Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving Multi-port Memory Performance on Single-Port Memory with Coding Techniques

Many performance critical systems today must rely on performance enhancements, such as multi-port memories, to keep up with the increasing demand of memory-access capacity. However, the large area footprints and complexity of existing multi-port memory designs limit their applicability. This paper explores a coding theoretic framework to address this problem. In particular, this paper introduces a framework to encode data across multiple single-port memory banks in order to algorithmically realize the functionality of multi-port memory. This paper proposes three code designs with significantly less storage overhead compared to the existing replication based emulations of multi-port memories. To further improve performance, we also demonstrate a memory controller design that utilizes redundancy across coded memory banks to more efficiently schedule read and write requests sent across multiple cores. Furthermore, guided by DRAM traces, the paper explores dynamic coding techniques to improve the efficiency of the coding based memory design. We then show significant performance improvements in critical word read and write latency in the proposed coded-memory design when compared to a traditional uncoded-memory design.
Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Deep neural networks have emerged as effective tools for computational imaging including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity, current approaches rely on supervised learning with training examples; consequently, their performance is sensitive to a match of training and imaging settings. Here we propose a new approach to phase microscopy by using an untrained deep neural network for measurement formation, encapsulating the image prior and imaging physics. Our approach does not require any training data and simultaneously reconstructs the sought phase and pupil-plane aberrations by fitting the weights of the network to the captured images. To demonstrate experimentally, we reconstruct quantitative phase from through-focus images blindly (i.e. no explicit knowledge of the aberrations).
Bias in Data-driven AI Systems - An Introductory Survey

AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts on individuals and society. Their decisions might affect everyone, everywhere and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multi-disciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful Machine Learning (ML) algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features like race, sex, etc.
Using Simulated Data to Generate Images of Climate Change

Generative adversarial networks (GANs) used in domain adaptation tasks have the ability to generate images that are both realistic and personalized, transforming an input image while maintaining its identifiable characteristics. However, they often require a large quantity of training data to produce high-quality images in a robust way, which limits their usability in cases when access to data is limited. In our paper, we explore the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming to use the resulting images to raise awareness of the potential future impacts of climate change.
Following Instructions by Imagining and Reaching Visual Goals

While traditional methods for instruction-following typically assume prior linguistic and perceptual knowledge, many recent works in reinforcement learning (RL) have proposed learning policies end-to-end, typically by training neural networks to map joint representations of observations and instructions directly to actions. In this work, we present a novel framework for learning to perform temporally extended tasks using spatial reasoning in the RL framework, by sequentially imagining visual goals and choosing appropriate actions to fulfill imagined goals. Our framework operates on raw pixel images, assumes no prior linguistic or perceptual knowledge, and learns via intrinsic motivation and a single extrinsic reward signal measuring task completion. We validate our method in two environments with a robot arm in a simulated interactive 3D environment. Our method outperforms two flat architectures with raw-pixel and ground-truth states, and a hierarchical architecture with ground-truth states on object arrangement tasks.
Subradiance and superradiance-to-subradiance transition in dilute atomic clouds

We experimentally study subradiance in a dilute cloud of ultracold rubidium (Rb) atoms where $n \lambda_a^3 \approx 10^{-2}$ ($n$: atomic density, $\lambda_a$ excitation wavelength) and the on-resonance optical depth of the cloud is of order unity. We show that in the strong excitation regime, the subradiant time-scales depend on the excitation fraction of the cloud; i.e., to the intensity of the excitation pulse. In this regime, the decay dynamics are highly complicated and there is not a single decay time-constant. Instead, the decay time constant varies during the dynamics. Specifically, we were able to observe signatures of superradiant-to-subradiant transition; i. e. , initially the decay rate is faster than independent decay (superradiant emission), while at later times it transitions to slower (subradiant emission). We also discuss a theoretical model whose numerical results are in good agreement with the experiments.
Hierarchical Reinforcement Learning for Self-Driving Decision-Making without Reliance on Labeled Driving Data

Decision making for self-driving cars is usually tackled by manually encoding rules from drivers' behaviours or imitating drivers' manipulation using supervised learning techniques. Both of them rely on mass driving data to cover all possible driving scenarios. This study presents a hierarchical reinforcement learning method for decision making of self-driving cars, which does not depend on a large amount of labelled driving data. This method comprehensively considers both high-level manoeuvre selection and low-level motion control in both lateral and longitudinal directions. The authors firstly decompose the driving tasks into three manoeuvres, including driving in lane, right lane change and left lane change, and learn the sub-policy for each manoeuvre. Then, a master policy is learned to choose the manoeuvre policy to be executed in the current state. All policies, including master policy and manoeuvre policies, are represented by fully-connected neural networks and trained by using asynchronous parallel reinforcement learners, which builds a mapping from the sensory outputs to driving decisions. Different state spaces and reward functions are designed for each manoeuvre. They apply this method to a highway driving scenario, which demonstrates that it can realise smooth and safe decision making for self-driving cars.
Complex emission patterns: fluctuations and bistability of polar-cap potentials

Development of the ion-proton pulsar model extends it to the limit of large unscreened polar-cap potentials, for example, as in the Vela pulsar, in which ion charges differ only by small increments from their complete screening values. It is shown that the atomic number Z of an ion following its passage from the canonical Z_0 = 26 value through the electromagnetic shower region to the surface is not necessarily time-independent but can vary between fixed limits in an irregular or quasi-periodic way in a characteristic time of the order of 10^4 s. Thus at a certain Z the system may transition to an unstable state of higher electric potential and it is argued that this is the physical basis for mode-changes, long-term nulls, periodic or otherwise. The model requires an orientation of magnetic dipole moment relative to rotational spin giving a positive corotational charge density. Success of the model would fix the particle composition of the remaining parts of the magnetosphere, including the Y-point and is therefore relevant to X-ray and gamma-ray emission processes.
The Megamaser Cosmology Project. XIII. Combined Hubble Constant Constraints

We present a measurement of the Hubble constant made using geometric distance measurements to megamaser-hosting galaxies. We have applied an improved approach for fitting maser data and obtained better distance estimates for four galaxies previously published by the Megamaser Cosmology Project: UGC 3789, NGC 6264, NGC 6323, and NGC 5765b. Combining these updated distance measurements with those for the maser galaxies CGCG 074-064 and NGC 4258, and assuming a fixed velocity uncertainty of 250 km s−1 associated with peculiar motions, we constrain the Hubble constant to be H0 = 73.9 ± 3.0 km s−1 Mpc−1 independent of distance ladders and the cosmic microwave background. This best value relies solely on maser-based distance and velocity measurements, and it does not use any peculiar velocity corrections. Different approaches for correcting peculiar velocities do not modify H0 by more than ±1σ, with the full range of best-fit Hubble constant values spanning 71.8–76.9 km s−1 Mpc−1. We corroborate prior indications that the local value of H0 exceeds the early-universe value, with a confidence level varying from 95% to 99% for different treatments of the peculiar velocities.
Secondary Use of Electronic Health Record: Opportunities and Challenges

In the present technological era, healthcare providers generate huge amounts of clinical data on a daily basis. Generated clinical data is stored digitally in the form of Electronic Health Record (EHR) as a central data repository of hospitals. Data contained in EHR is not only used for the patients’ primary care but also for various secondary purposes such as clinical research, automated disease surveillance and clinical audits for quality enhancement. Using EHR data for secondary purposes without consent or in some cases even with consent creates privacy issues. Secondly, EHR data is also made accessible to various stakeholders including different government agencies at various geographical sites through wired or wireless networks. Sharing of EHR across multiple agencies makes it vulnerable to cyber attacks and also makes it difficult to implement strict privacy laws as in some cases data is shared with organization that is governed by specific regional law. Privacy of individuals could be severely affected when their sensitive private information contained in EHR is leaked or exposed to the public. Data leaks can cause financial losses or an individual may encounter social boycott if his / her medical condition is exposed in public. To protect patients personal data from such threats, there exists different privacy regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) and My Health Record (MHR). However, continually evolving state-of-the-art techniques in Machine Learning (ML), Data Analytics (DA) and hacking are making it even more difficult to completely protect an individual’s/patient’s privacy. In this article, we have systematically examined various secondary uses of EHR with the aim to highlight how these secondary uses affect patients’ privacy. Secondly, we have critically analyzed GDPR & HIPAA regulations and highlighted their possible areas of improvement, considering escalating use of technology and different secondary uses of EHR.
Gravitational Microlensing Events from the First Year of the Northern Galactic Plane Survey by the Zwicky Transient Facility

The Zwicky Transient Facility (ZTF) (Bellm et al. 2019; Graham et al. 2019; Masci et al. 2019) is currently surveying the entire northern sky, including dense Galactic plane fields. Here, we present preliminary results of the search for gravitational microlensing events in the ZTF data collected from the beginning of the survey (2018 March 20) through 2019 June 30.
Developing Multi-Task Recommendations with Long-Term Rewards via Policy Distilled Reinforcement Learning

With the explosive growth of online products and content, recommendation techniques have been considered as an effective tool to overcome information overload, improve user experience, and boost business revenue. In recent years, we have observed a new desideratum of considering long-term rewards of multiple related recommendation tasks simultaneously. The consideration of long-term rewards is strongly tied to business revenue and growth. Learning multiple tasks simultaneously could generally improve the performance of individual task due to knowledge sharing in multi-task learning. While a few existing works have studied long-term rewards in recommendations, they mainly focus on a single recommendation task. In this paper, we propose {\it PoDiRe}: a \underline{po}licy \underline{di}stilled \underline{re}commender that can address long-term rewards of recommendations and simultaneously handle multiple recommendation tasks. This novel recommendation solution is based on a marriage of deep reinforcement learning and knowledge distillation techniques, which is able to establish knowledge sharing among different tasks and reduce the size of a learning model. The resulting model is expected to attain better performance and lower response latency for real-time recommendation services. In collaboration with Samsung Game Launcher, one of the world's largest commercial mobile game platforms, we conduct a comprehensive experimental study on large-scale real data with hundreds of millions of events and show that our solution outperforms many state-of-the-art methods in terms of several standard evaluation metrics.
Robust Gaussian Joint Source-Channel Coding with a Staircase Distortion-Noise Profile

Minimum energy required to achieve a distortion-noise profile, i.e., a function indicating the maximum allowed distortion value for each channel noise level. In this paper, the minimum energy required to achieve a distortion noise profile is studied for Gaussian sources which are transmitted robustly over Gaussian channels. We provide upper bound for the minimum energy behavior of the staircase profile using our proposed coding scheme. Conversely, utilizing a family of lower bounds originally derived for broadcast channels with power constraints, the minimum required energy is lower bounded for staircase profile.
Static Pull-In Behavior of Hybrid Levitation Microactuators: Simulation, Modeling, and Experimental Study

In this article, a systematic and comprehensive approach based on finite element analysis and analytical modeling for studying static pull-in phenomena in hybrid levitation microactuators is presented. A finite element model of electromagnetic levitation microactuators based on the Lagrangian formalism is formulated and developed as a result of recent progress in the analytical calculation of mutual inductance between filament loops. In particular, the developed finite element model allows us to calculate accurately and efficiently a distribution of induced eddy current within a levitated micro-object. At the same time, this fact provides a reason for formulating the analytical model in which the distribution of the induced eddy current can be approximated by one circuit represented by a circular filament. In turn, both developed models predict the static pull-in parameters of hybrid levitation microactuators without needs for solving nonlinear differential equations. The results of modeling obtained by means of the developed quasi-finite element and analytical model are verified by the comparison with experimental results.
Micro Fresnel mirror array with individual mirror control

We present the design and fabrication of a miniaturized array of piezoelectrically actuated high speed Fresnel mirrors with individual mirror control. These Fresnel mirrors can be used to generate propagation invariant and self-healing interference patterns. The mirrors are actuated using piezobimorph actuators, and the consequent change of the tilting angle of the mirrors changes the fringe spacing of the interference pattern generated. The array consists of four Fresnel mirrors each having an area of 2 × 2 mm2 arranged in a 2x2 configuration. The device, optimized using FEM simulations, is able to achieve maximum mirror deflections of 15 mrad, and has a resonance frequency of 28 kHz.
Infinitary commutativity and fundamental groups of topological monoids.

The well-known Eckmann-Hilton Principle may be applied to prove that fundamental groups of $H$-spaces are commutative. In this paper, we identify an infinitary analogue of the Eckmann-Hilton Principle that applies to fundamental groups of all topological monoids and slightly more general objects called pre-$\Delta$-monoids. In particular, we show that every pre-$\Delta$-monoid $M$ is "transfinitely $\pi_1$-commutative" in the sense that permutation of the factors of any infinite loop-concatenation indexed by a countably infinite order and based at the identity $e\in M$ is a homotopy invariant action. We also give a detailed account of fundamental groups of James reduced products and apply transfinite $\pi_1$-commutativity to make several computations.
Discovering Transforms: A Tutorial on Circulant Matrices, Circular Convolution, and the Discrete Fourier Transform.

How could the Fourier and other transforms be naturally discovered if one didn't know how to postulate them? In the case of the Discrete Fourier Transform (DFT), we show how it arises naturally out of analysis of circulant matrices. In particular, the DFT can be derived as the change of basis that simultaneously diagonalizes all circulant matrices. In this way, the DFT arises naturally from a linear algebra question about a set of matrices. Rather than thinking of the DFT as a signal transform, it is more natural to think of it as a single change of basis that renders an entire set of mutually-commuting matrices into simple, diagonal forms. The DFT can then be ``discovered'' by solving the eigenvalue/eigenvector problem for a special element in that set. A brief outline is given of how this line of thinking can be generalized to families of linear operators, leading to the discovery of the other common Fourier-type transforms, as well as its connections with group representations theory.
Data Techniques For Online End-to-end Speech Recognition

Practitioners often need to build ASR systems for new use cases in a short amount of time, given limited in-domain data. While recently developed end-to-end methods largely simplify the modeling pipelines, they still suffer from the data sparsity issue. In this work, we explore a few simple-to-implement techniques for building online ASR systems in an end-to-end fashion, with a small amount of transcribed data in the target domain. These techniques include data augmentation in the target domain, domain adaptation using models previously trained on a large source domain, and knowledge distillation on non-transcribed target domain data; they are applicable in real scenarios with different types of resources. Our experiments demonstrate that each technique is independently useful in the low-resource setting, and combining them yields significant improvement of the online ASR performance in the target domain.
Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience

The wide adoption of Machine Learning technologies has created a rapidly growing demand for people who can train ML models. Some advocated the term "machine teacher" to refer to the role of people who inject domain knowledge into ML models. One promising learning paradigm is Active Learning (AL), by which the model intelligently selects instances to query the machine teacher for labels. However, in current AL settings, the human-AI interface remains minimal and opaque. We begin considering AI explanations as a core element of the human-AI interface for teaching machines. When a human student learns, it is a common pattern to present one's own reasoning and solicit feedback from the teacher. When a ML model learns and still makes mistakes, the human teacher should be able to understand the reasoning underlying the mistakes. When the model matures, the machine teacher should be able to recognize its progress in order to trust and feel confident about their teaching outcome. Toward this vision, we propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an AL setting. We conducted an empirical study comparing the model learning outcomes, feedback content and experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation). Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload. Our study also reveals important individual factors that mediate a machine teacher's reception to AI explanations, including task knowledge, AI experience and need for cognition. By reflecting on the results, we suggest future directions and design implications for XAL.
Metastable attractors explain the variable timing of stable behavioral action sequences

Natural animal behavior displays rich lexical and temporal dynamics, even in a stable environment. This implies that behavioral variability arises from sources within the brain, but the origin and mechanics of these processes remain largely unknown. Here, we focus on the observation that the timing of self-initiated actions shows large variability even when they are executed in stable, well-learned sequences. Could this mix of reliability and stochasticity arise within the same circuit? We trained rats to perform a stereotyped sequence of self-initiated actions and recorded neural ensemble activity in secondary motor cortex (M2), which is known to reflect trial-by-trial action timing fluctuations. Using hidden Markov models we established a robust and accurate dictionary between ensemble activity patterns and actions. We then showed that metastable attractors, representing activity patterns with the requisite combination of reliable sequential structure and high transition timing variability, could be produced by reciprocally coupling a high dimensional recurrent network and a low dimensional feedforward one. Transitions between attractors were generated by correlated variability arising from the feedback loop between the two networks. This mechanism predicted a specific structure of low-dimensional noise correlations that were empirically verified in M2 ensemble dynamics. This work suggests a robust network motif as a novel mechanism to support critical aspects of animal behavior and establishes a framework for investigating its circuit origins via correlated variability.