent grounding of entities and dynamics from text to observations using only environment rewards. EMMA achieves successful zero-shot generalization to unseen games with new dynamics, obtaining a 40% higher win rate compared to multiple baselines. However, win rate on the hardest stage of Messenger remains low (10%), demonstrating the need for additional work in this direction.
Exploring Lexical Irregularities in Hypothesis-Only Models of Natural Language Inference
Authors: Qingyuan Hu, Yi Zhang, Kanishka Misra, Julia Rayz
Date: 2021-01-19 00:00:00
Tasks: Natural Language Inference, Natural Language Understanding, RTE

Natural Language Inference (NLI) or Recognizing Textual Entailment (RTE) is the task of predicting the entailment relation between a pair of sentences (premise and hypothesis). This task has been described as a valuable testing ground for the development of semantic representations, and is a key component in natural language understanding evaluation benchmarks. Models that understand entailment should encode both, the premise and the hypothesis. However, experiments by Poliak et al. revealed a strong preference of these models towards patterns observed only in the hypothesis, based on a 10 dataset comparison. Their results indicated the existence of statistical irregularities present in the hypothesis that bias the model into performing competitively with the state of the art. While recast datasets provide large scale generation of NLI instances due to minimal human intervention, the papers that generate them do not provide fine-grained analysis of the potential statistical patterns that can bias NLI models. In this work, we analyze hypothesis-only models trained on one of the recast datasets provided in Poliak et al. for word-level patterns. Our results indicate the existence of potential lexical biases that could contribute to inflating the model performance.
Inferring COVID-19 Biological Pathways from Clinical Phenotypes via Topological Analysis
Authors: Negin Karisani, Daniel E. Platt, Saugata Basu, Laxmi Parida
Date: 2021-01-19 00:00:00

COVID-19 has caused thousands of deaths around the world and also resulted in a large international economic disruption. Identifying the pathways associated with this illness can help medical researchers to better understand the properties of the condition. This process can be carried out by analyzing the medical records. It is crucial to develop tools and models that can aid researchers with this process in a timely manner. However, medical records are often unstructured clinical notes, and this poses significant challenges to developing the automated systems. In this article, we propose a pipeline to aid practitioners in analyzing clinical notes and revealing the pathways associated with this disease. Our pipeline relies on topological properties and consists of three steps: 1) pre-processing the clinical notes to extract the salient concepts, 2) constructing a feature space of the patients to characterize the extracted concepts, and finally, 3) leveraging the topological properties to distill the available knowledge and visualize the result. Our experiments on a publicly available dataset of COVID-19 clinical notes testify that our pipeline can indeed extract meaningful pathways.
Single versus Multiple Annotation for Named Entity Recognition of Mutations
Authors: David Martinez Iraola, Antonio Jimeno Yepes
Date: 2021-01-19 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The focus of this paper is to address the knowledge acquisition bottleneck for Named Entity Recognition (NER) of mutations, by analysing different approaches to build manually-annotated data. We address first the impact of using a single annotator vs two annotators, in order to measure whether multiple annotators are required. Once we evaluate the performance loss when using a single annotator, we apply different methods to sample the training data for second annotation, aiming at improving the quality of the dataset without requiring a full pass. We use held-out double-annotated data to build two scenarios with different types of rankings: similarity-based and confidence based. We evaluate both approaches on: (i) their ability to identify training instances that are erroneous (cases where single-annotator labels differ from double-annotation after discussion), and (ii) on Mutation NER performance for state-of-the-art classifiers after integrating the fixes at different thresholds.
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data
Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang
Date: 2021-01-19 00:00:00
Tasks: Multi-Task Learning, Representation Learning, Self-Supervised Learning, speech-recognition, Speech Recognition, Speech Representation Learning, Transfer Learning

In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.
Situation and Behavior Understanding by Trope Detection on Films
Authors: Chen-Hsi Chang, Hung-Ting Su, Jui-heng Hsu, Yu-Siang Wang, Yu-Cheng Chang, Zhe Yu Liu, Ya-Liang Chang, Wen-Feng Cheng, Ke-Jyun Wang, Winston H. Hsu
Date: 2021-01-19 00:00:00
Tasks: Reading Comprehension, Sentence, TAG

The human ability of deep cognitive skills are crucial for the development of various real-world applications that process diverse and abundant user generated input. While recent progress of deep learning and natural language processing have enabled learning system to reach human performance on some benchmarks requiring shallow semantics, such human ability still remains challenging for even modern contextual embedding models, as pointed out by many recent studies. Existing machine comprehension datasets assume sentence-level input, lack of casual or motivational inferences, or could be answered with question-answer bias. Here, we present a challenging novel task, trope detection on films, in an effort to create a situation and behavior understanding for machines. Tropes are storytelling devices that are frequently used as ingredients in recipes for creative works. Comparing to existing movie tag prediction tasks, tropes are more sophisticated as they can vary widely, from a moral concept to a series of circumstances, and embedded with motivations and cause-and-effects. We introduce a new dataset, Tropes in Movie Synopses (TiMoS), with 5623 movie synopses and 95 different tropes collecting from a Wikipedia-style database, TVTropes. We present a multi-stream comprehension network (MulCom) leveraging multi-level attention of words, sentences, and role relations. Experimental result demonstrates that modern models including BERT contextual embedding, movie tag prediction systems, and relational networks, perform at most 37% of human performance (23.97/64.87) in terms of F1 score. Our MulCom outperforms all modern baselines, by 1.5 to 5.0 F1 score and 1.5 to 3.0 mean of average precision (mAP) score. We also provide a detailed analysis and human evaluation to pave ways for future research.
Challenges for Computational Lexical Semantic Change
Authors: Simon Hengchen, Nina Tahmasebi, Dominik Schlechtweg, Haim Dubossarsky
Date: 2021-01-19 00:00:00

The computational study of lexical semantic change (LSC) has taken off in the past few years and we are seeing increasing interest in the field, from both computational sciences and linguistics. Most of the research so far has focused on methods for modelling and detecting semantic change using large diachronic textual data, with the majority of the approaches employing neural embeddings. While methods that offer easy modelling of diachronic text are one of the main reasons for the spiking interest in LSC, neural models leave many aspects of the problem unsolved. The field has several open and complex challenges. In this chapter, we aim to describe the most important of these challenges and outline future directions.
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach
Authors: ASHISH SHARMA, Inna W. Lin, Adam S. Miner, David C. Atkins, Tim Althoff
Date: 2021-01-19 00:00:00
Tasks: Deep Reinforcement Learning, Dialogue Generation, Language Modelling, reinforcement-learning, Reinforcement Learning (RL), Sentence, Specificity, Style Transfer

Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden. Psychologists have repeatedly demonstrated that empathy, the ability to understand and feel the emotions and experiences of others, is a key component leading to positive outcomes in supportive conversations. However, recent studies have shown that highly empathic conversations are rare in online mental health platforms. In this paper, we work towards improving empathy in online mental health support conversations. We introduce a new task of empathic rewriting which aims to transform low-empathy conversational posts to higher empathy. Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context. Here we propose PARTNER, a deep reinforcement learning agent that learns to make sentence-level edits to posts in order to increase the expressed level of empathy while maintaining conversation quality. Our RL agent leverages a policy network, based on a transformer language model adapted from GPT-2, which performs the dual task of generating candidate empathic sentences and adding those sentences at appropriate positions. During training, we reward transformations that increase empathy in posts while maintaining text fluency, context specificity and diversity. Through a combination of automatic and human evaluation, we demonstrate that PARTNER successfully generates more empathic, specific, and diverse responses and outperforms NLP methods from related tasks like style transfer and empathic dialogue generation. Our work has direct implications for facilitating empathic conversations on web-based platforms.