Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantizing for Noisy Channels

We consider the transmission of numerical data over a noisy channel. Two sources of error exist. The first is the quantizer where the input data is mapped into a finite set of rational numbers and the second is the channel which includes the encoder, transmitter, transmission medium, receiver, and decoder. For any given probability density on the input data and any given channel matrix, we determine the quantization values and transition levels which minimize the total mean-square error. We also determine the best quantizer structure under the constraint that quantization values and transition levels be equally spaced. For the special case of a noiseless channel both results reduce to those of Max [2]. As an example we consider the case of Gaussian input data and phase-shift keyed (PSK) transmission in additive white Gaussian noise. The transmitter is both peak and average power limited, and the system operates in real time. Both the natural and Gray codes are considered. The mean-square error, quantizer entropy, channel capacity, and information rate are computed for the system using the optimum uniform quantizer. Finally, we show that it is important to take the channel into consideration when designing the quantizer even when the system is not constrained to operate in real time.
Fast rates for nonparametric online learning: from realizability to learning in games

We study fast rates of convergence in the setting of nonparametric online regression, namely where regret is defined with respect to an arbitrary function class which has bounded complexity. Our contributions are two-fold: - In the realizable setting of nonparametric online regression with the absolute loss, we propose a randomized proper learning algorithm which gets a near-optimal cumulative loss in terms of the sequential fat-shattering dimension of the hypothesis class. In the setting of online classification with a class of Littlestone dimension d, our bound reduces to d · poly logT. This result answers a question as to whether proper learners could achieve near-optimal cumulative loss; previously, even for online classification, the best known cumulative loss was Õ( √dT). Further, for the real-valued (regression) setting, a cumulative loss bound with near-optimal scaling on sequential fat-shattering dimension was not even known for improper learners, prior to this work. - Using the above result, we exhibit an independent learning algorithm for general-sum binary games of Littlestone dimension d, for which each player achieves regret Õ(d3/4 · T1/4). This result generalizes analogous results of Syrgkanis et al. (2015) who showed that in finite games the optimal regret can be accelerated from O(√T) in the adversarial setting to O(T1/4) in the game setting. To establish the above results, we introduce several new techniques, including: a hierarchical aggregation rule to achieve the optimal cumulative loss for real-valued classes, a multi-scale extension of the proper online realizable learner of Hanneke et al. (2021), an approach to show that the output of such nonparametric learning algorithms is stable, and a proof that the minimax theorem holds in all online learnable games.
Hyperbolic Coxeter groups of minimal growth rates in higher dimensions

Abstract The cusped hyperbolic n-orbifolds of minimal volume are well known for $n\leq 9$ . Their fundamental groups are related to the Coxeter n-simplex groups $\Gamma _{n}$ . In this work, we prove that $\Gamma _{n}$ has minimal growth rate among all non-cocompact Coxeter groups of finite covolume in $\textrm{Isom}\mathbb H^{n}$ . In this way, we extend previous results of Floyd for $n=2$ and of Kellerhals for $n=3$ , respectively. Our proof is a generalization of the methods developed together with Kellerhals for the cocompact case.
SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing via Semantic Consistency

Semantically guided conditional Generative Adversarial Networks (cGANs) have become a popular approach for face editing in recent years. However, most existing methods introduce semantic masks as direct conditional inputs to the generator and often require the target masks to perform the corresponding translation in the RGB space. We propose SeCGAN, a novel label-guided cGAN for editing face images utilising semantic information without the need to specify target semantic masks. During training, SeCGAN has two branches of generators and discriminators operating in parallel, with one trained to translate RGB images and the other for semantic masks. To bridge the two branches in a mutually beneficial manner, we introduce a semantic consistency loss which constrains both branches to have consistent semantic outputs. Whilst both branches are required during training, the RGB branch is our primary network and the semantic branch is not needed for inference. Our results on CelebA and CelebA-HQ demonstrate that our approach is able to generate facial images with more accurate attributes, outperforming competitive baselines in terms of Target Attribute Recognition Rate whilst maintaining quality metrics such as self-supervised Fr\'{e}chet Inception Distance and Inception Score.
Effect of clinical anesthesia preoperative evaluation on the length of preoperative period and total hospitalization of patients undergoing laparoscopic cholecystectomy.

OBJECTIVE Anesthesia preoperative evaluation clinics (APECs) are currently operating in several South Korean hospitals. While several studies have investigated the impact of APEC operations on the length of total hospital stay (LTHS), few have investigated their impact on the length of preoperative hospital stay (LPHS) for patients. In this study, we aimed to determine whether APEC affected the LPHS and LTHS. METHODS Data of all patients who underwent surgery at Chungbuk National University Hospital between September 2009 and August 2019 were analyzed retrospectively. All patients who had undergone laparoscopic cholecystectomy over the last 10 years were categorized into two groups: those who visited the APEC (Group A), and those who did not (Group B). The age, sex, American Society of Anesthesiologists physical status score, LPHS, and LTHS of the two groups were compared. RESULTS The LPHS was 1.03±0.2 days in Group A and 1.61±1.6 days in Group B. The LTHS was 4.77±1.9 days in Group A and 5.63±2.6 days in Group B. The LPHS and LTHS of the two groups differed by 0.58 and 0.9 days, respectively. CONCLUSION We evaluated the effect of APEC operations on the LPHS and LTHS of inpatients undergoing laparoscopic cholecystectomy and observed a decrease in both the LPHS and LTHS. Understanding and accepting the importance of APEC is significant for physicians and administrators working to improve hospital efficiency and patient outcomes. Further research is needed to investigate the need and benefits of APECs.
Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification

Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity. State-of-the-art generative language models have been shown to provide significant gains across different NLP tasks. However, their applicability to data augmentation for text classification tasks in few-shot settings have not been fully explored, especially for specialised domains. In this paper, we leverage GPT-2 (Radford et al, 2019) for generating artificial training instances in order to improve classification performance. Our aim is to analyse the impact the selection process of seed training examples has over the quality of GPT-generated samples and consequently the classifier performance. We propose a human-in-the-loop approach for selecting seed samples. Further, we compare the approach to other seed selection strategies that exploit the characteristics of specialised domains such as human-created class hierarchical structure and the presence of noun phrases. Our results show that fine-tuning GPT-2 in a handful of label instances leads to consistent classification improvements and outperform competitive baselines. The seed selection strategies developed in this work lead to significant improvements over random seed selection for specialised domains. We show that guiding text generation through domain expert selection can lead to further improvements, which opens up interesting research avenues for combining generative models and active learning.
DYNAMIC STABILITY CONTROL OF FRONT WHEEL DRIVE WHEELCHAIRS USING SOLID STATE ACCELEROMETERS AND GYROSCOPES

While the active dynamic stability of automobiles has increased over the past 17 years there have been very few similar advances made with electrically powered wheelchairs. This lack of improvement has led to a long standing acceptance of less-than-optimal stability and control of these wheelchairs. Accidents due to loss of stability are well documented. Hence, the healthcare industry has made several efforts for improved control of electric powered wheelchairs (EPWs) to provide enhanced comfort, safety and manoeuvrability at a lower cost. In response, an area of stability control was identified that could benefit from a feedback control system using solid state sensors. To design an effective closed–loop feedback controller with optimal performance to overcome instabilities, an accurate model of wheelchair dynamics needed to be created. Such a model can be employed to test various controllers quickly and repeatedly, without the difficulties of physically setting a wheelchair up for each test. This task was one central goal of this research. A wireless test-bed of a front wheel drive (FWD) wheelchair was also developed to validate a dynamic wheelchair model. It integrates sensors, a data control system, an embedded controller, and the motorised mechanical system. The wireless communication ensures the integrity of sensor data collected and control signals sent. The test-bed developed not only facilitates the development of feedback controllers of motorised wheelchairs, but the
DeltaConv: Anisotropic Point Cloud Learning with Exterior Calculus

Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D data. In this paper, we aim to construct anisotropic convolutions that work directly on the surface derived from a point cloud. This is challenging because of the lack of a global coordinate system for tangential directions on surfaces. We introduce a new convolution operator called DeltaConv, which combines geometric operators from exterior calculus to enable the construction of anisotropic ﬁlters on point clouds. Because these operators are deﬁned on scalar-and vector-ﬁelds, we separate the network into a scalar-and a vector-stream, which are connected by the operators. The vector stream enables the network to explicitly represent, evaluate, and process directional information. Our convolutions are robust and simple to implement and show improved accuracy compared to state-of-the-art approaches on several benchmarks, while also speeding up training and inference.
Understanding and Testing Generalization of Deep Networks on Out-of-Distribution Data

Deep network models perform excellently on In-Distribution (ID) data, but can significantly fail on Out-Of-Distribution (OOD) data. While developing methods focus on improving OOD generalization, few attention has been paid to evaluating the capability of models to handle OOD data. This study is devoted to analyzing the problem of experimental ID test and designing OOD test paradigm to accurately evaluate the practical performance. Our analysis is based on an introduced categorization of three types of distribution shifts to generate OOD data. Main observations include: (1) ID test fails in neither reflecting the actual performance of a single model nor comparing between different models under OOD data. (2) The ID test failure can be ascribed to the learned marginal and conditional spurious correlations resulted from the corresponding distribution shifts. Based on this, we propose novel OOD test paradigms to evaluate the generalization capacity of models to unseen data, and discuss how to use OOD test results to find bugs of models to guide model debugging.
Do Not Trust Prediction Scores for Membership Inference Attacks

Membership inference attacks (MIAs) aim to determine whether a speciﬁc sample was used to train a predictive model. Knowing this may indeed lead to a privacy breach. Arguably, most MIAs, however, make use of the model’s prediction scores—the probability of each output given some input—following the intuition that the trained model tends to behave differently on its training data. We argue that this is a fallacy for many modern deep network architectures, e.g., ReLU type neural networks produce almost always high prediction scores far away from the training data. Consequently, MIAs will miserably fail since this behavior leads to high false-positive rates not only on known domains but also on out-of-distribution data and implicitly acts as a defense against MIAs. Speciﬁcally, using generative adversarial networks, we are able to produce a potentially inﬁnite number of samples falsely classiﬁed as part of the training data. In other words, the threat of MIAs is overestimated and less information is leaked than previously assumed. Moreover, there is actually a trade-off be-tween the overconﬁdence of classiﬁers and their susceptibility to MIAs: the more classiﬁers know when they do not know, making low conﬁdence predictions far away from the training data, the more they reveal the training data.
Detection of a 100,000 M ⊙ black hole in M31's Most Massive Globular Cluster: A Tidally Stripped Nucleus

We investigate the presence of a central black hole (BH) in B023-G078, M31's most massive globular cluster. We present high-resolution, adaptive-optics assisted, integral-field spectroscopic kinematics from Gemini/NIFS that show a strong rotation (∼20 km s−1) and a velocity dispersion rise toward the center (37 km s−1). We combine the kinematic data with a mass model based on a two-component fit to HST ACS/HRC data of the cluster to estimate the mass of a putative BH. Our dynamical modeling suggests a >3σ detection of a BH component of 9.1−2.8+2.6×104M⊙ (1σ uncertainties). The inferred stellar mass of the cluster is 6.22−0.05+0.03×106M⊙ , consistent with previous estimates, thus the BH makes up 1.5% of its mass. We examine whether the observed kinematics are caused by a collection of stellar mass BHs by modeling an extended dark mass as a Plummer profile. The upper limit on the size scale of the extended mass is 0.56 pc (95% confidence), which does not rule out an extended mass. There is compelling evidence that B023-G078 is the tidally stripped nucleus of a galaxy with a stellar mass >109 M ⊙, including its high-mass, two-component luminosity profile, color, metallicity gradient, and spread in metallicity. Given the emerging evidence that the central BH occupation fraction of >109 M ⊙ galaxies is high, the most plausible interpretation of the kinematic data is that B023-G078 hosts a central BH. This makes it the strongest BH detection in a lower-mass (<107 M ⊙) stripped nucleus, and one of the few dynamically detected intermediate-mass BHs.
MASS: Mobile Autonomous Station Simulation

We propose a set of tools to replay wireless network traffic traces, while preserving the privacy of the original traces. Traces are generated by a user- and context-aware trained generative adversarial network (GAN). The replay allows for realistic traces from any number of users and of any trace duration to be produced given contextual parameters like the type of application and the real-time signal strength. We demonstrate the usefulness of the tools in three replay scenarios: Linux- and Android-station experiments and NS3 simulations. We also evaluate the ability of the GAN model to generate traces that retain key statistical properties of the original traces such as feature correlation, statistical moments, and novelty. Our results show that we beat both traditional statistical distribution fitting approaches as well as a state-of-the-art GAN time series generator across these metrics. The ability of our GAN model to generate any number of user traces regardless of the number of users in the original trace also makes our tools more practically applicable compared to previous GAN approaches. Furthermore, we present a use case where our tools were employed in a Wi-Fi research experiment.
Vertical structure of Sb-intercalated quasifreestanding graphene on SiC(0001)

Using the normal incidence x-ray standing wave technique as well as low energy electron microscopy we have investigated the structure of quasi-freestanding monolayer graphene (QFMLG) obtained by intercalation of antimony under the (cid:0) 6 √ 3 × 6 √ 3 (cid:1) R 30 ° reconstructed graphitized 6H-SiC(0001) surface, also known as zeroth-layer graphene. We found that Sb intercalation decouples the QFMLG well from the substrate. The distance from the QFMLG to the Sb layer almost equals the expected van der Waals bonding distance of C and Sb. The Sb intercalation layer itself is mono-atomic, ﬂat, and located much closer to the substrate, at almost the distance of a covalent Sb-Si bond length. All data is consistent with Sb located on top of the uppermost Si atoms of the SiC bulk.
An Experimental Study of Latency for IEEE 802.11be Multi-link Operation

Will Multi-Link Operation (MLO) be able to improve the latency of Wi-Fi networks? MLO is one of the most disruptive MAC-layer techniques included in the IEEE 802.11be amendment. It allows a device to use multiple radios simultaneously and in a coordinated way, providing a new framework to improve the WLAN throughput and latency. In this paper, we investigate the potential latency benefits of MLO by using a large dataset containing 5 GHz spectrum occupancy measurements. Experimental results show that when the channels are symmetrically occupied, MLO can improve latency by one order of magnitude. In contrast, in asymmetrically occupied channels, MLO can sometimes be detrimental and increase latency. This is a result of packets being assigned to an interface before carrying out the backoff, which is more likely to be interrupted on the busier link. We overcome this issue by allowing multiple backoffs to run in parallel, assigning the packet to the particular interface where the backoff expires first, which also achieves lower latency overall.
Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach

Online advertising revenues account for an increasing share of publishers’ revenue streams, especially for small and medium-sized publishers who depend on the advertisement networks of tech companies such as Google and Facebook. Thus publishers may benefit significantly from accurate online advertising revenue forecasts to better manage their website monetization strategies. However, publishers who only have access to their own revenue data lack a holistic view of the total ad market of publishers, which in turn limits their ability to generate insights into their own future online advertising revenues. To address this business issue, we leverage a proprietary database encompassing Google Adsense revenues from a large collection of publishers in diverse areas. We adopt the Temporal Fusion Transformer (TFT) model, a novel attention-based architecture to predict publishers’ advertising revenues. We leverage multiple covariates, including not only the publisher’s own characteristics but also other publishers’ advertising revenues. Our prediction results outperform several benchmark deep-learning time-series forecast models over multiple time horizons. Moreover, we interpret the results by analyzing variable importance weights to identify significant features and self-attention weights to reveal persistent temporal patterns.
Merging Discrete Morse Vector Fields: A Case of Stubborn Geometric Parallelization

We address the basic question in discrete Morse theory of combining discrete gradient fields that are partially defined on subsets of the given complex. This is a well-posed question when the discrete gradient field V is generated using a fixed algorithm which has a local nature. One example is ProcessLowerStars, a widely used algorithm for computing persistent homology associated to a grey-scale image in 2D or 3D. While the algorithm for V may be inherently local, being computed within stars of vertices and so embarrassingly parallelizable, in practical use, it is natural to want to distribute the computation over patches Pi, apply the chosen algorithm to compute the fields Vi associated to each patch, and then assemble the ambient field V from these. Simply merging the fields from the patches, even when that makes sense, gives a wrong answer. We develop both very general merging procedures and leaner versions designed for specific, easy-to-arrange covering patterns.
Properties of Fast and Slow Bars Classified by Epicyclic Frequency Curves from Photometry of Barred Galaxies

We test the idea that bar pattern speeds decrease with time owing to angular momentum exchange with a dark matter halo. If this process actually occurs, then the radii of the corotation resonance and other resonances should generally increase with time. We therefore derive the angular velocity Ω and epicyclic frequency κ as functions of galactocentric radius for 85 barred galaxies using photometric data. Mass maps are constructed by assuming a dynamical mass-to-light ratio and then solving the Poisson equation for the gravitational potential. The locations of Lindblad resonances and the corotation resonance radius are then derived using the standard precession frequency curves in conjunction with bar pattern speeds recently estimated from the Tremaine–Weinberg method as applied to integral field spectroscopy data. Correlations between physical properties of bars and their host galaxies indicate that bar length and the corotation radius depend on the disk circular velocity while bar strength and pattern speed do not. As the bar pattern speed decreases, bar strength, length, and corotation radius increase, but when bars are subclassified into fast, medium, and slow domains, no significant change in bar length is found. Only a hint of an increase in bar strength from fast to slow bars is found. These results suggest that bar length in a galaxy undergoes little evolution, and is determined instead mainly by the size of the host galaxy.