Miracle-dz/UniFAR-FTUs huggingface dataset:
The Bayesian Origins of Growth Rates in Stochastic Environments

Stochastic multiplicative dynamics characterize many complex natural phenomena such as selection and mutation in evolving populations, and the generation and distribution of wealth within social systems. Population heterogeneity in stochastic growth rates has been shown to be the crit-ical driver of diversity dynamics and of the emergence of wealth inequality over long time scales. However, we still lack a general statistical framework that systematically explains the origins of these heterogeneities from the adaptation of agents to their environment. In this paper, we derive population growth parameters resulting from the interaction between agents and their knowable environment, conditional on subjective signals each agent receives. We show that average growth rates converge, under speciﬁc conditions, to their maximal value as the mutual information between the agent’s signal and the environment, and that sequential Bayesian inference is the optimal strategy for reaching this maximum. It follows that when all agents access the same environment using the same inference model, the learning process dynamically attenuates growth rate disparities, reversing the long-term eﬀects of heterogeneity on inequality. Our approach lays the foundation for a uniﬁed general quantitative modeling of social and biological phenomena such as the dynamical eﬀects of cooperation, and the eﬀects of education on life history choices.
Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones

Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using smartphone sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.
Training an Assassin AI for The Resistance: Avalon

The Resistance: Avalon is a partially observable social de-duction game. This area of AI game playing is fairly un-developed. Implementing an AI for this game involves multiple components speciﬁc to each phase as well as role in the game. In this paper, we plan to iteratively develop the required components for each role/phase by ﬁrst addressing the Assassination phase which can be modeled as a machine learning problem. Using a publicly available dataset from an online version of the game, we train classiﬁers that emulate an Assassin. After trying various classiﬁcation techniques, we are able to achieve above average human performance using a simple linear support vector classiﬁer. The eventual goal of this project is to pursue developing an intelligent and complete Avalon player that can play through each phase of the game as any role.
Target-Guided Open-Domain Conversation Planning

Prior studies addressing target-oriented conversational tasks lack a crucial notion that has been intensively studied in the context of goal-oriented artificial intelligence agents, namely, planning. In this study, we propose the task of Target-Guided Open-Domain Conversation Planning (TGCP) task to evaluate whether neural conversational agents have goal-oriented conversation planning abilities. Using the TGCP task, we investigate the conversation planning abilities of existing retrieval models and recent strong generative models. The experimental results reveal the challenges facing current technology.
Vlasov simulation of the emissive plasma sheath with energy-dependent secondary emission coefficient and improved modeling for dielectric charging effects

A one-dimensional Vlasov–Poisson simulation code is employed to investigate the plasma sheath considering electron-induced secondary electron emission (SEE) and backscattering. The SEE coefficient is commonly treated as constant in a range of plasma simulations; here, an improved SEE model of a charged dielectric wall is constructed, which includes the wall charging effect on the SEE coefficient and the energy dependency of the SEE coefficient. Pertinent algorithms to implement the previously mentioned SEE model in plasma simulation are studied in detail. It is found that the SEE coefficient increases with the amount of negative wall charges, which in turn reduces the emissive sheath potential. With an energy-dependent SEE coefficient, the sheath potential is a nonlinear function of the plasma electron temperature, as opposed to the linear relation predicted by the classic emissive sheath theory. Simulation combining both wall-charging effect and SEE coefficient’ energy dependency suggests that the space-charged limited sheath is formed at high plasma electron temperature levels, where both sheath potential and surface charging saturate. Additionally, different algorithms to implement the backscattering in the kinetic simulation are tested and compared. Converting backscattered electrons to secondary electrons via an effective SEE coefficient barely affects the sheath properties. The simulation results are shown to be commensurate with the upgraded sheath theory predictions.
Reduction from Complementary-Label Learning to Probability Estimates

Complementary-Label Learning (CLL) is a weakly-supervised learning problem that aims to learn a multi-class classifier from only complementary labels, which indicate a class to which an instance does not belong. Existing approaches mainly adopt the paradigm of reduction to ordinary classification, which applies specific transformations and surrogate losses to connect CLL back to ordinary classification. Those approaches, however, face several limitations, such as the tendency to overfit or be hooked on deep models. In this paper, we sidestep those limitations with a novel perspective--reduction to probability estimates of complementary classes. We prove that accurate probability estimates of complementary labels lead to good classifiers through a simple decoding step. The proof establishes a reduction framework from CLL to probability estimates. The framework offers explanations of several key CLL approaches as its special cases and allows us to design an improved algorithm that is more robust in noisy environments. The framework also suggests a validation procedure based on the quality of probability estimates, leading to an alternative way to validate models with only complementary labels. The flexible framework opens a wide range of unexplored opportunities in using deep and non-deep models for probability estimates to solve the CLL problem. Empirical experiments further verified the framework's efficacy and robustness in various settings.
Updating velocities in heterogeneous comprehensive learning particle swarm optimization with low-discrepancy sequences

: Heterogeneous comprehensive learning particle swarm optimization (HCLPSO) is a type of evolutionary algorithm with enhanced exploration and exploitation capabilities. The low-discrepancy sequence (LDS) is more uniform in covering the search space than random sequences. In this paper, making use of the good uniformity of LDS to improve HCLPSO is researched. Numerical experiments are performed to show that it is impossible to effectively improve the search ability of HCLPSO by only using LDS to generate the initial population. However, if we properly choose some random sequences from HCLPSO’s velocities updating formula and replace them with the deterministic LDS, we can obtain a more efficient algorithm. Compared with the original HCLPSO under the same accuracy requirement, the HCLPSO updating the velocities with the deterministic LDS can significantly reduce the iterations required for finding the optimal solution, without decreasing the success rate.
Safety-Critical Controller Verification via Sim2Real Gap Quantification

The well-known quote from George Box states that: “All models are wrong, but some are useful. ” To develop more useful models, we quantify the inaccuracy with which a given model represents a system of interest, so that we may leverage this quantity to facilitate controller synthesis and verification. Specifically, we develop a procedure that identifies a sim2real gap that holds with a minimum probability. Augmenting the nominal model with our identified sim2real gap produces an uncertain model which we prove is an accurate representor of system behavior. We leverage this uncertain model to synthesize and verify a controller in simulation using a probabilistic verification approach. This pipeline produces controllers with an arbitrarily high probability of realizing desired safe behavior on system hardware without requiring hardware testing except for those required for sim2real gap identification. We also showcase our procedure working on two hardware platforms - the Robotarium and a quadruped.
A Novel Framework based on Unknown Estimation via Principal Sub-space for Universal Domain Adaption

—Universal domain adaptation (UniDA) aims to transfer the knowledge of common classes from source domain to target domain without any prior knowledge on the label set, which requires to distinguish the unknown samples from the known ones in the target domain. Like the traditional unsupervised domain adaptation problem, the misalignment between two domains exists due to the biased and less-discriminative embedding. Recent methods proposed to complete the domain misalignment by clustering target samples with the nearest neighbors or the prototypes. However, it is dangerous to do so since we do not have any prior knowledge about the distributions of unknown samples which can magnify the misalignment especially when the unknown set is big. Meanwhile, other existing classiﬁer-based methods could easily produce overconﬁdent predictions of unknown samples because of the supervised objective in source domain leading the whole model to be biased towards the common classes in the target domain. Therefore, to deal with two issues above, this paper proposed a novel UniDA framework. First, we infer an empirical estimation value of the posterior probability of a target sample belonging to the unknown class based on the its neighborhood searched from source domain. Then, we propose a novel non-parameter unknown samples detection method based on mapping the samples in the original feature space into a reliable linear sub-space which makes data points more sparse to reduce the misalignment between unknown samples and source samples. Moreover, unlike the recent methods applying extra parameters to improve the classiﬁcation of unknown samples, this paper well balances the conﬁdence values of both known and unknown samples through an unknown-adaptive margin loss which can control the gradient updating of the classiﬁer learning on supervised source samples depending on the conﬁdence level of detected unknown samples at current step. Finally, experiments on four public datasets demonstrate that our method signiﬁcantly outperforms existing state-of-the-art methods.
Designing Personalized Garments with Body Movement

The standardized sizes used in the garment industry do not cover the range of individual differences in body shape for most people, leading to ill‐fitting clothes, high return rates and overproduction. Recent research efforts in both industry and academia, therefore, focus on virtual try‐on and on‐demand fabrication of individually fitting garments. We propose an interactive design tool for creating custom‐fit garments based on 3D body scans of the intended wearer. Our method explicitly incorporates transitions between various body poses to ensure a better fit and freedom of movement. The core of our method focuses on tools to create a 3D garment shape directly on an avatar without an underlying sewing pattern, and on the adjustment of that garment's rest shape while interpolating and moving through the different input poses. We alternate between cloth simulation and rest shape adjustment based on stretch to achieve the final shape of the garment. At any step in the real‐time process, we allow for interactive changes to the garment. Once the garment shape is finalized for production, established techniques can be used to parameterize it into a 2D sewing pattern or transform it into a knitting pattern.
Benchmarking Compositionality with Formal Languages

Recombining known primitive concepts into larger novel combinations is a quintessentially human cognitive capability. Whether large neural models in NLP acquire this ability while learning from data is an open question. In this paper, we look at this problem from the perspective of formal languages. We use deterministic finite-state transducers to make an unbounded number of datasets with controllable properties governing compositionality. By randomly sampling over many transducers, we explore which of their properties (number of states, alphabet size, number of transitions etc.) contribute to learnability of a compositional relation by a neural network. In general, we find that the models either learn the relations completely or not at all. The key is transition coverage, setting a soft learnability limit at 400 examples per transition.
An Efficient, High-order, Adaptive Finite Volume Solver for Modelling Capillary Effects in Advanced Manufacturing Processes

—We present a free and open source implementation of the Finite Volume Method that captures thermal and solute-driven free-surface ﬂows, more commonly known as Marangoni ﬂows. This type of physics commonly appears within many manufacturing processes as well as microﬂuidic devices and is known to be challenging to resolve. The application includes adaptive mesh reﬁnement and high order WENO schemes in order to substantially save computational cost in multiphase ﬂows where adequate tracking of the phase boundaries is crucial. By accurately simulating the temporal evolution of multiphase ﬂow patterns, unique insight into e.g. the physics of melt pool formation in metal additive manufacturing is possible that is otherwise hard to gain experimentally. We demonstrate our work by a simple two-dimensional benchmark case and outline possible applications with a mesoscopic model of Laser Powder Bed Fusion additive manufacturing.
NLP-Based Automated Compliance Checking of Data Processing Agreements Against GDPR

When the entity processing personal data (the processor) differs from the one collecting personal data (the controller), processing personal data is regulated in Europe by the General Data Protection Regulation (GDPR) through <italic>data processing agreements (DPAs)</italic>. Checking the compliance of DPAs contributes to the compliance verification of software systems as DPAs are an important source of requirements for software development involving the processing of personal data. However, manually checking whether a given DPA complies with GDPR is challenging as it requires significant time and effort for understanding and identifying DPA-relevant compliance requirements in GDPR and then verifying these requirements in the DPA. Legal texts introduce additional complexity due to convoluted language and inherent ambiguity leading to potential misunderstandings. In this paper, we propose an automated solution to check the compliance of a given DPA against GDPR. In close interaction with legal experts, we first built two artifacts: (i) the “shall” requirements extracted from the GDPR provisions relevant to DPA compliance and (ii) a glossary table defining the legal concepts in the requirements. Then, we developed an automated solution that leverages natural language processing (NLP) technologies to check the compliance of a given DPA against these “shall” requirements. Specifically, our approach automatically generates phrasal-level representations for the textual content of the DPA and compares them against predefined representations of the “shall” requirements. By comparing these two representations, the approach not only assesses whether the DPA is GDPR compliant but it further provides recommendations about missing information in the DPA. Over a dataset of 30 actual DPAs, the approach correctly finds 618 out of 750 genuine violations while raising 76 false violations, and further correctly identifies 524 satisfied requirements. The approach has thus an average precision of 89.1%, a recall of 82.4%, and an accuracy of 84.6%. Compared to a baseline that relies on off-the-shelf NLP tools, our approach provides an average accuracy gain of <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math><alternatives><mml:math><mml:mo>≈</mml:mo></mml:math><inline-graphic xlink:href="amaralcejas-ieq1-3288901.gif"/></alternatives></inline-formula>20 percentage points. The accuracy of our approach can be improved to <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math><alternatives><mml:math><mml:mo>≈</mml:mo></mml:math><inline-graphic xlink:href="amaralcejas-ieq2-3288901.gif"/></alternatives></inline-formula>94% with limited manual verification effort.
Unsupervised Early Exit in DNNs with Multiple Exits

Deep Neural Networks (DNNs) are generally designed as sequentially cascaded differentiable blocks/layers with a prediction module connected only to its last layer. DNNs can be attached with prediction modules at multiple points along the backbone where inference can stop at an intermediary stage without passing through all the modules. The last exit point may offer a better prediction error but also involves more computational resources and latency. An exit point that is ‘optimal’ in terms of both prediction error and cost is desirable. The optimal exit point may depend on the latent distribution of the tasks and may change from one task type to another. During neural inference, the ground truth of instances may not be available and the error rates at each exit point cannot be estimated. Hence one is faced with the problem of selecting the optimal exit in an unsupervised setting. Prior works tackled this problem in an offline supervised setting assuming that enough labeled data is available to estimate the error rate at each exit point and tune the parameters for better accuracy. However, pre-trained DNNs are often deployed in new domains for which a large amount of ground truth may not be available. We thus model the problem of exit selection as an unsupervised online learning problem and leverage the bandit theory to identify the optimal exit point. Specifically, we focus on the Elastic BERT, a pre-trained multi-exit DNN to demonstrate that it ‘nearly’ satisfies the Strong Dominance (SD) property making it possible to learn the optimal exit in an online setup without knowing the ground truth labels. We develop upper confidence bound (UCB) based algorithm named UEE-UCB that provably achieves sub-linear regret under the SD property. Thus our method provides a means to adaptively learn domain-specific optimal exit points in multi-exit DNNs. We empirically validate our algorithm on IMDb and Yelp datasets.
Deep Q-Network for AI Soccer

Reinforcement learning has shown an outstanding performance in the applications of games, particularly in Atari games as well as Go. Based on these successful examples, we attempt to apply one of the well-known reinforcement learning algorithms, Deep Q-Network, to the AI Soccer game. AI Soccer is a 5:5 robot soccer game where each participant develops an algorithm that controls five robots in a team to defeat the opponent participant. Deep Q-Network is designed to implement our original rewards, the state space, and the action space to train each agent so that it can take proper actions in different situations during the game. Our algorithm was able to successfully train the agents, and its performance was preliminarily proven through the mini-competition against 10 teams wishing to take part in the AI Soccer international competition. The competition was organized by the AI World Cup committee, in conjunction with the WCG 2019 Xi'an AI Masters. With our algorithm, we got the achievement of advancing to the round of 16 in this international competition with 130 teams from 39 countries.
emojiSpace: Spatial Representation of Emojis

In the absence of nonverbal cues during messaging communication, users express part of their emotions using emojis. Thus, having emojis in the vocabulary of text messaging language models can significantly improve many natural language processing (NLP) applications such as online communication analysis. On the other hand, word embedding models are usually trained on a very large corpus of text such as Wikipedia or Google News datasets that include very few samples with emojis. In this study, we create emojiSpace , which is a combined word-emoji embedding using the word2vec model from the Genism library in Python. We trained emojiSpace on a corpus of more than 4 billion tweets and evaluated it by implementing sentiment analysis on a Twitter dataset containing more than 67 mil-lion tweets as an extrinsic task. For this task, we compared the performance of two different classifiers of random forest (RF) and linear support vector machine (SVM). In the evaluation task, we compared emojiSpace performance with two other pre-trained embeddings and demonstrated that emojiSpace outperforms both.
Calibrated Uncertainty Estimation Improves Bayesian Optimization

Bayesian optimization is a sequential procedure for obtaining the global optimum of black-box functions without knowing a priori their true form. Good uncertainty estimates over the shape of the objective function are essential in guiding the optimization process. However, these estimates can be inaccurate if the true objective function violates assumptions made by its model (e.g., Gaussianity). This paper studies which uncertainties are needed in Bayesian optimization models and argues that ideal uncertainties should be calibrated -- i.e., an 80% predictive interval should contain the true outcome 80% of the time. We propose a simple algorithm for enforcing this property and show that it enables Bayesian optimization to arrive at the global optimum in fewer steps. We provide theoretical insights into the role of calibrated uncertainties and demonstrate the improved performance of our method on standard benchmark functions and hyperparameter optimization tasks.
A Compact Low Noise Single Frequency Linearly Polarized DBR Fiber Laser at 1550 nm

A compact low noise single frequency linearly polarized distributed Bragg reflector (DBR) fiber laser based on a 1.4-cm-long homemade Er3+/Yb3+ -codoped phosphate single mode glass fiber has been demonstrated. An over 50 mW stable single frequency linearly polarized fiber laser was achieved. The measured slope efficiency is more than 21.6%, and the signal-to-noise ratio (SNR) is higher than 65 dB and the laser linewidth is less than 2.0kHz. The laser RIN is measured to be less than −150 dB/Hz at the frequencies over 2.0MHz, and the obtained linear polarization extinction ratio (LPER) is more than 30 dB.