Miracle-dz/UniFAR-FTUs huggingface dataset:
Applications of Deep Learning to the Design of Enhanced Wireless Communication Systems

Innovation in the physical layer of communication systems has traditionally been achieved by breaking down the transceivers into sets of processing blocks, each optimized independently based on mathematical models. Conversely, deep learning (DL)-based systems are able to handle increasingly complex tasks for which no tractable models are available. This thesis aims at comparing different approaches to unlock the full potential of DL in the physical layer. First, we describe a neural network (NN)-based block strategy, where an NN is optimized to replace a block in a communication system. We apply this strategy to introduce a multi-user multiple-input multiple-output (MU-MIMO) detector that builds on top of an existing DL-based architecture. Second, we detail an end-to-end strategy, in which the transmitter and receiver are modeled as an autoencoder. This approach is illustrated with the design of waveforms that achieve high throughputs while satisfying peak-to-average power ratio (PAPR) and adjacent channel leakage ratio (ACLR) constraints. Lastly, we propose a hybrid strategy, where multiple DL components are inserted into a traditional architecture but are trained to optimize the end-to-end performance. To demonstrate its benefits, we propose a DL-enhanced MU-MIMO receiver that both enable lower bit error rates (BERs) compared to a conventional receiver and remains scalable to any number of users. Each approach has its own strengths and shortcomings. While the first one is the easiest to implement, its individual block optimization does not ensure the overall system optimality. On the other hand, systems designed with the second approach are computationally complex but allow for new opportunities such as pilotless transmissions. Finally, the combined flexibility and end-to-end performance gains of the third approach motivate its use for short-term practical implementations.
Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We then develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states. The collision probability is calculated by summing up the probabilities of the states where two vehicles spatially overlap. Simulation results show that the prediction model has superior performance in terms of vehicle motion position errors, and the proposed collision detection approach is agile and effective to identify the collision in cut-in crash events.
Exploiting Informatics in Clinical Decision Support

The Diagnostic Cycle (2) THE DIAGNOSTIC PROCESS The diagnostic process can be thought of as an iterative cycle (Figure 1) in which clinicians form hypotheses based on clinical examination of the patient and order investigations to rule in or rule out diagnoses. The cycle is iterative in that the information gained by each testing cycle feeds forward into the next cycle informing the selection of subsequent tests (Figure 2). In acute disease states the cycle may turn only once or twice and for sake of speed many tests may be ordered in parallel. In chronic disease states the cycle may continue indefinitely, though under those circumstances ongoing monitoring and management of disease are the goals rather than the generation of diagnostic evidence. INTRODUCTION The modern hospital with its growing IT infrastructure provides fertile ground for the development and exploitation of clinical decision support systems (1). The combination of the increasing power and sophistication of information systems and the rapid development of networks spanning the clinical domain presents the laboratory with unprecedented opportunities to expand its role into the clinical arena. In particular the increasing interdependence of clinical and diagnostic services makes this area a focus for the early implementation of such systems. To date the main emphasis of information systems in the laboratory environment has been on control of analytical systems and administrative support of operational processes (2,3,4). Support for the use of analytical results in clinical decision-making, apart from a few isolated examples such as in thyroid test interpretation, has generally been a manual process exploiting the skills of senior medical and scientific staff: This short paper will explore some of the issues involved in the provision of automated clinical information support drawing on examples of existing systems. It should help readers to fully understand the requirements for future systems and be in a better position to exploit such systems to enhance the performance of their laboratories. Dr Richard Jones, MA, OM, MRCP, MRCPath, Institute of Pathology, Leeds Teaching Hospitals NHS Trust and University of Leeds, Leeds LSI 3EX, UK Tel: (44) 113233 5677, Fax: (44) 113233 5672 Email : r.g. jones@leeds.ac. uk.
Axion wormholes with massive dilaton

If Euclidean wormholes contribute meaningfully to the path integral of quantum gravity they can have important implications for particle physics and cosmology. The dominant effects arise from wormholes whose sizes are comparable to the cut-off scale of effective field theory, for which ultraviolet corrections become relevant. We study corrections to classical axion wormhole solutions in string motivated scenarios in which the dilaton partner of the axion becomes massive. We find corrections near the neck region which are consistent with a recent version of the weak gravity conjecture for axions.
Initial Access for Millimeter-Wave and Terahertz Communications with Hybrid Beamforming

In order to achieve terabits-per-second (Tbps) data rates in the sixth-generation (6G) mobile system, wireless communications are required to exploit the abundant spectrum in the millimeter-wave (mmWave) and terahertz (THz) bands. However, high-frequency transmission heavily relies on high beamforming gain to compensate for severe propagation loss. A beam-based system faces a barrier in the process of initial access, where a base station must broadcast synchronization signals and system information to all users within its coverage. Hence, this paper proposes a novel omnidirectional broadcasting scheme for mmWave and THz systems with hybrid beamforming. It provides an instantaneously equal gain over all directions by forming complementary beams over sub-arrays. Numerical results verify that it can achieve omnidirectional coverage with a performance that remarkably outperforms the previous scheme.
Extended Abstract: Productive Parallel Programming with Parsl

Parsl is a parallel programming library for Python that aims to make it easy to specify parallelism in programs and to realize that parallelism on arbitrary parallel and distributed computing systems. Parsl relies ondevelopers annotating Python functions—wrapping either Python or external applications—to indicate that these functions may be executed concurrently. Developers can then link to-gether functions via the exchange of data. Parsl establishes a dynamic dependency graph and sends tasks for execution on con-nected resources when dependencies are resolved. Parsl’s runtime system enables diﬀerent compute resources to be used, from lap-topstosupercomputers, withoutmodiﬁcationtothe Parsl program. Extended Abstract: Productive Parallel Programming with Parsl. In HILT ’20: Workshop on Safe Languages and Technologies for Structured and Eﬃcient Paral- lel and Distributed/Cloud Computing. ACM, USA, pages.
Ultrafast imaging of coherent polariton propagation and interactions

Semiconductor excitations can hybridize with cavity photons to form exciton-polaritons (EPs) with remarkable properties, including light-like energy flow combined with matter-like interactions. To fully harness these properties, EPs must maintain long-range coherence despite exciton-mediated interactions with lattice phonons. To address this open question, we develop a nonlinear momentum-resolved optical approach that images EPs in real space on femtosecond scales. We directly visualize EP wavepacket propagation in two-dimensional halide perovskite microcavities, allowing precise quantification of EP interactions with lattice phonons and reservoir exciton states throughout their lifetimes. We show that EP-lattice interactions strongly renormalize EP group velocities at high exciton content without sacrificing long-range coherence, enabling ballistic propagation to be maintained for up to half-exciton EPs. These results concur with our quantum simulations of dynamic disorder shielding through light-matter hybridization. We extend our measurements to molecular polaritons and self-hybridized EPs in two-dimensional materials, testifying to the remarkable generalizability of our approach.
Volatility Sensitive Bayesian Estimation of Portfolio VaR and CVaR

In this paper, a new way to integrate volatility information for estimating value at risk (VaR) and conditional value at risk (CVaR) of a portfolio is suggested. The new method is developed from the perspective of Bayesian statistics and it is based on the idea of volatility clustering. By specifying the hyperparameters in a conjugate prior based on two different rolling window sizes, it is possible to quickly adapt to changes in volatility and automatically specify the degree of certainty in the prior. This constitutes an advantage in comparison to existing Bayesian methods that are less sensitive to such changes in volatilities and also usually lack standardized ways of expressing the degree of belief. We illustrate our new approach using both simulated and empirical data. Compared to some other well known homoscedastic and heteroscedastic models, the new method provides a good alternative for risk estimation, especially during turbulent periods where it can quickly adapt to changing market conditions.
Uniform Recalibration of Common Spectrophotometry Standard Stars onto the CALSPEC System Using the SuperNova Integral Field Spectrograph

We calibrate spectrophotometric optical spectra of 32 stars commonly used as standard stars, referenced to 14 stars already on the Hubble Space Telescope–based CALSPEC flux system. Observations of CALSPEC and non-CALSPEC stars were obtained with the SuperNova Integral Field Spectrograph over the wavelength range 3300–9400 Å as calibration for the Nearby Supernova Factory cosmology experiment. In total, this analysis used 4289 standard-star spectra taken on photometric nights. As a modern cosmology analysis, all presubmission methodological decisions were made with the flux scale and external comparison results blinded. The large number of spectra per star allows us to treat the wavelength-by-wavelength calibration for all nights simultaneously with a Bayesian hierarchical model, thereby enabling a consistent treatment of the Type Ia supernova cosmology analysis and the calibration on which it critically relies. We determine the typical per-observation repeatability (median 14 mmag for exposures ≳5 s), the Maunakea atmospheric transmission distribution (median dispersion of 7 mmag with uncertainty 1 mmag), and the scatter internal to our CALSPEC reference stars (median of 8 mmag). We also check our standards against literature filter photometry, finding generally good agreement over the full 12 mag range. Overall, the mean of our system is calibrated to the mean of CALSPEC at the level of ∼3 mmag. With our large number of observations, careful cross-checks, and 14 reference stars, our results are the best calibration yet achieved with an integral-field spectrograph, and among the best calibrated surveys.
Differentially Private Subgraph Counting in the Shuffle Model

Subgraph counting is fundamental for analyzing connection patterns or clustering tendencies in graph data. Recent studies have applied LDP (Local Differential Privacy) to subgraph counting to protect user privacy even against a data collector in both centralized and decentralized social networks. However, existing local algorithms suffer from extremely large estimation errors or assume multi-round interaction between users and the data collector, which requires a lot of user effort and synchronization. In this paper, we focus on a one-round of interaction and propose accurate subgraph counting algorithms by introducing a recently studied shuffle model. We first propose a basic technique called wedge shuffling to send wedge information, the main component of several subgraphs, with small noise. Then we apply our wedge shuffling to counting triangles and 4-cycles – basic subgraphs for analyzing clustering tendencies – with several additional techniques. We also show upper bounds on the estimation error for each algorithm. We show through comprehensive experiments that our one-round shuffle algorithms significantly outperform the one-round local algorithms in terms of accuracy and achieve small estimation errors with a reasonable privacy budget, e.g., smaller than 1 in edge DP.
Level-Based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector Multiplication

The multiplication of a sparse matrix with a dense vector (SpMV) is a key component in many numerical schemes and its performance is known to be severely limited by main memory access. Several numerical schemes require the multiplication of a sparse matrix polynomial with a dense vector which is typically implemented as a sequence of SpMVs. This results in low performance and ignores the potential to increase the arithmetic intensity by reusing the matrix data from cache. In this work we use the recursive algebraic coloring engine (RACE) to enable blocking of sparse matrix data across the polynomial computations. In the graph representing the sparse matrix we form levels using a breadth-first search. Locality relations of these levels are then used to improve spatial and temporal locality when accessing the matrix data and to implement an efficient multithreaded parallelization. Our approach is independent of the matrix structure and avoids shortcomings of existing “blocking” strategies in terms of hardware efficiency and parallelization overhead. We quantify the quality of our implementation using performance modelling and demonstrate speedups of up to 3× and 5× compared to an optimal SpMV-based baseline on a single multicore chip of recent Intel and AMD architectures. Various numerical schemes like <inline-formula><tex-math notation="LaTeX">$s$</tex-math><alternatives><mml:math><mml:mi>s</mml:mi></mml:math><inline-graphic xlink:href="alappat-ieq1-3223512.gif"/></alternatives></inline-formula>-step Krylov solvers, polynomial preconditioners and power clustering algorithms will benefit from our development.
Novel cosmological bounds on thermally-produced axion-like particles

We constrain the coupling of thermally-produced axion-like particles (here axions) with photons and gluons, using data from the cosmic microwave background (CMB) spectra and baryon acoustic oscillations. The axion possesses an explicit soft breaking mass term and it is produced thermally in the early Universe from either axion-photon or axion-gluon processes, accounting for the recent progresses in the field. We derive the most stringent bounds on the axion-gluon coupling to date on the mass range considered 10-4 ≲ ma / eV ≲ 100, superseding the current bounds from SN1987A. The bounds on the axion-photon coupling are competitive with the results from the CAST collaboration for the axion mass ma ≳ 3eV. We comment on the forecast reaches that will be available given the sensitivity of future CMB-S4 missions.
Group Separation Strikes Back

Group languages are regular languages recognized by finite groups, or equivalently by finite automata in which each letter induces a permutation on the set of states. We investigate the separation problem for this class of languages: given two arbitrary regular languages as input, we show how to decide if there exists a group language containing the first one while being disjoint from the second. We prove that covering, a problem generalizing separation, is decidable. A simple covering algorithm was already known: it can be obtained indirectly as a corollary of an algebraic theorem by Ash. Unfortunately, while deducing the algorithm from this algebraic result is straightforward, all proofs of Ash’s result itself require a strong background on algebraic concepts, and a wealth of technical machinery outside of automata theory. Our proof is independent previous ones. It relies exclusively on standard notions from automata theory: we directly deal with separation and work with input languages represented by nondeterministic finite automata.We also investigate two strict subclasses. First, the alphabet modulo testable languages are those defined by counting the occurrences of each letter modulo some fixed integer (equivalently, they are the languages recognized by a commutative group). Secondly, the modulo languages are those defined by counting the length of words modulo some fixed integer. We prove that covering is decidable for both classes, with algorithms that rely on the construction made for group languages.Our proofs lead to tight complexity bounds for separation for all three classes, as well as for covering for both alphabet modulo testable languages and for modulo testable languages.
Improving Dual-Microphone Speech Enhancement by Learning Cross-Channel Features with Multi-Head Attention

Hand-crafted spatial features, such as inter-channel intensity difference (IID) and inter-channel phase difference (IPD), play a fundamental role in recent deep learning based dual-microphone speech enhancement (DMSE) systems. However, learning the mutual relationship between artificially designed spatial and spectral features is hard in the end-to-end DMSE. In this work, a novel architecture for DMSE using a multi-head cross-attention based convolutional recurrent network (MHCA-CRN) is presented. The proposed MHCA-CRN model includes a channel-wise encoding structure for preserving intra-channel features and a multi-head cross-attention mechanism for fully exploiting cross-channel features. In addition, the proposed approach specifically formulates the decoder with an extra SNR estimator to estimate frame-level SNR under a multi-task learning framework, which is expected to avoid speech distortion led by end-to-end DMSE module. Finally, a spectral gain function is adopted to further suppress the unnatural residual noise. Experiment results demonstrated superior performance of the proposed model against several state-of-the-art models.
Does a Program Yield the Right Distribution? Verifying Probabilistic Programs via Generating Functions

We study discrete probabilistic programs with potentially unbounded looping behaviors over an infinite state space. We present, to the best of our knowledge, the first decidability result for the problem of determining whether such a program generates exactly a specified distribution over its outputs (provided the program terminates almost surely). The class of distributions that can be specified in our formalism consists of standard distributions (geometric, uniform, etc.) and finite convolutions thereof. Our method relies on representing these (possibly infinite-support) distributions as probability generating functions which admit effective arithmetic operations. We have automated our techniques in a tool called prodigy, which supports automatic invariance checking, compositional reasoning of nested loops, and efficient queries on various quantities of to the output distribution, as demonstrated by experiments.
Critical balance and scaling of strongly stratified turbulence at low Prandtl number

Abstract We extend the scaling relations of strongly (stably) stratified turbulence from the geophysical regime of unity Prandtl number to the astrophysical regime of extremely small Prandtl number applicable to stably stratified regions of stars and gas giants. A transition to a new turbulent regime is found to occur when the Prandtl number drops below the inverse of the buoyancy Reynolds number, i.e. $Pr\,Rb<1$, which signals a shift of the dominant balance in the buoyancy equation. Application of critical balance arguments then derives new predictions for the anisotropic energy spectrum and dominant balance of the Boussinesq equations in the $Pr\,Rb\ll 1$ regime. We find that all the standard scaling relations from the unity $Pr$ limit of strongly stratified turbulence simply carry over if the Froude number, $Fr$, is replaced by a modified Froude number, $Fr_M\equiv Fr/(Pr\,Rb)^{1/4}$. The geophysical and astrophysical regimes are thus smoothly connected across the $Pr\,Rb=1$ transition. Applications to vertical transport in stellar radiative zones and modification to the instability criterion for the small-scale dynamo are discussed.
Episodic Memory Question Answering

Egocentric augmented reality devices such as wearable glasses passively capture visual data as a human wearer tours a home environment. We envision a scenario wherein the human communicates with an AI agent powering such a device by asking questions (e.g., “where did you last see my keys? ”). In order to succeed at this task, the egocentric AI assistant must (1) construct semantically rich and efficient scene memories that encode spatio-temporal infor-mation about objects seen during the tour and (2) possess the ability to understand the question and ground its answer into the semantic memory representation. Towards that end, we introduce (1) a new task - Episodic Memory Question Answering (EMQA) wherein an egocentric AI assistant is provided with a video sequence (the tour) and a question as an input and is asked to localize its answer to the question within the tour, (2) a dataset of grounded questions designed to probe the agent's spatio-temporal understanding of the tour, and (3) a model for the task that encodes the scene as an allocentric, top-down semantic feature map and grounds the question into the map to localize the answer. We show that our choice of episodic scene memory outperforms naive, off-the-shelf solutions for the task as well as a host of very competitive baselines and is robust to noise in depth, pose as well as camera jitter.
Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy

We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public policy. While such algorithms have well-characterized performance in the private sector (e.g., online advertising), the tendency to naively apply algorithms motivated by one domain, often online advertisements, can be called the ''advertisement fallacy. '' Our main thesis is that law and public policy pose distinct methodological challenges that the machine learning community has not yet addressed. Machine learning will need to address these methodological problems to move ''beyond ads. '' Public law, for instance, can pose multiple objectives, necessitate batched and delayed feedback, and require systems to learn rational, causal decision-making policies, each of which presents novel questions at the research frontier. We discuss a wide range of potential applications of sequential decision-making algorithms in regulation and governance, including public health, environmental protection, tax administration, occupational safety, and benefits adjudication. We use these examples to highlight research needed to render sequential decision making policy-compliant, adaptable, and effective in the public sector. We also note the potential risks of such deployments and describe how sequential decision systems can also facilitate the discovery of harms. We hope our work inspires more investigation of sequential decision making in law and public policy, which provide unique challenges for machine learning researchers with potential for significant social benefit.