Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-Stack End-to-End mmWave Simulations Using 3GPP and NYUSIM Channel Model in ns-3

Accurate channel modeling and simulation tools are vital for studying sub-THz and millimeter (mmWave) wideband communication system performance. To accurately design future high data rate, low latency wireless modems, the entire protocol stack must be appropriately modeled to understand how the physical layer impacts the end-to-end performance experienced by the end user. This paper presents a full stack end-to-end performance analysis in ns-3 using drop-based NYU channel model (NYUSIM) and 3GPP statistical channel model (SCM) in scenarios, namely urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), and indoor hotspot (InH) at 28 GHz with 100 MHz bandwidth. Video data is transmitted at 50 Mbps using User Datagram Protocol (UDP), and we observe that the RMa channel is benign in non-line of sight (NLOS) for NYUSIM and 3GPP SCM as it exhibits no packet drops and yields maximum throughput (48.1 Mbps) and latency of ∼ 20 ms. In NLOS, for NYUSIM, the UMa and RMa channels are similar in terms of throughput and packet drops, and the latency in UMi and InH scenarios is 10 times and 25 times higher respectively compared to UMa. Our results indicate that mmWave bands can support data rates of 50 Mbps with negligible packet drops and latency below 150 ms in all scenarios using NYUSIM.
The calculation of capacitance and force in electrostatic fields by using the boundary element method

Methods of calculating the integrated parameters, capacitance and force, in electrostatic fields by using boundary element analysis are discussed. Force is calculated using Maxwell stress. It is concluded that the integrated parameters can be obtained with very high accuracy by using the boundary element method, due to its highly accurate field solutions. Both capacitance and force can be calculated from integrations performed on the boundary or in the field space, and the integration on the boundary needs much less calculation work than the integration in the field space. For capacitance, boundary integration results are quite accurate, without the need for the space integration. >
Physics Informed Deep Learning: Applications in Transportation

A recent development in machine learning - physics-informed deep learning (PIDL) - presents unique advantages in transportation applications such as traffic state estimation. Consolidating the benefits of deep learning (DL) and the governing physical equations, it shows the potential to complement traditional sensing methods in obtaining traffic states. In this paper, we first explain the conservation law from the traffic flow theory as ``physics'', then present the architecture of a PIDL neural network and demonstrate its effectiveness in learning traffic conditions of unobserved areas. In addition, we also exhibit the data collection scenario using fog computing infrastructure. A case study on estimating the vehicle velocity is presented and the result shows that PIDL surpasses the performance of a regular DL neural network with the same learning architecture, in terms of convergence time and reconstruction accuracy. The encouraging results showcase the broad potential of PIDL for real-time applications in transportation with a low amount of training data.
Bandwidth selection for local linear regression smoothers

Summary. The paper presents a general strategy for selecting the bandwidth of nonparametric regression estimators and specializes it to local linear regression smoothers. The procedure requires the sample to be divided into a training sample and a testing sample. Using the training sample we first compute a family of regression smoothers indexed by their bandwidths. Next we select the bandwidth by minimizing the empirical quadratic prediction error on the testing sample. The resulting bandwidth satisfies a finite sample oracle inequality which holds for all bounded regression functions. This permits asymptotically optimal estimation for nearly any regression function. The practical performance of the method is illustrated by a simulation study which shows good finite sample behaviour of our method compared with other bandwidth selection procedures.
Characterizing chaos through Lyapunov metrics

Science, engineering, medicine, biology and many other areas deal with signals acquired in the form of time series from different dynamical systems for the purpose of analysis, diagnosis and control of the systems. The signals are often mixed with noise. Separating the noise from the signal may be very difficult if both the signal and the noise are broadband. The problem becomes inherently difficult when the signal is chaotic because its power spectrum is indistinguishable from a broadband noise. This paper describes how to measure and analyze chaos using Lyapunov metrics. The principle of characterizing strange attractors by the divergence and folding of trajectories is studied. A practical approach to evaluating the largest local and global Lyapunov exponents by rescaling and renormalization leads to calculating the m Lyapunov exponents for m-dimensional strange attractors either modeled explicitly (analytically), or reconstructed from experimental time-series data. Several practical algorithms for calculating Lyapunov exponents are summarized. The Lyapunov fractal dimension and Kolmogorov-Sinai and Renyi entropies are also described as they are related to the Lyapunov exponents.
Decoupling the All-Reduce Primitive for Accelerating Distributed Deep Learning

—Communication scheduling has been shown to be effective in accelerating distributed training, which enables all-reduce communications to be overlapped with backpropagation computations. This has been commonly adopted in popular distributed deep learning frameworks. However, there exist two fundamental problems: (1) excessive startup latency proportional to the number of workers for each all-reduce operation; (2) it only achieves sub-optimal training performance due to the dependency and synchronization requirement of the feed-forward computation in the next iteration. We propose a novel scheduling algorithm, DeAR, that decouples the all-reduce primitive into two continuous operations, which overlaps with both backpropagation and feed-forward computations without extra communications. We further design a practical tensor fusion algorithm to improve the training performance. Experimental results with ﬁve popular models show that DeAR achieves up to 83% and 15% training speedup over the state-of-the-art solutions on a 64-GPU cluster with 10Gb/s Ethernet and 100Gb/s InﬁniBand interconnects, respectively.
FedPDC: Federated Learning for Public Dataset Correction

As people pay more and more attention to privacy protection, Federated Learning (FL), as a promising distributed machine learning paradigm, is receiving more and more attention. However, due to the biased distribution of data on devices in real life, federated learning has lower classification accuracy than traditional machine learning in Non-IID scenarios. Although there are many optimization algorithms, the local model aggregation in the parameter server is still relatively traditional. In this paper, a new algorithm FedPDC is proposed to optimize the aggregation mode of local models and the loss function of local training by using the shared data sets in some industries. In many benchmark experiments, FedPDC can effectively improve the accuracy of the global model in the case of extremely unbalanced data distribution, while ensuring the privacy of the client data. At the same time, the accuracy improvement of FedPDC does not bring additional communication costs.
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis

Synthetic data is becoming an increasingly promising technology, and successful applications can improve privacy, fairness, and data democratization. While there are many methods for generating synthetic tabular data, the task remains non-trivial and unexplored for specific scenarios. One such scenario is survival data. Here, the key difficulty is censoring: for some instances, we are not aware of the time of event, or if one even occurred. Imbalances in censoring and time horizons cause generative models to experience three new failure modes specific to survival analysis: (1) generating too few at-risk members; (2) generating too many at-risk members; and (3) censoring too early. We formalize these failure modes and provide three new generative metrics to quantify them. Following this, we propose SurvivalGAN, a generative model that handles survival data firstly by addressing the imbalance in the censoring and event horizons, and secondly by using a dedicated mechanism for approximating time-to-event/censoring. We evaluate this method via extensive experiments on medical datasets. SurvivalGAN outperforms multiple baselines at generating survival data, and in particular addresses the failure modes as measured by the new metrics, in addition to improving downstream performance of survival models trained on the synthetic data.
3D PETCT Tumor Lesion Segmentation via GCN Refinement

Whole-body PET/CT scan is an important tool for diagnosing various malignancies (e.g., malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part for subsequent treatment. In recent years, CNN-based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as over-segmentation and under-segmentation. Therefore, to address such issues, we propose a post-processing method based on a graph convolutional neural network (GCN) to refine inaccurate segmentation parts and improve the overall segmentation accuracy. Firstly, nnUNet is used as an initial segmentation framework, and the uncertainty in the segmentation results is analyzed. Certainty and uncertainty nodes establish the nodes of a graph neural network. Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected for uncertain nodes to form edges. The highly uncertain nodes are taken as the subsequent refinement targets. Secondly, the nnUNet result of the certainty nodes is used as label to form a semi-supervised graph network problem, and the uncertainty part is optimized through training the GCN network to improve the segmentation performance. This describes our proposed nnUNet-GCN segmentation framework. We perform tumor segmentation experiments on the PET/CT dataset in the MICCIA2022 autoPET challenge. Among them, 30 cases are randomly selected for testing, and the experimental results show that the false positive rate is effectively reduced with nnUNet-GCN refinement. In quantitative analysis, there is an improvement of 2.12 % on the average Dice score, 6.34 on 95 % Hausdorff Distance (HD95), and 1.72 on average symmetric surface distance (ASSD). The quantitative and qualitative evaluation results show that GCN post-processing methods can effectively improve tumor segmentation performance.
Dicke-state preparation through global transverse control of Ising-coupled qubits

We consider the problem of engineering the two-excitation Dicke state $|D^{3}_{2}\rangle$ in a three-qubit system with all-to-all Ising-type qubit-qubit interaction, which is also subject to global transverse (Zeeman-type) control fields. The theoretical underpinning for our envisioned state-preparation scheme, in which $|000\rangle$ is adopted as the initial state of the system, is provided by a Lie-algebraic result that guarantees state-to-state controllability of this system for an arbitrary choice of initial- and final states that are invariant with respect to permutations of qubits. This scheme is envisaged in the form of a pulse sequence that involves three instantaneous control pulses, which are equivalent to global qubit rotations, and two Ising-interaction pulses of finite durations between consecutive control pulses. The design of this pulse sequence (whose total duration is $T\approx 0.95\:\hbar/J$, where $J$ is the Ising-coupling strength) leans heavily on the concept of the symmetric sector, a four-dimensional, permutationally-invariant subspace of the three-qubit Hilbert space. We demonstrate the feasibility of the proposed state-preparation scheme by carrying out a detailed numerical analysis of its robustness to systematic errors, i.e. deviations from the optimal values of the eight parameters that characterize the underlying pulse sequence. Finally, we discuss how our proposed scheme can be generalized for engineering Dicke states in systems with $N \ge 4$ qubits. For the sake of illustration, we describe the preparation of the two-excitation Dicke state $|D^{4}_{2}\rangle$ in a four-qubit system.
Can CAPE Maintain Polar Lows

Abstract A unique dataset of atmospheric observations over the Nordic Seas has been analyzed to investigate the role of convective available potential energy (CAPE) for the energetics of polar lows. The observations were made during the flight campaign of the Norwegian International Polar Year (IPY) and The Observing System Research and Predictability Experiment (THORPEX) in February and March 2008, which specifically targeted polar lows. The data reveal virtually no conditional instability and very limited CAPE. It is suggested that the significance of CAPE values should be assessed by calculating the time scale tCAPE that is necessary for the heat fluxes from the ocean to transfer the corresponding amount of energy. Even the largest CAPE values have a tCAPE of less than 1 h. These CAPE values are associated with unconditional instability. It is concluded that the observed CAPE should be seen as a temporary stage in an energy flux rather than as an energy reservoir. Based on the findings in this investig.
Uncoiled affine Temperley-Lieb algebras and their Wenzl-Jones projectors

Affine and periodic Temperley-Lieb algebras are families of diagrammatic algebras that find diverse applications in mathematics and physics. These algebras are infinite-dimensional, yet most of their interesting modules are finite. In this paper, we introduce finite quotients for these algebras, which we term uncoiled affine Temperley-Lieb algebras and uncoiled periodic Temperley-Lieb algebras. We study some of their properties, including their defining relations, their description with diagrams, their dimensions, and their relation with affine and sandwich cellular algebras. The uncoiled algebras all have finitely many one-dimensional modules. We construct a family of Wenzl-Jones idempotents, each of which projects onto one of these one-dimensional modules. Our construction is explicit and uses the similar projectors for the ordinary Temperley-Lieb algebras, as well as the diagrammatic description of the uncoiled algebras in terms of sandwich diagrams.
Spike formation theory in 3D flow separation

We develop a frame-invariant theory of material spike formation during flow separation over a no-slip boundary in three-dimensional flows with arbitrary time dependence. Based on the exact evolution of the largest principal curvature on near-wall material surfaces, our theory identifies fixed and moving separation. Our approach is effective over short time intervals and admits an instantaneous limit. As a byproduct, we derive explicit formulas for the evolution of the Weingarten map and the principal curvatures of any surface advected by general three-dimensional flows. The material backbone we identify acts first as a precursor and later as the centerpiece of Lagrangian flow separation. We discover previously undetected spiking points and curves where the separation backbones connect to the boundary and provide wall-based analytical formulas for their locations. We illustrate our results on several steady and unsteady flows.
Flow instability and momentum exchange in separation control by a synthetic jet

This study investigates a mechanism for controlling separated flows around an airfoil using a synthetic jet (SJ). A large-eddy simulation (LES) was performed for a leading-edge separation flow around an airfoil at the chord Reynolds number of 63 000 and the angle of attack of 12°. The present LES resolves a turbulent structure inside a deforming SJ cavity with a deforming grid. An optimal actuation-frequency band is identified between the normalized frequencies of F+=6.0 and 20, which suppresses the separation and drastically improves the lift-to-drag ratio. In the controlled flows, the laminar separation bubble near the leading edge periodically releases multiple spanwise-uniform vortex structures, which diffuse and merge to generate a single coherent vortex in the period of F+. Such a coherent vortex plays a significant role in exchanging a chordwise momentum between a near-wall surface and the freestream away from the wall. It also entrains smaller turbulent vortices and eventually enhances the turbulent component of the Reynolds stress throughout the suction surface. Linear stability theory (LST) was subsequently compared with the LES result, which clarifies the applicability of the LST to the controlled flows. In the optimal F+ regime, both linear and nonlinear modes are excited in a well-balanced manner, where the first mode is associated with the Kelvin–Helmholtz instability and contributes to a quick and smooth turbulent transition, while the second mode shows a frequency lower than that of the linear mode and encourages a formation of the coherent vortex structure that eventually entrains smaller turbulent vortices.
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for one single grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, <inline-formula><tex-math notation="LaTeX">$\mathsf {S4Eq}$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="sans-serif">S</mml:mi><mml:mn mathvariant="sans-serif">4</mml:mn><mml:mi mathvariant="sans-serif">Eq</mml:mi></mml:mrow></mml:math><inline-graphic xlink:href="kommrusch-ieq1-3271065.gif"/></alternatives></inline-formula>, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programs.
Balanced Off-Policy Evaluation for Personalized Pricing

We consider a personalized pricing problem in which we have data consisting of feature information, historical pricing decisions, and binary realized demand. The goal is to perform off-policy evaluation for a new personalized pricing policy that maps features to prices. Methods based on inverse propensity weighting (including doubly robust methods) for off-policy evaluation may perform poorly when the logging policy has little exploration or is deterministic, which is common in pricing applications. Building on the balanced policy evaluation framework of Kallus (2018), we propose a new approach tailored to pricing applications. The key idea is to compute an estimate that minimizes the worst-case mean squared error or maximizes a worst-case lower bound on policy performance, where in both cases the worst-case is taken with respect to a set of possible revenue functions. We establish theoretical convergence guarantees and empirically demonstrate the advantage of our approach using a real-world pricing dataset.
On the Factors Modulating the Stratocumulus to Cumulus Transitions

AbstractLarge-eddy simulation (LES) is used to explore the role of various processes in regulating the stratocumulus to cumulus transition (SCT). Simulations are based on a composite case derived from a Lagrangian analysis of 2 yr of data from the northeastern Pacific. The simulations reproduce well the observed transition from a compact stratocumulus layer to more broken fields of cumulus, simply as a response to increasing sea surface temperatures (SSTs) along the transition. In so doing they support earlier theoretical work that argued that the SCT was a response of boundary layer circulations to increased forcing by surface latent heat fluxes. Although the basic features of the SCT imposed by the increase in SST are robust, a variety of other factors affect the detailed character of the SCT. For example, enhanced precipitation or increased downwelling longwave radiative fluxes can accelerate the reduction in cloud cover that accompanies the SCT, while a gradual decrease in the large-scale divergence c.
Discussion of Causality

It is one of the main tenets of classical statistics that ‘Statistics cannot prove causality’. This cautious attitude has roots back to the origins of modern statistics, for instance to R. A. Fisher with his sceptical attitude to the connection between smoking and lung cancer. The attitude is certainly quite rational: statistics, with its measurements and counting typically moves on the surface of phenomena. Epidemiology, for instance, is based on counting occurrences in various risk groups, and it is rather obvious that one may distrust the depth of the conclusions based on such methods. However, the conventional scepticism towards proving causality from statistics is in contrast to the actual practice of statistics. For instance, medicine is one of the main fields of application of statistics today. Biostatistics, or medical statistics, is a great success. One can hardly open a medical journal without coming across statistical presentations or concepts. Indeed an editorial in the New England Journal of Medicine (Editors, 2000) ranked ‘Application of Statistics to Medicine’ as one of the 11 ‘most important medical developments of the past millennium’. Statistics was ranked together with ‘Elucidation of Human Anatomy and Physiology’ and other great developments of medicine. Although the word ‘causality’ is not mentioned, the authors certainly had it in mind when writing their paper. This is evident from the following quotation: