Evolvability of the Skull: A Study of Genetic Basis and Integration in the Teleost Craniofacial Skeleton

As the field of evolutionary biology pivots away from a gene-centric view of how adaptive evolution proceeds, renewed emphasis is placed on the origin of phenotypic variation. Understanding the developmental processes that underlie the production of novel traits, and how they might influence evolvability, is considered a primary goal in the on-going “extended evolutionary synthesis”. The following dissertation explores these questions in the context of adaptive radiations in fish, with a focus on morphological variation in the craniofacial skeleton. Specifically, the first chapter investigates the genetic and developmental basis of shape (co-)variation in the feeding apparatus of African cichlid fishes, and uncovers a common signaling pathway that underlies the adaptive evolution of multiple elements in a complex functional structure. The second chapter presents a new method that is capable of evaluating phenotypic integration on the individual level, and demonstrates its utility in genetic mapping studies. The third chapter characterizes the pattern of morphological diversification in the Antarctic notothenioid fishes, and discusses how integration might have facilitated their adaptive radiation in the Southern Ocean

Game of Travesty: Decoy-based Psychological Cyber Deception for Proactive Human Agents

The concept of cyber deception has been receiving emerging attention. The development of cyber defensive deception techniques requires interdisciplinary work, among which cognitive science plays an important role. In this work, we adopt a signaling game framework between a defender and a human agent to develop a cyber defensive deception protocol that takes advantage of the cognitive biases of human decision-making using quantum decision theory to combat insider attacks (IA). The defender deceives an inside human attacker by luring him to access decoy sensors via generators producing perceptions of classical signals to manipulate the human attacker's psychological state of mind. Our results reveal that even without changing the classical traffic data, strategically designed generators can result in a worse performance for defending against insider attackers in identifying decoys than the ones in the deceptive scheme without generators, which generate random information based on input signals. The proposed framework leads to fundamental theories in designing more effective signaling schemes

Quantum Man-in-the-middle Attacks: a Game-theoretic Approach with Applications to Radars

The detection and discrimination of quantum states serve a crucial role in quantum signal processing, a discipline that studies methods and techniques to process signals that obey the quantum mechanics frameworks. However, just like classical detection, evasive behaviors also exist in quantum detection. In this paper, we formulate an adversarial quantum detection scenario where the detector is passive and does not know the quantum states have been distorted by an attacker. We compare the performance of a passive detector with the one of a non-adversarial detector to demonstrate how evasive behaviors can undermine the performance of quantum detection. We use a case study of target detection with quantum radars to corroborate our analytical results

Personal Attribute Prediction from Conversations

Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually labeled utterances are required for model training; (2) personal attribute knowledge embedded in both utterances and external resources is underutilized; (3) the performance on predicting some difficult personal attributes is unsatisfactory. In this paper, we propose a framework DSCGN based on the pre-trained language model with a noise-robust loss function to predict personal attributes from conversations without requiring any labeled utterances. We yield two categories of supervision, i.e., document-level supervision via a distant supervision strategy and contextualized word-level supervision via a label guessing method, by mining the personal attribute knowledge embedded in both unlabeled utterances and external resources to fine-tune the language model. Extensive experiments over two real-world data sets (i.e., a profession data set and a hobby data set) show our framework obtains the best performance compared with all the twelve baselines in terms of nDCG and MRR.Comment: Accepted by WWW'22 Companio

A MICROSCOPIC CHARACTERIZATION OF WETTABILITY IN SHALE KEROGEN WITH VARYING MATURITY LEVELS AND ITS ROLE IN GOVERNING FLUID DISTRIBUTION

Kerogen is defined as the insoluble macromolecular organic matter in sedimentary rocks and is a complex mixture of organic chemical compounds. The process of kerogen maturation is accompanied by the loss of functionalized molecules, leading to a decreased H/C and O/C ratios, as well as a reduction in molecular weight. The degree of thermal maturation is often expressed by the van Krevelen diagram with H/C and O/C ratios as indicators. Even though kerogen pores are widely viewed as hydrocarbon-wetting, some recent experimental work indicates the existence of water in kerogen. It then becomes necessary to evaluate the wettability characteristics of kerogen and to determine the governing factors controlling kerogen pore surface wettability. Addressing these concerns is very essential because wettability is directly related to the dynamics of fluids and is likely to be extremely relevant to developing models for reserves estimates and multiphase flow. Additionally it may provide some answers to the common observation of low recovery of hydraulic fracture water. In this study, pore-scale molecular dynamics simulations are used to understand the relationships between kerogen maturity and its wettability. The modeling approach adopted here includes a proper description of the kerogen pore systems with differing levels of thermal maturity, surface roughness, tortuous paths, and porous nature. Three kerogen models, namely activated kerogen, kerogen free of activated sites and the graphite slit pore are considered in this study. This work examines the sensitivity of the storage of pure water, brine water and a mixture of water and hydrocarbon to pore size, degree of kerogen maturity and pore wall roughness. A quantitative analysis to discuss the dependence of kerogen wettability on maturity degree is also presented. The results indicate that the kerogen models constructed in this study more accurately represent organic pores in comparison to widely used planar graphite slit-pore systems. Confinement of water in kerogen pores is shown to lead to water entrapment and phase changes of water in comparison to its bulk properties. Fluid adsorption on the kerogen surface is observed to be multi-layer, instead of monolayer assumed in the Langmuir adsorption theory. Lastly, the results demonstrate that kerogen maturity governs wettability of organic kerogen pores. Kerogen of intermediate maturity is shown to be characterized by heterogeneous wettability and may lead to trapping of water in organic pores

Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations

Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations. The framework applies contrast-aware adversarial training to generate worst-case samples and uses a joint class-spread contrastive learning objective on both original and adversarial samples. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. To avoid the negative impact of adversarial perturbations on context-dependent data, we design a contextual adversarial training strategy to learn more diverse features from context and enhance the model's context robustness. We develop a sequence-based method SACL-LSTM under this framework, to learn label-consistent and context-robust emotional features for ERC. Experiments on three datasets demonstrate that SACL-LSTM achieves state-of-the-art performance on ERC. Extended experiments prove the effectiveness of the SACL framework.Comment: 16 pages, accepted by ACL 202

Low-resource Personal Attribute Prediction from Conversation

Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extracting personal attribute knowledge from users' conversation data. Given some users of a conversational system, a personal attribute and these users' utterances, our goal is to predict the ranking of the given personal attribute values for each user. Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory. In addition, it is found that some text classification methods could be employed to resolve this task directly. However, they also perform not well over those difficult personal attributes. In this paper, we propose a novel framework PEARL to predict personal attributes from conversations by leveraging the abundant personal attribute knowledge from utterances under a low-resource setting in which no labeled utterances or external data are utilized. PEARL combines the biterm semantic information with the word co-occurrence information seamlessly via employing the updated prior attribute knowledge to refine the biterm topic model's Gibbs sampling process in an iterative manner. The extensive experimental results show that PEARL outperforms all the baseline methods not only on the task of personal attribute prediction from conversations over two data sets, but also on the more general weakly supervised text classification task over one data set.Comment: Accepted by AAAI'2

PSP: Pre-trained Soft Prompts for Few-Shot Abstractive Summarization

Few-shot abstractive summarization has become a challenging task in natural language generation. To support it, we designed a novel soft prompts architecture coupled with a prompt pre-training plus fine-tuning paradigm that is effective and tunes only extremely light parameters. The soft prompts include continuous input embeddings across an encoder and a decoder to fit the structure of the generation models. Importantly, a novel inner-prompt placed in the text is introduced to capture document-level information. The aim is to devote attention to understanding the document that better prompts the model to generate document-related content. The first step in the summarization procedure is to conduct prompt pre-training with self-supervised pseudo-data. This teaches the model basic summarizing capabilities. The model is then fine-tuned with few-shot examples. Experimental results on the CNN/DailyMail and XSum datasets show that our method, with only 0.1% of the parameters, outperforms full-model tuning where all model parameters are tuned. It also surpasses Prompt Tuning by a large margin and delivers competitive results against Prefix-Tuning with 3% of the parameters.Comment: 12 page

Potential roles of smell and taste in the orientation behaviour of coral‐reef fish larvae: insights from morphology

An ontogenetic analysis of the olfactory organ and the number and distribution of internal taste buds was carried out in two neon gobies (Elacatinus lori and Elacatinus colini) with the goal of revealing morphological trends that might inform an understanding of the roles of olfaction and taste in larval orientation behaviour. The pattern of development of the olfactory organ is unremarkable and enclosure of the olfactory epithelium occurs concurrently with metamorphosis and settlement in both species. Like other gobies, juvenile and adult E. lori and E. colini lack complex olfactory lamellae, and lack the accessory nasal sacs present in some adult gobies that could facilitate active olfactory ventilation (i.e., sniffing). A small number of internal taste buds are present at hatch with most found in the caudal region of the buccal cavity (on gill arches, roof of buccal cavity). As taste bud number increases, they demonstrate an anterior spread to the lips, buccal valves and tongue (i.e., tissue covering the basihyal). In the absence of an active ventilatory mechanism for the olfactory organs, the water that moves through the buccal cavity with cyclic gill ventilation may provide chemical cues allowing the internal taste buds to play a role in chemical‐mediated orientation and reef‐seeking behavior in pelagic larval fishes