Miracle-dz/UniFAR-FTUs huggingface dataset:
Quadratic-stretch elasticity

A nonlinear small-strain elastic theory is constructed from a systematic expansion in Biot strains, truncated at quadratic order. The primary motivation is the desire for a clean separation between stretching and bending energies for shells, which appears to arise only from reduction of a bulk energy of this type. An approximation of isotropic invariants, bypassing the solution of a quartic equation or computation of tensor square roots, allows stretches, rotations, stresses, and balance laws to be written in terms of derivatives of position. Two-field formulations are also presented. Extensions to anisotropic theories are briefly discussed.
Generating Continuous Motion and Force Plans in Real-Time for Legged Mobile Manipulation

Manipulators can be added to legged robots, allowing them to interact with and change their environment. Legged mobile manipulation planners must consider how contact forces generated by these manipulators affect the system. Current planning strategies either treat these forces as immutable during planning or are unable to optimize over these contact forces while operating in real-time. This paper presents the Stability and Task Oriented Receding-Horizon Motion and Manipulation Autonomous Planner (STORMMAP) that is able to generate continuous plans for the robot’s motion and manipulation force trajectories that ensure dynamic feasibility and stability of the platform, and incentivizes accomplishing manipulation and motion tasks specified by a user. A variety of simulated experiments on a quadruped with a manipulator mounted to its torso demonstrate the versatility of STOR-MMAP. In contrast to existing state of the art methods, the approach described in this paper generates continuous plans in under ten milliseconds, an order of magnitude faster than previous strategies.
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning

The contribution of this work is twofold: (1) We introduce a collection of ensemble methods for time series forecasting to combine predictions from base models. We demonstrate insights on the power of ensemble learning for forecasting, showing experiment results on about 16000 openly available datasets, from M4, M5, M3 competitions, as well as FRED (Federal Reserve Economic Data) datasets. Whereas experiments show that ensembles provide a benefit on forecasting results, there is no clear winning ensemble strategy (plus hyperparameter configuration). Thus, in addition, (2), we propose a meta-learning step to choose, for each dataset, the most appropriate ensemble method and their hyperparameter configuration to run based on dataset meta-features.
Dissipative Phase Transition in Systems with Two-Photon Drive and Nonlinear Dissipation near the Critical Point

In this paper, we examine dissipative phase transition (DPT) near the critical point for a system with two-photon driving and nonlinear dissipations. The proposed mean-field theory, which explicitly takes into account quantum fluctuations, allowed us to describe properly the evolutionary dynamics of the system and to demonstrate new effects in its steady-state. We show that the presence of quantum fluctuations leads to a power-law dependence of the anomalous average at the phase transition point, with which the critical exponent is associated. Also, we investigate the effect of the quantum fluctuations on the critical point renormalization and demonstrate the existence of a two-photon pump “threshold”. It is noteworthy that the obtained results are in a good agreement with the numerical simulations.
What are higher-order networks?

Network-based modeling of complex systems and data using the language of graphs has become an essential topic across a range of different disciplines. Arguably, this graph-based perspective derives its success from the relative simplicity of graphs: A graph consists of nothing more than a set of vertices and a set of edges, describing relationships between pairs of such vertices. This simple combinatorial structure makes graphs interpretable and flexible modeling tools. The simplicity of graphs as system models, however, has been scrutinized in the literature recently. Specifically, it has been argued from a variety of different angles that there is a need for higher-order networks, which go beyond the paradigm of modeling pairwise relationships, as encapsulated by graphs. In this survey article we take stock of these recent developments. Our goals are to clarify (i) what higher-order networks are, (ii) why these are interesting objects of study, and (iii) how they can be used in applications.
Multimode parity-time symmetry and loss compensation in coupled waveguides with loss and gain

Loss compensation via inserting gain is of fundamental importance in different branches of photonics, nanoplasmonics, and metamaterial science. This effect has found an impressive implementation in the parity-time symmetric (PT -symmetric) structures possessing balanced distribution of loss and gain. In this work, we generalize this phenomenon to the asymmetric systems demonstrating loss compensation in the coupled multi-mode loss-gain dielectric waveguides of different radii. We show that similar to the PT -symmetric coupled single-mode waveguides of identical radii, the asymmetric systems support the exceptional points called here the loss compensation (LC) thresholds where the frequency spectrum undergoes a transition from complex to real values. Moreover, the LC-symmetry thresholds can be obtained for dissimilar modes excited in the waveguides providing an additional degree of freedom to control the system response. In particular, changing loss and gain of asymmetric coupled waveguides, we observe loss compensation for TM and TE modes as well as for the hybrid HE and EH modes.
Design (Not) Lost in Translation: A Case Study of an Intimate-Space Socially Assistive “Robot” for Emotion Regulation

We present a Research-through-Design case study of the design and development of an intimate-space tangible device perhaps best understood as a socially assistive robot, aimed at scaffolding children’s efforts at emotional regulation. This case study covers the initial research device development, as well as knowledge transfer to a product development company toward translating the research into a workable commercial product that could also serve as a robust “research product” for field trials. Key contributions to the literature include: (1) sharing of lessons learned from the knowledge transfer process that can be useful to others interested in developing robust products (whether commercial or research) that preserve design values, while allowing for large scale deployment and research; (2) articulation of a design space in HCI/HRI (Human Robot Interaction) of intimate space socially assistive robots, with the current artifact as a central exemplar, contextualized alongside other related HRI artifacts.
Knodle: Modular Weakly Supervised Learning with PyTorch

Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture. In this work, we introduce Knodle, a software framework that treats weak data annotations, deep learning models, and methods for improving weakly supervised training as separate, modular components. This modularization gives the training process access to fine-grained information such as data set characteristics, matches of heuristic rules, or elements of the deep learning model ultimately used for prediction. Hence, our framework can encompass a wide range of training methods for improving weak supervision, ranging from methods that only look at correlations of rules and output classes (independently of the machine learning model trained with the resulting labels), to those that harness the interplay of neural networks and weakly labeled data. We illustrate the benchmarking potential of the framework with a performance comparison of several reference implementations on a selection of datasets that are already available in Knodle.
Recent Advances of Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective

Estimation of the human pose from a monocular camera has been an emerging research topic in the computer vision community with many applications. Recently, benefiting from the deep learning technologies, a significant amount of research efforts have advanced the monocular human pose estimation both in 2D and 3D areas. Although there have been some works to summarize different approaches, it still remains challenging for researchers to have an in-depth view of how these approaches work from 2D to 3D. In this article, we provide a comprehensive and holistic 2D-to-3D perspective to tackle this problem. First, we comprehensively summarize the 2D and 3D representations of human body. Then, we summarize the mainstream and milestone approaches for these human body presentations since the year 2014 under unified frameworks. Especially, we provide insightful analyses for the intrinsic connections and methods evolution from 2D to 3D pose estimation. Furthermore, we analyze the solutions for challenging cases, such as the lack of data, the inherent ambiguity between 2D and 3D, and the complex multi-person scenarios. Next, we summarize the benchmarks, evaluation metrics, and the quantitative performance of popular approaches. Finally, we discuss the challenges and give deep thinking of promising directions for future research. We believe this survey will provide the readers (researchers, engineers, developers, etc.) with a deep and insightful understanding of monocular human pose estimation.
The Abundancy Index and Feebly Amicable Numbers

This research explores the sum of divisors - $\sigma(n)$ - and the abundancy index given by the function $\frac{\sigma(n)}{n}$. We give a generalization of amicable pairs - feebly amicable pairs (also known as harmonious pairs), that is $m,n$ such that $\frac{n}{\sigma(n)}+ \frac{m}{\sigma(m)}=1$. We first give some groundwork in introductory number theory, then the goal of the paper is to determine if all numbers are feebly amicable with at least one other number by using known results about the abundancy index. We establish that not all numbers are feebly amicable with at least one other number. We generate data using the R programming language and give some questions and conjectures.
Tuning Magnetic and Transport Properties in Quasi-2D (Mn1−xNix)2P2S6 Single Crystals

We report an optimized chemical vapor transport method to grow single crystals of (Mn1−xNix)2P2S6 where x = 0, 0.3, 0.5, 0.7, and 1. Single crystals up to 4 mm × 3 mm × 200 μm were obtained by this method. As-grown crystals are characterized by means of scanning electron microscopy and powder X-ray diffraction measurements. The structural characterization shows that all crystals crystallize in monoclinic symmetry with the space group C2/m (No. 12). We have further investigated the magnetic properties of this series of single crystals. The magnetic measurements of the all as-grown single crystals show long-range antiferromagnetic order along all principal crystallographic axes. Overall, the Néel temperature TN is non-monotonous; with increasing Ni2+ doping, the temperature of the antiferromagnetic phase transition first decreases from 80 K for pristine Mn2P2S6 (x = 0) up to x = 0.5 and then increases again to 155 K for pure Ni2P2S6 (x = 1). The magnetic anisotropy switches from out-of-plane to in-plane as a function of composition in (Mn1−xNix)2P2S6 series. Transport studies under hydrostatic pressure on the parent compound Mn2P2S6 evidence an insulator-metal transition at an applied critical pressure of ~22 GPa.
Virtines: Virtualization at Function Call Granularity

Virtual execution environments provide strong isolation, on-demand infrastructure, simpliﬁed device models, and many other beneﬁts for systems and application programmers. However, these environments are often perceived as heavy-weight and rife with performance pitfalls for ﬁne-grained or low-latency tasks. While others have shown that a virtual environment’s footprint can be drastically reduced by paring down the guest and host software stacks, in this paper we take a different approach. We probe the limits of ﬁne-grained, virtual execution by investigating the viability of hardware virtualized environments at function call granularity. We introduce a new abstraction called a virtine , or virtualized subroutine, and present the design of a new micro-hypervisor called Wasp that enables them. Through a series of examples we show that Wasp can enable virtines with start-up latencies as low as 100 µ s. We also provide two convenient programming interfaces to virtines, using the Rust language and using extensions to C. Using these extensions we implement a simple HTTP server and integrate virtines into an off-the-shelf implementation of OpenSSL.
Managing mental & psychological wellbeing amidst COVID-19 pandemic: Positive psychology interventions

COVID-19 pandemic has shaken the roots of healthcare facilities worldwide, with the US being one of the most affected countries irrespective of being a superpower. Along with the current pandemic, COVID-19 can cause a secondary crisis of mental health pandemic if left unignored. Various studies from past epidemics, financial turmoil and pandemic, especially SARS and MERS, have shown a steep increase in mental and psychological issues like depression, low quality of life, self-harm and suicidal tendencies among general populations. The most venerable being the individuals infected and cured due to social discrimination. The government is taking steps to contain and prevent further infections of COVID-19. However, the mental and psychological wellbeing of people is still left ignored in developing countries like India. There is a significant gap in India concerning mental and psychological health still being stigmatized and considered 'non-existent'. This study's effort is to highlight the importance of mental and psychological health and to suggest interventions based on positive psychology literature. These interventions can support the wellbeing of people acting as a psychological first aid. Keywords: COVID-19, Coronavirus, Pandemic, Mental wellbeing, Psychological Wellbeing, Positive Psychology Interventions. KEYWORDS - COVID-19, Coronavirus, Pandemic, Wellbeing, Positive Psychology, Interventions, PPI.
Real-Time Trajectory Optimization in Robot-Assisted Exercise and Rehabilitation

This work focuses on the optimization of the training trajectory orientation using a robot as an advanced exercise machine (AEM) and muscle activations as biofeedback. Muscle recruitment patterns depend on trajectory parameters of the AEMs and correlate with the efficiency of exercise. Thus, improvements to training efficiency may be achieved by optimizing these parameters. The optimal regulation of these parameters is challenging because of the complexity of the physiological dynamics from person to person as a result of the unique physical features such as musculoskeletal distribution. Furthermore, these effects can vary due to fatigue, body temperature, and other physiological factors. In this paper, a model-free optimization method using Extremum Seeking Control (ESC) as a real-time optimizer is proposed. After selecting a muscle objective, this method seeks for the optimal combination of parameters using the muscle activations as biofeedback. The muscle objective can be selected by a therapist to emphasize or de-emphasize certain muscle groups. The feasibility of this method has been proven for the automatic regulation of an ellipsoidal curve orientation, suggesting the existence of two local optimal orientations. This methodology can also be applied to other parameter regulations using a different physiological effects such as oxygen consumption and heart rate as biofeedback.
Removal lemmas and approximate homomorphisms

We study quantitative relationships between the triangle removal lemma and several of its variants. One such variant, which we call the triangle-free lemma, states that for each $\epsilon>0$ there exists M such that every triangle-free graph G has an $\epsilon$ -approximate homomorphism to a triangle-free graph F on at most M vertices (here an $\epsilon$ -approximate homomorphism is a map $V(G) \to V(F)$ where all but at most $\epsilon \left\lvert{V(G)}\right\rvert^2$ edges of G are mapped to edges of F). One consequence of our results is that the least possible M in the triangle-free lemma grows faster than exponential in any polynomial in $\epsilon^{-1}$ . We also prove more general results for arbitrary graphs, as well as arithmetic analogues over finite fields, where the bounds are close to optimal.
Learning to Learn to be Right for the Right Reasons

Improving model generalization on held-out data is one of the core objectives in common- sense reasoning. Recent work has shown that models trained on the dataset with superficial cues tend to perform well on the easy test set with superficial cues but perform poorly on the hard test set without superficial cues. Previous approaches have resorted to manual methods of encouraging models not to overfit to superficial cues. While some of the methods have improved performance on hard instances, they also lead to degraded performance on easy in- stances. Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and the hard test set without superficial cues. Using a meta-learning objective, we learn such a model that improves performance on both the easy test set and the hard test set. By evaluating our models on Choice of Plausible Alternatives (COPA) and Commonsense Explanation, we show that our proposed method leads to improved performance on both the easy test set and the hard test set upon which we observe up to 16.5 percentage points improvement over the baseline.
Quantum tomography of three-qubit generalized Werner states

In this paper, we introduce a numerical framework for quantum tomography and entanglement quantification of three-qubit generalized Werner states. The scheme involves the single-qubit symmetric, informationally complete, positive operator-valued measure (SIC-POVM), which is then generalized to perform three-qubit measurements. We introduce random errors into the scheme by imposing the Poisson noise on the measured photon counts. The precision of state estimation is quantified and presented on graphs. As a special case, we compare the efficiency of the framework for the pure three-qubit generalized Werner state and the W state. The reconstructed states are presented graphically and discussed.
DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing From Decentralized Data

Deep neural speech and audio processing systems have a large number of trainable parameters, a relatively complex architecture, and require a vast amount of training data and computational power. These constraints make it more challenging to integrate such systems into embedded devices and utilize them for real-time, real-world applications. We tackle these limitations by introducing DeepSpectrumLite, an open-source, lightweight transfer learning framework for on-device speech and audio recognition using pre-trained image Convolutional Neural Networks (CNNs). The framework creates and augments Mel spectrogram plots on the fly from raw audio signals which are then used to finetune specific pre-trained CNNs for the target classification task. Subsequently, the whole pipeline can be run in real-time with a mean inference lag of 242.0 ms when a DenseNet121 model is used on a consumer-grade Motorola moto e7 plus smartphone. DeepSpectrumLite operates decentralized, eliminating the need for data upload for further processing. We demonstrate the suitability of the proposed transfer learning approach for embedded audio signal processing by obtaining state-of-the-art results on a set of paralinguistic and general audio tasks, including speech and music emotion recognition, social signal processing, COVID-19 cough and COVID-19 speech analysis, and snore sound classification. We provide an extensive command-line interface for users and developers which is comprehensively documented and publicly available at https://github.com/DeepSpectrum/DeepSpectrumLite.
Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. In this paper, we decide HVAC control schedules under uncertain PV generation, using a distributionally robust chance-constrained (DRCC) building load control model under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve tradeoff between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions. Summary of Contribution: The problem studied in this paper is motivated by a building load control problem that uses the aggregation of heating, ventilation, and air conditioning (HVAC) loads as flexible reserves to absorb uncertain solar photovoltaic (PV) generation. The problem is formulated as distributionally robust chance-constrained (DRCC) programs with right-hand side (RHS) uncertainty. In addition, we propose a risk-adjustable variant of the DRCC programs, where the risk level, instead of being predetermined, is treated as a decision variable. The paper aims to provide tractable reformulations and solution algorithms for both the (general) DRCC and the (general) adjustable DRCC models with RHS uncertainty.
Nash Equilibria of The Multiplayer Colonel Blotto Game on Arbitrary Measure Spaces

The Colonel Blotto Problem proposed by Borel in 1921 has served as a widely applicable model of budget-constrained simultaneous winner-take-all competitions in the social sciences. Applications include elections, advertising, R&D and more. However, the classic Blotto problem and variants limit the study to competitions over a finite set of discrete battlefields. In this paper, we extend the classical theory to study multiplayer Blotto games over arbitrary measurable battlegrounds, provide an algorithm to efficiently sample equilibria of symmetric “equipartionable” Generalized Blotto games, and characterize the symmetric fair equilibria of the Blotto game over the unit interval.