Miracle-dz/UniFAR-FTUs huggingface dataset:
Abundance, survival and population growth of killer whales Orcinus orca at subantarctic Marion Island

Killer whales, Orcinus orca , are a cosmopolitan species with large ecological and demographic variation across populations. Population-specific demographic studies are, therefore, crucial in accurately assessing the status and trends of local killer whale populations. Such studies require long-term datasets and remain scarce, particularly in the Southern Ocean where detailed population specific studies have only been conducted at a single archipelago – Îles Crozet. Here, we analysed 12 years of capture–recapture data (comprising nearly 90 000 identification photographs taken from 2006 to 2018) of killer whales at subantarctic Marion Island (46 ° 54 ′ S, 37 ° 45 ′ E) to estimate the abundance, survival and growth rate of this popu- lation. Demographic parameters were estimated using multistate capture recapture models, and Pradel Survival-Lambda and POPAN single-state models implemented in the program MARK. Annual survival probability (0.98 [95% CI: 0.96– 0.99]) was constant over time, and no important differences between sexes and age-classes (calves, juveniles, adults) were found. This estimate of survival suggests a life expectancy of approximately 48 years. Realised mean population growth rate ( λ ) was 1.012 (0.987–1.037) with an estimated population size of 54 (54–60) individuals and a mean calving rate of 0.13 (0.06–0.20) calves born per year per reproductive female. The survival and reproduction rates of killer whales at Marion Island are similar to those of killer whale populations in the eastern North Pacific, Norway and Îles Crozet. However, subtle differences in survival and reproduction rates are present. These are likely the result of local differences in resource abundances, historical impacts on social structure and/or stressors. Also, the presence and scale of fisheries (legal and illegal) in the area may provide opportunities for direct interactions with fishing activities impacting survival and reproduction rates.
NeurInt-Learning Interpolation by Neural ODEs

A range of applications require learning image generation models whose latent space effectively captures the high-level factors of variation in the data distribution, which can be judged by its ability to interpolate between images smoothly. However, most generative models mapping a ﬁxed prior to the generated images lead to interpolation trajectories lacking smoothness and images of reduced quality. We propose a novel generative model that learns a ﬂexible non-parametric prior over interpolation trajectories, conditioned on a pair of source and target images. Instead of relying on deterministic interpolation methods like linear or spherical interpolation in latent space, we devise a framework that learns a distribution of trajectories between two given images using Latent Second-Order Neural Ordinary Differential Equations. Through a hybrid combination of reconstruction and adversarial losses, the generator is trained to map the sampled points from these trajectories to sequences of realistic images of improved quality that smoothly transition from the source to the target image.
Comprehensive behavioural intervention for tics: a neurophysiological intervention.

BACKGROUND Gilles de la Tourette Syndrome (GTS) is a childhood-onset neuropsychiatric disorder characterised by motor and vocal tics. While Comprehensive Behavioural Intervention for Tics (CBIT) is an effective, non-pharmacological treatment for patients with GTS, the underlying neurophysiological basis of this intervention has not been investigated. METHODS To investigate the clinical effectiveness of CBIT in reducing tic severity in young people with GTS and explore neurophysiological mechanisms associated with clinical change. RESULTS There was a significant overall improvement in tic severity of large effect size. The Cortical Silent Period (CSP) to motor evoked potential (MEP) ratio (CSP/MEP ratio) increased after the intervention with a small effect size. Other neurophysiological measures of inhibition were not significantly related to the change in tic severity. CONCLUSIONS Alongside significant clinical improvements, these results suggest a role for motor cortical Gamma-aminobutyric acid (GABA)-ergic inhibitory circuitry in the neurophysiological changes underlying CBIT treatment. These findings need to be replicated in larger studies using control samples.
Optimization in the Small-Data, Large-Scale Regime

This chapter introduces the small-data, large-scale optimization regime, an asymptotic setting that arguably better describes certain data-driven optimization applications than the more traditional large-sample regime. We highlight unique phenomena that emerge in the small-data, large-scale regime, and show how these phenomena cause certain traditional data-driven optimization algorithms like sample average approximation (SAA) to fail. We then propose a new debiasing approach that has provably good performance in this regime, highlighting a new path forward for research and development into these types of applications.
Current Opinion in Behavioral Sciences What is Positive Affect?

: Positive affect has become a major topic in behavioral science, of growing importance in basic and applied research. A broad review of the literature reveals a variety of terms and theoretically distinct underlying constructs associated with positive affect and emotion, which are sometimes conflated across this body of work. This article differentiates three main versions of “positivity” in the affective domain – subjectively pleasant affect, approach or appetitive motivation, and response to opportunities and rewards in the environment – summarizing the major theoretical perspectives linked to each definition. Examples are offered of divergence among these constructs, as well as dynamic ways in which they influence each other. Distinct cognitive, physiological, and behavioral mechanisms by which each version of positivity may produce downstream effects are discussed, as is the importance of selecting and operationalizing the target construct with care in both basic affective science and translational research. Abstract Positive affect has become a major topic in behavioral science, of growing importance in basic and applied research. A broad review of the literature reveals a variety of terms and theoretically distinct underlying constructs associated with positive affect and emotion, which are sometimes conflated across this body of work. This article differentiates three main versions of “positivity” in the affective domain – subjectively pleasant affect, approach or appetitive motivation, and response to opportunities and rewards in the environment – summarizing the major theoretical perspectives linked to each definition. Examples are offered of divergence among these constructs, as well as dynamic ways in which they influence each other. Distinct cognitive, physiological, and behavioral mechanisms by which each version of positivity may produce downstream effects are discussed, as is the importance of selecting and operationalizing the target construct with care in both basic affective science and translational research. Highlights : the research left- right-weighted with direction and The extent of variability across pleasant approach Behavioral Neuroscience 2020, 1-14. This article presents a well-powered experiment in which pleasant affect and approach motivation were manipulated independently, and neural activity markers of reward expectancy and response to reward outcomes during a gambling task were the dependent variables. Results indicate that optimistic reward expectations track pleasant affect rather than approach motivation. cross-sectional study performance outcomes
Lecture 23: Hardness for Auctions via Communication Complexity

In this lecture we’ll discuss communication problems. That is, problems where no single party holds the entire input and we’ll want to know how much communication the parties need to possibly solve the problem. We’ll show a strong hardness result for combinatorial auctions where different agents hold their valuations: finding the welfare maximizing allocation could require exponential communication (even if we ignore incentives).
Simultaneous Worlds: Supporting Fluid Exploration of Multiple Data Sets via Physical Models

We take the well-established use of physical scale models in architecture and identify new opportunities for using them to interactively visualize and examine multiple streams of geospatial data. Over-laying, comparing, or integrating visualizations of complementary data sets in the same physical space is often challenging given the constraints of various data types and the limited design space of possible visual encodings. Our vision of “simultaneous worlds” uses physical models as a substrate upon which visualizations of multiple data streams can be dynamically and concurrently integrated. To explore the potential of this concept, we created three design explorations that use an illuminated campus model to integrate visualizations about building energy use, climate, and movement paths on a university campus. We use a research through design approach, documenting how our interdisciplinary collaborations with domain experts, students, and architects informed our designs. Based on our observations, we characterize the beneﬁts of models for 1) situating visualizations, 2) composing visualizations, and 3) manipulating and authoring visualizations. Our work highlights the potential of physical models to support embodied exploration of spatial and non-spatial visualizations through ﬂuid interactions.
Identification of Three Prognosis-Related Differentially Expressed lncRNAs Driven by Copy Number Variation in Thyroid Cancer

Thyroid cancer as the malignant tumor with the highest incidence in the endocrine system also shows a fast growth and development. In this work, we developed a new method to identify copy number variation– (CNV–) driven differentially expressed lncRNAs in thyroid cancer for predicting cancer prognosis. The data of RNA sequencing, CNV, methylation, mutation, and clinical details of thyroid cancer were obtained from the Cancer Genome Atlas database (TCGA). Molecular subtypes were clustered by iClusterPlus. Weighted gene co-expression network analysis (WGCNA) was employed to show co-expression modules. DEseq2 was conducted to identify protein coding genes (PCGs) and differentially expressed lncRNAs. CNV was detected using GISTIC 2.0. Three molecular subtypes were identified, and 68 differentially expressed lncRNAs (DElncRNAs) related to cancer were found among different molecular subtypes. CNV of FOXD2-AS1, FAM181A-AS1, and RNF157-AS1 was associated with overall survival and was involved in cancer-related pathways. These three DElncRNAs discovered based on CNV could serve as prognostic biomarkers to predict prognosis for thyroid cancer and new targets to explore molecular drugs.
Design, fabrication and characterization of a VMOS monolithic integrated optical detector

The work presented in this PhD manuscript deals with the monolithic integrationof an optical galvanic isolation unit within the vertical FET structure of a 600Vpower transistor. The optical galvanic isolation unit is a photodetector that is responsiblefor transferring the gating information signal from an external control unit to the powerswitch. The necessary energy to switch the power device is provided by means of a TIAfollowed by a gate driver. This document has four chapters: introduction and motivation:Integrated glavanic isolation for power devices, Compatible integrated phootdiodes (IPDs)for power switches: Modeling and design, IPD fabrication and characterization, and conclusionsand future work. The results of this research work are interesting for a wide rangeof applications specially as the power electronic community strives for a fully integratedpower function with lower implementation costs and reliable, high level galvanic isolationsolutions that are compact and cost effective.
Inflammation Development Triggered by Epiregulin Regulates Temporal Expression of Growth Factors

In this study, we investigated the relationship between several growth factors and inflammation development. Serum concentrations of epiregulin, amphiregulin, betacellulin, TGF-a, fibroblast growth factor 2, placental growth factor (PLGF), and tenascin C were increased in rheumatoid arthritis patients. Furthermore, local blockades of these growth factors suppressed the development of cytokine-induced arthritis in mice by inhibiting chemokine and IL-6 expressions. We found that epiregulin expression was early and followed by the induction of other growth factors at different sites of the joints. The same growth factors then regulated the expression of epiregulin at later time points of the arthritis. These growth factors were increased in patients suffering from multiple sclerosis (MS) and also played a role in the development of an MS model, experimental autoimmune encephalomyelitis. The results suggest that the temporal expression of growth factors is involved in the inflammation development seen in several diseases, including rheumatoid arthritis and MS. Therefore, various growth factor pathways might be good therapeutic targets for various inflammatory diseases. I nterleukin-6 is a cytokine expressed by various activated cells, including CD4 + cells, and has an important role in the development of inflammation (1, 2). It is also required for the development of Th17 cells, which are IL-17–expressing activated CD4 + T cells (3), and strongly correlates with various inflamma-tory disease models (4). We previously identified the inflammation amplifier (formerly the IL-6 amplifier) as a fundamental mechanism of inflammation induction in such disease models as well as in human inflammatory diseases (4–6). The amplifier, which is activated by simultaneous stimulation of NF-kB and STAT3 via cytokines such as IL-17A and IL-6 in type 1 collagen + nonimmune cells, induces a positive feedback loop of IL-6 (5). The amplifier acts as a local chemokine inducer that accumulates various immune cells followed by the local dysregulation of homeostasis, that is, inflammation. Since its discovery, we have shown that the amplifier is hyperactivated by various factors, including cytokines, neurotransmitters, and the growth factor epiregulin (1, 4). Growth factors consist of many groups, including the epidermal growth factor (EGF) family, the platelet-derived growth factor family, the vascular endothelial growth factor family, and the fi-broblast growth factor (FGF) family, all of which have the potential to initiate and mediate many complex biological responses. Most receptors of these families have a tyrosine kinase region (7). The extracellular ligand–binding domain is more variable, leading to different ligand profiles even in the same …
Temporal dilemma, time preferences and natural resource extraction

This dissertation sheds lights on two important questions related to temporal dilemma with respect to natural resource (fisheries) extraction: (i) How does the presence of temporal dilemma impact resource users´ extraction behavior? (ii) What is the relationship between individual time preferences and resource extraction behavior? For both cases I consider two different aspects of extraction behavior: (i) effort level decision, and (ii) extraction method choice. Chapter 2 looks at the impact of temporal dilemma on the distinct but inter-related problems of over-extraction and destructive extraction in natural resource use settings. I employ standard CPR experiments without time delay (control groups) and CPR experiments with time delay (time treatment groups) in conservation earnings to investigate whether or not participants were likely to extract greater amount of resource in the presence of temporal dilemma. Our results suggest that delaying the benefits of conservation – an experimental feature which brings the experiment closer to reality, as conservation benefits always occur with a time delay – mainly impacts participants’ extraction decision by making them more likely to try out destructive extraction methods. I find that the number of people, who do not opt for destructive extraction method even once during gear choice rounds, was significantly less in the time treatment groups as compared to control groups. On the other hand, I do not find any evidence of difference in effort level between time treatment groups and control groups when participants could not chose their extraction method. Chapter 3 combines experimental methods and questionnaire data to understand the relationship between individual time preferences and natural resource (fisheries) extraction. I elicit individual time preference with incentivized choice experiments and link the resulting time preference measures to extraction data from questionnaires and CPR experiment. Our findings su
Setting Reserve Prices in Second-Price Auctions with Unobserved Bids

In this work we consider a seller who sells an item via second-price auctions with a reserve price. By controlling the reserve price, the seller can influence the revenue from the auction, and in this paper, we propose a method for learning optimal reserve prices. We study a limited information setting where the probability distribution of the bids from bidders is unknown and the values of the bids are not revealed to the seller. Furthermore, we do not assume that the seller has access to a historical data set with bids. Our main contribution is a method that incorporates knowledge about the rules of second-price auctions into a multiarmed bandit framework for optimizing reserve prices in our limited information setting. The proposed method can be applied in both stationary and nonstationary environments. Experiments show that the proposed method outperforms state-of-the-art bandit algorithms. In stationary environments, our method outperforms these algorithms when the horizon is short and performs as good as they do for longer horizons. Our method is especially useful if there is a high number of potential reserve prices. In addition, our method adapts quickly to changing environments and outperforms state-of-the-art bandit algorithms designed for nonstationary environments. Summary of Contribution: A key challenge in online advertising is the pricing of advertisements in online auctions. The scope of our study is second-price auctions with a focus on the reserve price optimization problem from a seller’s point of view. This problem is motivated by the real-life practice of small and medium-sized web publishers. However, the proposed solution approach is applicable to any seller who sells an item via second-price auctions and wants to optimize its reserve price during these auctions. Our solution approach is based on techniques from machine learning and operations research, and it would be beneficial especially for sellers who start the selling process without any historical data and can collect the data on the outcomes of the auctions while making reserve price decisions over time.
A Multimodal Corpus for Emotion Recognition in Sarcasm

While sentiment and emotion analysis have been studied extensively, the relationship between sarcasm and emotion has largely remained unexplored. A sarcastic expression may have a variety of underlying emotions. For example, “I love being ignored” belies sadness, while “my mobile is fabulous with a battery backup of only 15 minutes! ” expresses frustration. Detecting the emotion behind a sarcastic expression is non-trivial yet an important task. We undertake the task of detecting the emotion in a sarcastic statement, which to the best of our knowledge, is hitherto unexplored. We start with the recently released multimodal sarcasm detection dataset (MUStARD) pre-annotated with 9 emotions. We identify and correct 343 incorrect emotion labels (out of 690). We double the size of the dataset, label it with emotions along with valence and arousal which are important indicators of emotional intensity. Finally, we label each sarcastic utterance with one of the four sarcasm types-Propositional, Embedded, Likeprefixed and Illocutionary, with the goal of advancing sarcasm detection research. Exhaustive experimentation with multimodal (text, audio, and video) fusion models establishes a benchmark for exact emotion recognition in sarcasm and outperforms the state-of-art sarcasm detection. We release the dataset enriched with various annotations and the code for research purposes: https://github.com/apoorva-nunna/MUStARD_Plus_Plus
Evaluating Bloom’s Taxonomy-based Learning Modules for Parallel Coordinates Literacy

In this paper, we present the results of an intervention designed to introduce parallel coordinates to students. The intervention contains six new modules inspired by Bloom’s taxonomy that featured a combination of videos, tests, and tasks. We studied the impact of our modules with a corrective feedback mechanism inspired by Mastery Learning. Based on analyzing the data of our students, we found that students in the Corrective Immediate Feedback (CIF) group performed better on average on all the modules as compared to the students in the No Feedback (NF) group. In the tasks where students were required to construct parallel coordinates plots, students in the Corrective Immediate Feedback group produced plots with appropriate use of color, labels, legends, etc. Overall, students in both groups grew more conﬁdent in their ability to recognize parallel coordinates plots and expressed high conﬁdence in their ability to interpret, create, and use parallel coordinates plots for data exploration and presentation in the future.
Engaging Experts: Dealing with Divergent Elicited Priors in Political Science

Priors over parameters constitute one of the most visible manifestations of the theoretical distinctions between Bayesian and frequentist statistics. The information contained within the specified prior, however, will depend upon the knowledge available to a given researcher. An elicited prior seeks to leverage the substantive knowledge of area experts, whether through interviews or published research, in order to improve the accuracy of posterior estimates. Elicited priors as a concept have been detailed since the early work of Savage (1971), Shuford and Brown (1975), Kadane and Wolfson (1998), and Garthwaite, Kadane, and O’Hagan (2005). Formally, Gill and Walker (2005, 841) discusses the elicited prior “as a means of drawing information from subject-area experts with the goal of constructing a probability structure that reflects their specific qualitative knowledge, and perhaps experiential intuition, about the studied effects. ” These aims and applications for elicited priors seem both reasonable and desirable, yet the 10 years since the publication of Gill and Walker’s article have seen few instances of researchers, especially in political science, adopting elicited priors as a way to incorporate qualitative knowledge into quantitative research.