Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-terminal communication channels

The problem of transmitting information in a specified direction over a communication channel with three terminals is considered. Examples are given of the various ways of sending information. Basic inequalities for average mutual information rates are obtained. A coding theorem and weak converse are proved and a necessary and sufficient condition for a positive capacity is derived. Upper and lower bounds on the capacity are obtained, which coincide for channels with symmetric structure.
Better Generalized Few-Shot Learning Even Without Base Data

This paper introduces and studies zero-base generalized few-shot learning (zero-base GFSL), which is an extreme yet practical version of few-shot learning problem. Motivated by the cases where base data is not available due to privacy or ethical issues, the goal of zero-base GFSL is to newly incorporate the knowledge of few samples of novel classes into a pretrained model without any samples of base classes. According to our analysis, we discover the fact that both mean and variance of the weight distribution of novel classes are not properly established, compared to those of base classes. The existing GFSL methods attempt to make the weight norms balanced, which we find help only the variance part, but discard the importance of mean of weights particularly for novel classes, leading to the limited performance in the GFSL problem even with base data. In this paper, we overcome this limitation by proposing a simple yet effective normalization method that can effectively control both mean and variance of the weight distribution of novel classes without using any base samples and thereby achieve a satisfactory performance on both novel and base classes. Our experimental results somewhat surprisingly show that the proposed zero-base GFSL method that does not utilize any base samples even outperforms the existing GFSL methods that make the best use of base data. Our implementation is available at: https://github.com/bigdata-inha/Zero-Base-GFSL.
Zero-Shot Opinion Summarization with GPT-3

Very large language models such as GPT-3 have shown impressive performance across a wide variety of tasks, including text summarization. In this paper, we show that this strong performance extends to opinion summarization. We explore several pipeline methods for applying GPT-3 to summarize a large collec-tion of user reviews in a zero-shot fashion, no-tably approaches based on recursive summarization and selecting salient content to summarize through supervised clustering or extraction. On two datasets, an aspect-oriented summarization dataset of hotel reviews and a generic summarization dataset of Amazon and Yelp reviews, we show that the GPT-3 models achieve very strong performance in human evaluation. We argue that standard evaluation metrics do not reﬂect this, and evaluate against several new measures targeting faithfulness, factuality, and genericity to contrast these different methods.
Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index

In this work, a machine learning approach is developed for predicting the outcomes of football matches. The novelty of this research lies in the utilisation of the Kelly Index to first classify matches into categories where each one denotes the different levels of predictive difficulty. Classification models using a wide suite of algorithms were developed for each category of matches in order to determine the efficacy of the approach. In conjunction to this, a set of previously unexplored features were engineering including Elo-based variables. The dataset originated from the Premier League match data covering the 2019-2021 seasons. The findings indicate that the process of decomposing the predictive problem into sub-tasks was effective and produced competitive results with prior works, while the ensemble-based methods were the most effective. The paper also devised an investment strategy in order to evaluate its effectiveness by benchmarking against bookmaker odds. An approach was developed that minimises risk by combining the Kelly Index with the predefined confidence thresholds of the predictive models. The experiments found that the proposed strategy can return a profit when following a conservative approach that focuses primarily on easy-to-predict matches where the predictive models display a high confidence level.
The Vanishing Decision Boundary Complexity and the Strong First Component

We show that unlike machine learning classiﬁers, there are no complex boundary structures in the decision boundaries for well-trained deep models. However, we found that the complicated structures do appear in training but they vanish shortly after shaping. This is a pessimistic news if one seeks to capture different levels of complexity in the decision boundary for understanding generalization, which works well in machine learning. Nonetheless, we found that the decision boundaries of predecessor models on the training data are reﬂective of the ﬁnal model’s generalization. We show how to use the predecessor decision boundaries for studying the generalization of deep models. We have three major ﬁndings. One is on the strength of the ﬁrst principle component of deep models, another about the singularity of optimizers, and the other on the effects of the skip connections in ResNets. Code is at https://github.com/ hengshu1/decision_boundary_github .
Image denoising by block-matching and 1D filtering

In this paper, we develop a new image denoising method based on block-matching and transform-domain filtering. The developed method is derived from the current state-of-the-art denoising method (BM3D). We separate the 3D transform in the original method to two steps 1D transform, to further enhance the sparsity for signals whose elements are highly similar and to weaken the sparsity for those signals whose elements are dissimilar. Because the 1D filtering is on highly similar elements and the 2D filtering on image blocks are all removed, the image details can be better reserved and fewer artifacts are introduced than original method. Experimental results demonstrate that the developed method is competitive and better than some of the current state-of-the-art denoising methods in terms of peak signal-to-noise ratio, structural similarity, and subjective visual quality.
Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges

For conceptual design, engineers rely on conventional iterative (often manual) techniques. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive. Pure optimisation methods, however, ignore qualitative aspects (e.g. aesthetics or construction methods). This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE), which serves as forward performance predictor for given design features as well as an inverse design feature predictor conditioned on a set of performance requests. The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland. Sensitivity analysis is employed for explainability and informing designers about (i) relations of the model between features and/or performances and (ii) structural improvements under user-defined objectives. A case study proved our framework's potential to serve as a future co-pilot for conceptual design studies of pedestrian bridges and beyond.
Textual Enhanced Contrastive Learning for Solving Math Word Problems

Solving math word problems is the task that analyses the relation of quantities and requires an accurate understanding of contextual natural language information. Recent studies show that current models rely on shallow heuristics to predict solutions and could be easily misled by small textual perturbations. To address this problem, we propose a Textual Enhanced Contrastive Learning framework, which enforces the models to distinguish semantically similar examples while holding different mathematical logic. We adopt a self-supervised manner strategy to enrich examples with subtle textual variance by textual reordering or problem re-construction. We then retrieve the hardest to differentiate samples from both equation and textual perspectives and guide the model to learn their representations. Experimental results show that our method achieves state-of-the-art on both widely used benchmark datasets and also exquisitely designed challenge datasets in English and Chinese. \footnote{Our code and data is available at \url{https://github.com/yiyunya/Textual_CL_MWP}
Spin contribution to the inverse Faraday effect of nonmagnetic metals

We formulate the spin contribution to the inverse Faraday effect of non-magnetic metals. We deal with the role of the inversion symmetry, which forces all electronic bands to be at least twice degenerate at every point in the Brillouin zone. We show both analytically and numerically that our formulation of the inverse Faraday effect is invariant under unitary rotation within the doubly degenerate set of bands. In addition, we show the importance of resonance-like features in the band structure for the inverse Faraday effect. Our first-principles computed spin component of the inverse Faraday effect in a simple metal such as Au is reminiscent of its optical absorption, with a characteristic d-s resonance in the optical spectrum.
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library

Recently, Vehicle-to-Everything (V2X) cooperative perception has attracted increasing attention. Infrastructure sensors play a critical role in this research field; however, how to find the optimal placement of infrastructure sensors is rarely studied. In this paper, we investigate the problem of infrastructure sensor placement and propose a pipeline that can efficiently and effectively find optimal installation positions for infrastructure sensors in a realistic simulated environment. To better simulate and evaluate LiDAR place-ment, we establish a Realistic LiDAR Simulation library that can simulate the unique characteristics of different popular LiDARs and produce high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating point cloud data in different LiDAR placements, we can evaluate the perception accuracy of these placements using multiple detection models. Then, we analyze the correlation between the point cloud distribution and perception accuracy by calculating the density and uniformity of regions of interest. Experiments show that when using the same number and type of LiDAR, the placement scheme optimized by our proposed method improves the average precision by 15%, compared with the conventional placement scheme in the standard lane scene. We also analyze the correlation between perception performance in the region of interest and LiDAR point cloud distribution and validate that density and uniformity can be indicators of performance. Both the RLS Library and related code will be released at https://github.com/PJLab-ADG/LiDARSimLib-and-Placement-Evaluation.
Theta divisors and permutohedra

We establish a surprising relation of the smooth theta-divisor $\Theta^n$ with permutohedron $\Pi^n$ and the corresponding toric variety $X_\Pi^n. $ In particular, we show that the generalised Todd genus of the theta divisor $\Theta^n$ coincides with $h$-polynomial of permutohedron $\Pi^n$ and thus is different from the same genus of $X_\Pi^n$ only by the sign $(-1)^n. $ As an application we find all the Hodge numbers of the theta divisors in terms of the Eulerian numbers. We reveal also interesting numerical relations between theta-divisors and Tomei manifolds from the theory of the integrable Toda lattice.
Bayesian Semiparametric Model for Sequential Treatment Decisions with Informative Timing

We develop a Bayesian semiparametric model for the impact of dynamic treatment rules on survival among patients diagnosed with pediatric acute myeloid leukemia (AML). The data consist of a subset of patients enrolled in a phase III clinical trial in which patients move through a sequence of four treatment courses. At each course, they undergo treatment that may or may not include anthracyclines (ACT). While ACT is known to be effective at treating AML, it is also cardiotoxic and can lead to early death for some patients. Our task is to estimate the potential survival probability under hypothetical dynamic ACT treatment strategies, but there are several impediments. First, since ACT is not randomized, its effect on survival is confounded over time. Second, subjects initiate the next course depending on when they recover from the previous course, making timing potentially informative of subsequent treatment and survival. Third, patients may die or drop out before ever completing the full treatment sequence. We develop a generative Bayesian semiparametric model based on Gamma Process priors to address these complexities. At each treatment course, the model captures subjects' transition to subsequent treatment or death in continuous time. G-computation is used to compute a posterior over potential survival probability that is adjusted for time-varying confounding. Using our approach, we estimate the efficacy of hypothetical treatment rules that dynamically modify ACT based on evolving cardiac function.
Tridendriform algebras on hypergraph polytopes

We extend the works of Loday-Ronco and Burgunder-Ronco on the tridendriform decomposition of the shuffle product on the faces of associahedra and permutohedra, to other families of hypergraph polytopes (or nestohedra), including simplices, hypercubes and some new families. We also extend the shuffle product to take more than two arguments, and define accordingly a new algebraic structure, that we call polydendriform, from which the original tridendriform equations can be crisply synthesized.
High performance MMICs with submillimeter wave InP-based HEMTs

This paper presents some recently developed MMICs based on a 0.1-/spl mu/m gate-length InAlAs/InGaAs/InP HEMT process with an f/sub max/ above 600 GHz. InP-based HEMTs provide more power gain and lower noise at higher frequencies than any other transistor, including GaAs-based pHEMTs. A number of state-of-the-art InP HEMT MMICs will be presented. This includes a 150-205 GHz amplifier with 15 dB of gain, a broadband 60-140 GHz amplifier with 25 mW output power at 140 GHz, a high gain Ka-band LNA and static frequency-divider circuits operating at clock rates above 45 GHz. The high frequency performance of a next-generation 0.08-/spl mu/m-gate InAlAsSb/InAlAs/InGaAs/InP HEMT technology will also be presented.
Predicting Wind Power with Reforecasts

AbstractEnergy traders and decision-makers need accurate wind power forecasts. For this purpose, numerical weather predictions (NWPs) are often statistically postprocessed to correct systematic errors. This requires a dataset of past forecasts and observations that is often limited by frequent NWP model enhancements that change the statistical model properties. Reforecasts that recompute past forecasts with a recent model provide considerably longer datasets but usually have weaker setups than operational models. This study tests the reforecasts from the National Oceanic and Atmospheric Administration (NOAA) and the European Centre for Medium-Range Weather Forecasts (ECMWF) for wind power predictions. The NOAA reforecast clearly performs worse than the ECMWF reforecast, the operational ECMWF deterministic and ensemble forecasts, and a limited-area model of the Austrian weather service [Zentralanstalt fur Meteorologie und Geodynamik (ZAMG)]. On the contrary, the ECMWF reforecast has, of all tested models, .
Modelling and forecasting mortality distributions in England and Wales using the Lee–Carter model

Abstract Lee and Carter proposed in 1992 a non-linear model m xt = exp (a x + b x k t + ϵ xt ) for fitting and forecasting age-specific mortality rates at age x and time t. For the model parameter estimation, they employed the singular value decomposition method to find a least squares solution. However, the singular value decomposition algorithm does not provide the standard errors of estimated parameters, making it impossible to assess the accuracy of model parameters. This article describes the Lee–Carter model and the technical procedures to fit and extrapolate this model. To estimate the precision of the parameter estimates of the Lee–Carter model, we propose a binomial framework, whose parameter point estimates can be obtained by the maximum likelihood approach and interval estimates by a bootstrap approach. This model is used to fit mortality data in England and Wales from 1951 to 1990 and to forecast mortality change from 1991 to 2020. The Lee–Carter model fits these mortality data very well with R 2 being 0.9980. The estimated overall age pattern of mortality ax is very robust whereas there is considerable uncertainty in bx (changes in the age pattern over time) and kt (overall change in mortality). The fitted log age-specific mortality rates have been declining linearly from 1951 to 1990 at different paces and the projected rates will continue to decline in such a way in the 30 years prediction period.
Quantum Mechanics as a Theory of Relativistic Brownian Motion

It is shown that non-relativistic quantum mechanics can be treated as a kind of relativistic statistical theory which describes a random motion of a classical particle. The theory is relativistic in the sense that for the description of the particle behaviour the relativistic notion of the state is used. This is very important because a statistics is a state calculus and the result depends on the definition of the notion of state.
Convergence of the New Iterative Method

A new iterative method introduced by Daftardar-Gejji and Jafari (2006) (DJ Method) is an efficient technique to solve nonlinear functional equations. In the present paper, sufficiency conditions for convergence of DJM have been presented. Further equivalence of DJM and Adomian decomposition method is established.
When did you stop speaking to yourself? Age-related differences in adolescents’ world knowledge-based audience design

The ability to adapt utterances to the world knowledge of one’s addressee is undeniably ubiquitous in human social cognition, but its development and association with other cognitive mechanisms during adolescence have not been studied. In an online production task, we measured the ability of children entering adolescence (ages 11–12, M = 11.8, N=29, 17 girls) and adolescents (ages 15–16, M = 15.9, N=29, 17 girls) to tailor referential expressions in accordance with the inferred world knowledge of their addressee—an ability we refer to as world knowledge-based audience design (AD). A post-test survey showed that both age groups held similar assumptions about the addressees’ knowledge of referents, but the younger age group did not consistently adapt their utterances in accordance with these assumptions during online production, resulting in a significantly improved AD behaviour across age groups. We also investigated the reliance of AD on executive functions (EF). Executive functioning (as reflected by performance on the Wisconsin card sorting task) increased significantly with age, but did not explain the age-related increase in AD performance. We thus provide evidence in support of an adolescent development of world knowledge-based AD over and above development of EF.
Dynamic model of respiratory infectious disease transmission by population mobility based on city network

In modern societies, newly emerging infectious diseases spread rapidly between regions owing to frequent contact between people, causing considerable social and economic impacts. In this study, first, a scale-free city network was established, and then the shortest path between any two nodes was determined. Second, the movement path of tourists was designed based on the shortest path. Subsequently, every infected person's information, such as the city, infection time, onset and hospitalization, was confirmed based on their movement path. Third, the features of the transmission path and time distribution of the epidemic were characterized after summarizing the information. Finally, the reliability of the model was verified. The number of citizens and tourists in every city remained stable during this time. The results indicated that a larger basic reproduction number (R0) and population outflow rate signify a faster growth rate of infected people in each city in the network. Compared with small and medium-sized cities, the epidemic spread faster in central cities. Population mobility was the decisive factor causing the spread of the epidemic to other areas. Therefore, the rapid spread of epidemics can be prevented by swiftly reducing the flow of people between cities.
Young Lads and Old Tars: Changing Age Structure of the Nordic Sailors, 1750s–1930s

Abstract This article analyzes the changing age structure of Swedish and Finnish sailors for almost 200 years. We show that the proportion of the youngest men increased during the age of sail (i.e., the older technology). The average age increased significantly during the early twentieth century as steam (i.e., the newer technology) replaced sail in Nordic shipping. Thus, a technological revolution did not displace the older workers, but rather diminished the demand for the younger ones. This study shows, however, that technological changes were not the only drivers of changes in the age structure of Nordic sailors. Institutional and societal changes also played an important role, though they were at least partly coevolving with the technological changes. This study also shows that the maritime industry experienced professionalization especially during the latter part of the period.
Reconstructing Hand-Held Objects from Monocular Video

This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach. The dataset and code will be released at https://dihuangdh.github. io/hhor.
Systematic Evaluation of Automotive Intrusion Detection Datasets

Some current and next generation security solutions employ machine learning and related technologies. Due to the nature of these applications, correct use of machine learning can be critical. One area that is of particular interest in this regard is the use of appropriate data for training and evaluation. In this work, we investigate different characteristics of datasets for security applications and propose a number of qualitative and quantitative metrics which can be evaluated with limited domain knowledge. We illustrate the need for such metrics by analyzing a number of datasets for anomaly and intrusion detection in automotive systems, covering both internal vehicle network and vehicle-to-vehicle (V2V) communication. We demonstrate how the proposed metrics can be used to learn the strengths and weaknesses in these datasets.