Miracle-dz/UniFAR-FTUs huggingface dataset:
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think

Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning. Framing the problem as a self-supervised task, where data samples are discriminated from noise samples, is at the core of state-of-the-art methods, beginning with Noise-Contrastive Estimation (NCE). Yet, such contrastive learning requires a good noise distribution, which is hard to specify; domain-specific heuristics are therefore widely used. While a comprehensive theory is missing, it is widely assumed that the optimal noise should in practice be made equal to the data, both in distribution and proportion. This setting underlies Generative Adversarial Networks (GANs) in particular. Here, we empirically and theoretically challenge this assumption on the optimal noise. We show that deviating from this assumption can actually lead to better statistical estimators, in terms of asymptotic variance. In particular, the optimal noise distribution is different from the data's and even from a different family.
Benchmarking Augmentation Methods for Learning Robust Navigation Agents: the Winning Entry of the 2021 iGibson Challenge

Recent advances in deep reinforcement learning and scalable photorealistic simulation have led to increasingly mature embodied AI for various visual tasks, including navigation. However, while impressive progress has been made for teaching embodied agents to navigate static environments, much less progress has been made on more dynamic environments that may include moving pedestrians or movable obstacles. In this study, we aim to benchmark different augmentation techniques for improving the agent's performance in these challenging environments. We show that adding several dynamic obstacles into the scene during training confers significant improvements in test-time generalization, achieving much higher success rates than baseline agents. We find that this approach can also be combined with image augmentation methods to achieve even higher success rates. Additionally, we show that this approach is also more robust to sim-to-sim transfer than image augmentation methods. Finally, we demonstrate the effectiveness of this dynamic obstacle augmentation approach by using it to train an agent for the 2021 iGibson Challenge at CVPR, where it achieved 1st place for Interactive Navigation.
LIMEADE: From AI Explanations to Advice Taking

Research in human-centered AI has shown the benefits of systems that can explain their predictions. Methods that allow AI to take advice from humans in response to explanations are similarly useful. While both capabilities are well developed for transparent learning models (e.g., linear models and GA2Ms) and recent techniques (e.g., LIME and SHAP) can generate explanations for opaque models, little attention has been given to advice methods for opaque models. This article introduces LIMEADE, the first general framework that translates both positive and negative advice (expressed using high-level vocabulary such as that employed by post hoc explanations) into an update to an arbitrary, underlying opaque model. We demonstrate the generality of our approach with case studies on 70 real-world models across two broad domains: image classification and text recommendation. We show that our method improves accuracy compared to a rigorous baseline on the image classification domains. For the text modality, we apply our framework to a neural recommender system for scientific papers on a public website; our user study shows that our framework leads to significantly higher perceived user control, trust, and satisfaction.
Expert Q-learning: Deep Reinforcement Learning with Coarse State Values from Offline Expert Examples

In this article, we propose a novel algorithm for deep reinforcement learning named Expert Q-learning. Expert Q-learning is inspired by Dueling Q-learning and aims to incorporate semi-supervised learning into reinforcement learning through splitting Q-values into state values and action advantages. We require that an offline expert assesses the value of a state in a coarse manner using three discrete values. An expert network is designed in addition to the Q-network, which updates each time following the regular offline minibatch update whenever the expert example buffer is not empty. Using the board game Othello, we compare our algorithm with the baseline Q-learning algorithm, which is a combination of Double Q-learning and Dueling Q-learning. Our results show that Expert Q-learning is indeed useful and more resistant to the overestimation bias. The baseline Q-learning algorithm exhibits unstable and suboptimal behavior in non-deterministic settings, whereas Expert Q-learning demonstrates more robust performance with higher scores, illustrating that our algorithm is indeed suitable to integrate state values from expert examples into Q-learning.
Artificial Concepts of Artificial Intelligence: Institutional Compliance and Resistance in AI Startups

Scholars and industry practitioners have debated how to best develop interventions for ethical artificial intelligence (AI). Such interventions recommend that companies building and using AI tools change their technical practices, but fail to wrangle with critical questions about the organizational and institutional context in which AI is developed. In this paper, we contribute descriptive research around the life of "AI" as a discursive concept and organizational practice in an understudied sphere--emerging AI startups--and with a focus on extra-organizational pressures faced by entrepreneurs. Leveraging a theoretical lens for how organizations change, we conducted semi-structured interviews with 23 entrepreneurs working at early-stage AI startups. We find that actors within startups both conform to and resist institutional pressures. Our analysis identifies a central tension for AI entrepreneurs: they often valued scientific integrity and methodological rigor; however, influential external stakeholders either lacked the technical knowledge to appreciate entrepreneurs' emphasis on rigor or were more focused on business priorities. As a result, entrepreneurs adopted hyped marketing messages about AI that diverged from their scientific values, but attempted to preserve their legitimacy internally. Institutional pressures and organizational constraints also influenced entrepreneurs' modeling practices and their response to actual or impending regulation. We conclude with a discussion for how such pressures could be used as leverage for effective interventions towards building ethical AI.
Learning Efficiently Function Approximation for Contextual MDP

We study learning contextual MDPs using a function approximation for both the rewards and the dynamics. We consider both the case that the dynamics dependent or independent of the context. For both models we derive polynomial sample and time complexity (assuming an efficient ERM oracle). Our methodology gives a general reduction from learning contextual MDP to supervised learning.
The Theoretical Expressiveness of Maxpooling

Over the decade since deep neural networks became state of the art image classifiers there has been a tendency towards less use of max pooling: the function that takes the largest of nearby pixels in an image. Since max pooling featured prominently in earlier generations of image classifiers, we wish to understand this trend, and whether it is justified. We develop a theoretical framework analyzing ReLU based approximations to max pooling, and prove a sense in which max pooling cannot be efficiently replicated using ReLU activations. We analyze the error of a class of optimal approximations, and find that whilst the error can be made exponentially small in the kernel size, doing so requires an exponentially complex approximation. Our work gives a theoretical basis for understanding the trend away from max pooling in newer architectures. We conclude that the main cause of a difference between max pooling and an optimal approximation, a prevalent large difference between the max and other values within pools, can be overcome with other architectural decisions, or is not prevalent in natural images.
Video Question Answering: Datasets, Algorithms and Challenges

This survey aims to sort out the recent advances in video question answering (VideoQA) and point towards future directions. We firstly categorize the datasets into 1) normal VideoQA, multi-modal VideoQA and knowledge-based VideoQA, according to the modalities invoked in the question-answer pairs, or 2) factoid VideoQA and inference VideoQA, according to the technical challenges in comprehending the questions and deriving the correct answers. We then summarize the VideoQA techniques, including those mainly designed for Factoid QA (e.g., the early spatio-temporal attention-based methods and the recently Transformer-based ones) and those targeted at explicit relation and logic inference (e.g., neural modular networks, neural symbolic methods, and graph-structured methods). Aside from the backbone techniques, we delve into the specific models and find out some common and useful insights either for video modeling, question answering, or for cross-modal correspondence learning. Finally, we point out the research trend of studying beyond factoid VideoQA to inference VideoQA, as well as towards the robustness and interpretability. Additionally, we maintain a repository, https://github.com/VRU-NExT/VideoQA, to keep trace of the latest VideoQA papers, datasets, and their open-source implementations if available. With these efforts, we strongly hope this survey could shed light on the follow-up VideoQA research.
Nanophotonic Cavity Based Synapse for Scalable Photonic Neural Networks

The bandwidth and energy demands of neural networks has spurred tremendous interest in developing novel neuromorphic hardware, including photonic integrated circuits. Although an optical waveguide can accommodate hundreds of channels with THz bandwidth, the channel count of photonic systems is always bottlenecked by the devices within. In WDM-based photonic neural networks, the synapses, i.e. network interconnections, are typically realized by microring resonators (MRRs), where the WDM channel count ($N$) is bounded by the free-spectral range of the MRRs. For typical Si MRRs, we estimate $N \leq 30$ within the C-band. This not only restrains the aggregate throughput of the neural network but also makes applications with high input dimensions unfeasible. We experimentally demonstrate that photonic crystal nanobeam based synapses can be FSR-free within C-band, eliminating the bound on channel count. This increases data throughput as well as enables applications with high-dimensional inputs like natural language processing and high resolution image processing. In addition, the smaller physical footprint of photonic crystal nanobeam cavities offers higher tuning energy efficiency and a higher compute density than MRRs. Nanophotonic cavity based synapse thus offers a path towards realizing highly scalable photonic neural networks.
Ion Acceleration at Magnetotail Plasma Jets

We investigate a series of Earthward bursty bulk flows (BBFs) observed by the Magnetospheric Multiscale (MMS) spacecraft in Earth’s magnetotail at (-24, 7, 4) RE in Geocentric Solar Magnetospheric (GSM) coordinates. At the leading edges of the BBFs, we observe complex magnetic field structures. In particular, we focus on one which presents a chain of small scale (∼ 0.5 RE) dipolarizations, and another with a large scale (∼ 3.5 RE) dipolarization. Although the two structures have different scales, both of these structures are associated with flux increases of supra-thermal ions with energies & 100 keV. We investigate the ion acceleration mechanism and its dependence on the mass and charge state. We show that the ions with gyroradii smaller than the scale of the structure are accelerated by the ion bulk flow. We show that whereas in the small scale structure, ions with gyroradii comparable with the scale of the structure undergo resonance acceleration, and the acceleration in the larger scale structure is more likely due to a spatially limited electric field.
U-Singer: Multi-Singer Singing Voice Synthesizer that Controls Emotional Intensity

We propose U-Singer, the ﬁrst multi-singer emotional singing voice synthesizer that expresses various levels of emotional intensity. During synthesizing singing voices according to the lyrics, pitch, and duration of the music score, U-Singer reﬂects singer characteristics and emotional intensity by adding variances in pitch, energy, and phoneme duration according to singer ID and emotional intensity. Representing all attributes by conditional residual embeddings in a single uniﬁed embedding space, U-Singer controls mutually correlated style attributes, minimizing interference. Additionally, we apply emotion embedding interpolation and extrapolation techniques that lead the model to learn a linear embedding space and allow the model to express emotional intensity levels not included in the training data. In experiments, U-Singer synthesized high-ﬁdelity singing voices reﬂecting the singer ID and emotional intensity. The visualization of the uniﬁed embedding space exhibits that U-singer estimates the correct variations in pitch and energy highly correlated with the singer ID and emotional intensity level. The audio samples are presented at https://u-singer.github. io.
A new wind tunnel for the study of pressure-induced separating and reattaching flows

Abstract The design, construction, and validation of a new academic wind tunnel is described in detail. The wind tunnel is of a classical, blow-down type and generates a pressure-induced, turbulent separation bubble on a flat test surface by a combination of adverse and favorable pressure gradients. The Reynolds number, based on momentum thickness just upstream of separation, is Reθ ≃ 5,000 at a free-stream velocity of Uref = 25ms−1. The length of the separation bubble is estimated at 0°42 ± 0°02m by three different methods. Results of a numerical simulation demonstrate the absence of flow separation in the wind-tunnel contraction. This results in a turbulence level of about 0·05% in the test section. Oil-film visualisation experiments show that the flow near the wall is strongly three-dimensional in the recirculating region and that the topology of the limiting streamlines is consistent with experiments performed on configurations with fixed separation. Finally, spatial variations of the forward-flow fraction have been documented using a thermal-tuft probe and are shown to compare well with the results of the oil-film visualisation.
On Current-Squared Flows and ModMax Theories

We show that the recently introduced ModMax theory of electrodynamics and its Born-Infeld-like generalization are related by a flow equation driven by a quadratic combination of stress-energy tensors. The operator associated to this flow is a 4d4d analogue of the T\overline{T}TT¯ deformation in two dimensions. This result generalizes the observation that the ordinary Born-Infeld Lagrangian is related to the free Maxwell theory by a current-squared flow. As in that case, we show that no analogous relationship holds in any other dimension besides d=4d=4. We also demonstrate that the \mathcal{N}=1𝒩=1 supersymmetric version of the ModMax-Born-Infeld theory obeys a related supercurrent-squared flow which is formulated directly in \mathcal{N} = 1𝒩=1 superspace.
Recursively Feasible Stochastic Predictive Control Using an Interpolating Initial State Constraint

We present a stochastic model predictive control (SMPC) framework for linear systems subject to possibly unbounded disturbances. State-of-the-art SMPC approaches with closed-loop chance constraint satisfaction recursively initialize the nominal state based on the previously predicted nominal state or possibly the measured state under some case distinction. We improve these initialization strategies by allowing for a continuous optimization over the nominal initial state in an interpolation of these two extremes. The resulting SMPC scheme can be implemented as one standard quadratic program and is more flexible compared to state-of-the-art initialization strategies. As the main technical contribution, we show that the proposed SMPC framework also ensures closed-loop satisfaction of chance constraints and suitable performance bounds.
DEVELOPMENT AND VALIDATION OF SYSTEM FOR MEASURING POLING FORCES DURING NORDIC WALKING

Background: Recently, the popularity of Nordic walking (NW) has been rising steadily. Many scientific studies researched the promising and beneficial effects of this form of physical activity. However, only a few studies provided data about the forces acting on the poles. We did not find a commercially available system that enables the measurement of the poling forces. Objective: The objective of this paper was to develop and validate a system for measuring the poling forces during NW. Methods: Strain gauge force tranducers were mounted below the grips of standard NW poles. The transducer signals were amplified and converted to digital form for transmitting to a personal computer. Special software was developed for processing the measured data and the calculation method of output variables was described. Validation of the system was performed using a Kistler force plate. Poling cycles with peak force of about 150 N were imitated by pressing the pole over a force plate. Results: A function sample of the measurement system was constructed. Validation yielded the mean absolute error of 1.1 N in case of poling cycles without pole impacts or 3.0 N in case of poling cycles with impacts. Conclusions: The validation result of our system is comparable to the results of similar systems used for measurements during cross-country skiing. The system enables independent measurement of the poling forces on both poles and the duration of measurement can be up to one hour. The system provides a tool that can be used to answer a number of questions that researches raise about NW. Understanding of the biomechanical and physiological aspects of poling action can constitute a scientific basis for promoting, teaching and training of NW.
Protecting Celebrities with Identity Consistency Transformer

In this work we propose Identity Consistency Trans-former, a novel face forgery detection method that focuses on high-level semantics, speciﬁcally identity information, and detecting a suspect face by ﬁnding identity inconsistency in inner and outer face regions. The Identity Consistency Transformer incorporates a consistency loss for identity consistency determination. We show that Identity Consistency Transformer exhibits superior generalization ability not only across different datasets but also across various types of image degradation forms found in real-world applications including deepfake videos. The Identity Consistency Transformer can be easily enhanced with additional identity information when such information is available, and for this reason it is especially well-suited for detecting face forg-eries involving celebrities.
End-to-End Simulation of 5G Networks Assisted by IRS and AF Relays

The high propagation and penetration loss experienced at millimeter wave (mmWave) frequencies requires ultra-dense deployments of 5th generation (5G) base stations, which may be infeasible and costly for network operators. Integrated Access and Backhaul (IAB) has been proposed to partially address this issue, though raising concerns in terms of power consumption and scalability. Recently, the research community has been investigating Intelligent Reflective Surfaces (IRSs) and Amplify-and-Forward (AF) relays as more energy-efficient alternatives to solve coverage issues in 5G scenarios. Along these lines, this paper relies on a new simulation framework, based on ns-3, to simulate IRS/AF systems with a full-stack, end-to-end perspective, with considerations on the impact of the channel model and the protocol stack of 5G NR networks. Our goal is to demonstrate whether these technologies can be used to relay 5G traffic requests and, if so, how to dimension IRS/AF nodes as a function of the number of end users.
Repeated Robot-Assisted Unilateral Stiffness Perturbations Result in Significant Aftereffects Relevant to Post-Stroke Gait Rehabilitation

Due to hemiparesis, stroke survivors frequently develop a dysfunctional gait that is often characterized by an overall decrease in walking speed and a unilateral decrease in step length. With millions currently affected by this dys-functional gait, robust and effective rehabilitation protocols are needed. Although robotic devices have been used in numerous rehabilitation protocols for gait, the lack of significant afteref-fects that translate to effective therapy makes their application still questionable. This paper proposes a novel type of robot-assisted intervention that results in significant aftereffects that last much longer than any other previous study. With the utilization of a novel robotic device, the Variable Stiffness Treadmill (VST), the stiffness of the walking surface underneath one leg is decreased for a number of steps. This unilateral stiffness perturbation results in a significant aftereffect that is both useful for stroke rehabilitation and often lasts for over 200 gait cycles after the intervention has concluded. More specifically, the aftereffect created is an increase in both left and right step lengths, with the unperturbed step length increasing significantly more than the perturbed. These effects may be helpful in correcting two of the most common issues in post-stroke gait: overall decrease in walking speed and a unilateral shortened step length. The results of this work show that a robot-assisted therapy protocol involving repeated unilateral stiffness perturbations can lead to a more permanent and effective solution to post-stroke gait.
Neveu’s Exchange Formula for Analysis of Wireless Networks With Hotspot Clusters

Theory of point processes, in particular Palm calculus within the stationary framework, plays a fundamental role in the analysis of spatial stochastic models of wireless communication networks. Neveu’s exchange formula, which connects the respective Palm distributions for two jointly stationary point processes, is known as one of the most important results in the Palm calculus. However, its use in the analysis of wireless networks seems to be limited so far and one reason for this may be that the formula in a well-known form is based upon the Voronoi tessellation. In this paper, we present an alternative form of Neveu’s exchange formula, which does not rely on the Voronoi tessellation but includes the one as a special case. We then demonstrate that our new form of the exchange formula is useful for the analysis of wireless networks with hotspot clusters modeled using cluster point processes.
Dojo: A Differentiable Simulator for Robotics

—We present a differentiable rigid-body-dynamics simulator for robotics that prioritizes physical accuracy and differentiability: Dojo. The simulator utilizes an expressive maximal-coordinates representation, achieves stable simulation at low sample rates, and conserves energy and momentum by employing a variational integrator. A nonlinear complementarity problem, with nonlinear friction cones, models hard contact and is reliably solved using a custom primal-dual interior-point method. The implicit-function theorem enables efficient differentiation of an intermediate relaxed problem and computes smooth gradients from the contact model. We demonstrate the usefulness of the simulator and its gradients through a number of examples including: simulation, trajectory optimization, reinforcement learning, and system identification.