Miracle-dz/UniFAR-FTUs huggingface dataset:
Drainage of Groundwater Resting on a Sloping Bed

The Dupuit-Forchheimer convention for horizontal impermeable beds is that the gradient of hydraulic potential is synonymous with the absolute slope of the water table. When the impermeable bed is sloping, however, the gradient is normally better approximated by dZ/dl, where l is the distance measured along the bed and Z is the height of the intersection with the water table of the perpendicular through l. Thus the solution of the resulting flow equation for drainage to a transverse ditch in the absence of surface recharge provides a family of branched curves for different slopes, instead of the single branched curve currently accepted. When there is surface recharge and a system of parallel equidistant drains, there is no simple analytic solution. However, numerical solutions show that the water table peak, as compared with a horizontal bed, is shifted downhill; its height is often not greatly affected.
Unsupervised representational learning with recognition-parametrised probabilistic models

We introduce a new approach to probabilistic unsupervised learning based on the recognition-parametrised model (RPM): a normalised semi-parametric hypothesis class for joint distributions over observed and latent variables. Under the key assumption that observations are conditionally independent given the latents, RPMs directly encode the “recognition” process, parametrising both the prior distribution on the latents and their conditional distributions given observations. This recognition model is paired with non-parametric descriptions of the marginal distribution of each observed variable. Thus, the focus is on learning a good latent representation that captures dependence between the measurements. The RPM permits exact maximum-likelihood learning in settings with discrete latents and a tractable prior, even when the mapping between continuous observations and the latents is expressed through a ﬂexible model such as a neural network. We develop effective approximations for the case of continuous latent variables with tractable priors. Unlike the approximations necessary in dual-parametrised models such as Helmholtz machines and variational autoencoders, these RPM approximations introduce only minor bias, which may often vanish asymptotically. Furthermore, where the prior on latents is intractable the RPM may be combined effectively with standard probabilistic techniques such as variational Bayes. We demonstrate the model in high dimensional data settings, including a form of weakly supervised learning on MNIST digits and the discovery of latent maps from sensory observations. The RPM provides an effective way to discover, represent and reason probabilistically about the latent structure underlying observational data, functions which are critical to both animal and artiﬁcial intelligence
Measuring Tree Properties and Responses Using Low-Cost Accelerometers

Trees play a crucial role in the water, carbon and nitrogen cycle on local, regional and global scales. Understanding the exchange of momentum, heat, water, and CO2 between trees and the atmosphere is important to assess the impact of drought, deforestation and climate change. Unfortunately, ground measurements of tree properties such as mass and canopy interception of precipitation are often expensive or difficult due to challenging environments. This paper aims to demonstrate the concept of using robust and affordable accelerometers to measure tree properties and responses. Tree sway is dependent on mass, canopy structure, drag coefficient, and wind forcing. By measuring tree acceleration, we can relate the tree motion to external forcing (e.g., wind, precipitation and related canopy interception) and tree physical properties (e.g., mass, elasticity). Using five months of acceleration data of 19 trees in the Brazilian Amazon, we show that the frequency spectrum of tree sway is related to mass, canopy interception of precipitation, and canopy–atmosphere turbulent exchange.
Stable Transmission in the Time-Varying MIMO Broadcast Channel

Both linear and nonlinear transmit precoding strategies based on accurate channel state information (CSI) can significantly increase available throughput in a multiuser wireless system. With propagation delay, infrequent channel updates, lag due to network layer overhead, and time-varying node position or environment characteristics, channel knowledge becomes outdated and CSI-based transmission schemes can experience severe performance degradation. This paper studies the performance of precoding techniques for the multiuser broadcast channel with outdated CSI at the transmitter. Traditional channel models as well as channel realizations measured by a wideband channel sounder are used in the analysis. With measured data from an outdoor urban environment, it is further shown the existence of stable subspaces upon which transmission is possible without any instantaneous CSI at the transmitter. Such transmissions allow for consistent performance curves at the cost of initial suboptimality compared to CSI-based schemes.
Stochastic network utility maximisation - a tribute to Kelly's paper published in this journal a decade ago

SUMMARY Since the seminal work by Kelly on distributed network resource allocation using the language of network utility maximisation (NUM) a decade ago, there have been extensive research efforts generalising and applying NUM to model, analyse and design various network protocols and architectures. Some of these works combine the distributed optimisation approach with stochastic network models to study NUM under network dynamics occurring at the session, packet and constraint levels. We survey these works by presenting the key questions, results and methodologies in this emerging theory of stochastic network utility maximisation, followed by discussion on related work and future research challenges. Copyright © 2008 John Wiley & Sons, Ltd.
BLiMP: A Benchmark of Linguistic Minimal Pairs for English

We introduce The Benchmark of Linguistic Minimal Pairs (shortened to BLiMP, or ), a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars, and aggregate human agreement with the labels is 96.4%. We use it to evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs. We find that state-of-the-art models identify morphological contrasts reliably, but they struggle with semantic restrictions on the distribution of quantifiers and negative polarity items and subtle syntactic phenomena such as extraction islands.
Measuring IS success of e-government: A case study of the disability sector in Australia

Despite unprecedented investments in e-government annually, the degree to which these initiatives are successful in delivering government outcomes varies widely. It is posited that traditional means of evaluating these systems overlook important measures needed to support the ultimate success of e-government. To address this gap, this study will: (1) identify the critical features of e-government that influence success; (2) investigate how public value is created through the use of e-government; and (3) propose and test a public value based success model to explain the creation of value within the disability sector. This study extends the DeLone and McLean IS Success Model (2003) using a theoretically based framework grounded from Public Value Theory to provide a perspective for evaluating e-government success. The contribution of the model is to assist governments in making complex information system portfolio investment decisions.
Nested Primal-dual Gradient Algorithms for Distributed Constraint-coupled Optimization

—We study a class of distributed optimization prob- lems with a globally coupled equality constraint. A novel nested primal-dual gradient algorithm (NPGA) is proposed from the dual perspective, which can achieve linear convergence under a quite weak condition. Furthermore, the upper bounds of the step- sizes and the converge rate are explicitly given. It is worth noting that NPGA is not only an algorithm but also an algorithmic framework. By choosing different parameter matrices, we can obtain many different versions of NPGA, which offers us a chance to design more efﬁcient algorithms. Finally, the convergence rates of NPGA and existing algorithms are compared in numerical experiments.
Training ReLU networks to high uniform accuracy is intractable

Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a generalization error, that is, an expected value of a given loss function. However, for several applications — for example in a security-critical context or for problems in the computational sciences — accuracy in this sense is not sufﬁcient. In such cases, one would like to have guarantees for high accuracy on every input value, that is, with respect to the uniform norm. In this paper we precisely quantify the number of training samples needed for any conceivable training algorithm to guarantee a given uniform accuracy on any learning problem formulated over target classes containing (or consisting of) ReLU neural networks of a prescribed architecture. We prove that, under very general assumptions, the minimal number of training samples for this task scales exponentially both in the depth and the input dimension of the network architecture. As a corollary we conclude that the training of ReLU neural networks to high uniform accuracy is intractable. In a security-critical context this points to the fact that deep learning based systems are prone to being fooled by a possible adversary. We corroborate our theoretical ﬁndings by numerical results.
Unbounded superoptimization

Our aim is to enable software to take full advantage of the capabilities of emerging microprocessor designs without modifying the compiler. Towards this end, we propose a new approach to code generation and optimization. Our approach uses an SMT solver in a novel way to generate efficient code for modern architectures and guarantee that the generated code correctly implements the source code. The distinguishing characteristic of our approach is that the size of the constraints does not depend on the candidate sequence of instructions. To study the feasibility of our approach, we implemented a preliminary prototype, which takes as input LLVM IR code and uses Z3 SMT solver to generate ARMv7-A assembly. The prototype handles arbitrary loop-free code (not only basic blocks) as input and output. We applied it to small but tricky examples used as standard benchmarks for other superoptimization and synthesis tools. We are encouraged to see that Z3 successfully solved complex constraints that arise from our approach. This work paves the way to employing recent advances in SMT solvers and has a potential to advance SMT solvers further by providing a new category of challenging benchmarks that come from an industrial application domain.
Online Planning for Large Markov Decision Processes with Hierarchical Decomposition

Markov decision processes (MDPs) provide a rich framework for planning under uncertainty. However, exactly solving a large MDP is usually intractable due to the “curse of dimensionality”— the state space grows exponentially with the number of state variables. Online algorithms tackle this problem by avoiding computing a policy for the entire state space. On the other hand, since online algorithm has to find a near-optimal action online in almost real time, the computation time is often very limited. In the context of reinforcement learning, MAXQ is a value function decomposition method that exploits the underlying structure of the original MDP and decomposes it into a combination of smaller subproblems arranged over a task hierarchy. In this article, we present MAXQ-OP—a novel online planning algorithm for large MDPs that utilizes MAXQ hierarchical decomposition in online settings. Compared to traditional online planning algorithms, MAXQ-OP is able to reach much more deeper states in the search tree with relatively less computation time by exploiting MAXQ hierarchical decomposition online. We empirically evaluate our algorithm in the standard Taxi domain—a common benchmark for MDPs—to show the effectiveness of our approach. We have also conducted a long-term case study in a highly complex simulated soccer domain and developed a team named WrightEagle that has won five world champions and five runners-up in the recent 10 years of RoboCup Soccer Simulation 2D annual competitions. The results in the RoboCup domain confirm the scalability of MAXQ-OP to very large domains.
Bayesian federated estimation of causal effects from observational data

We propose a Bayesian framework for estimating causal effects from federated observational data sources. Bayesian causal inference is an important approach to learning the distribution of the causal estimands and understanding the uncertainty of causal effects. Our framework estimates the posterior distributions of the causal effects to compute the higher-order statistics that capture the uncertainty. We integrate local causal effects from different data sources without centralizing them. We then estimate the treatment effects from observational data using a non-parametric reformulation of the classical potential outcomes framework. We model the potential outcomes as a random function distributed by Gaussian processes, with deﬁning parameters that can be efﬁciently learned from multiple data sources. Our method avoids exchanging raw data among the sources, thus contributing towards privacy-preserving causal learning. The promise of our approach is demonstrated through a set of simulated and real-world examples.
Mapping sounds onto images using binaural spectrograms

We propose a novel method for mapping sound spectrograms onto images and thus enabling alignment between auditory and visual features for subsequent multimodal processing. We suggest a supervised learning approach to this audio-visual fusion problem, on the following grounds. Firstly, we use a Gaussian mixture of locally-linear regressions to learn a mapping from image locations to binaural spectrograms. Secondly, we derive a closed-form expression for the conditional posterior probability of an image location, given both an observed spectrogram, emitted from an unknown source direction, and the mapping parameters that were previously learnt. Prominently, the proposed method is able to deal with completely different spectrograms for training and for alignment. While fixed-length wide-spectrum sounds are used for learning, thus fully and robustly estimating the regression, variable-length sparse-spectrum sounds, e.g., speech, are used for alignment. The proposed method successfully extracts the image location of speech utterances in realistic reverberant-room scenarios.
Demysifying Deep Neural Networks Through Interpretation: A Survey

Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn. The problem is that the single metric is an incomplete description of the real world tasks. The single metric cannot explain why the algorithm learn. When an erroneous happens, the lack of interpretability causes a hardness of understanding and fixing the error. Recently, there are works done to tackle the problem of interpretability to provide insights into neural networks behavior and thought process. The works are important to identify potential bias and to ensure algorithm fairness as well as expected performance.
Incremental Learning of Concept Drift from Streaming Imbalanced Data

Learning in nonstationary environments, also known as learning concept drift, is concerned with learning from data whose statistical characteristics change over time. Concept drift is further complicated if the data set is class imbalanced. While these two issues have been independently addressed, their joint treatment has been mostly underexplored. We describe two ensemble-based approaches for learning concept drift from imbalanced data. Our first approach is a logical combination of our previously introduced Learn++. NSE algorithm for concept drift, with the well-established SMOTE for learning from imbalanced data. Our second approach makes two major modifications to Learn++. NSE-SMOTE integration by replacing SMOTE with a subensemble that makes strategic use of minority class data; and replacing Learn++. NSE and its class-independent error weighting mechanism with a penalty constraint that forces the algorithm to balance accuracy on all classes. The primary novelty of this approach is in determining the voting weights for combining ensemble members, based on each classifier's time and imbalance-adjusted accuracy on current and past environments. Favorable results in comparison to other approaches indicate that both approaches are able to address this challenging problem, each with its own specific areas of strength. We also release all experimental data as a resource and benchmark for future research.
Shielded storage precipitation‐gages

The United States Weather Bureau has for many years rendered an important public service in measuring the precipitation throughout the country and publishing the data for general use. During recent years, because of the rapid expansion in the field of applied hydrology, the Bureau has been called upon to improve its method of measuring snowfall, and to expand its services into new fields. J. Cecil Alter, Meteorologist in charge of the Weather Bureau office at Salt Lake City, Utah, began experimenting with storage-gages and shielded gages in 1909. His work has resulted in the present design of the shielded storage precipitation-gage which made its debut in the snowfields of the Western States in 1937. The principal reason which prompted Mr. Alter to continue his work with the shielded . storage-gages was that several key precipitation-stations in Utah were attended from May to October, producing fine records, but because of isolation in winter the stations would be abandoned from November to April. As these were the months of heaviest precipitation, a storage-type of gage was required to hold the catch until it could be measured. At regular cooperative stations, especially in windy locations, the measurement of snowfall and its water-equivalent by ordinary means has been subject to criticism, and especially where snow-depth alone became the basis of the estimate.
Decision Forests Induce Characteristic Kernels

Decision forests are popular tools for classification and regression. These forests naturally produce proximity matrices measuring how often each pair of observations lies in the same leaf node. Recently it has been demonstrated that these proximity matrices can be thought of as kernels, connecting the decision forest literature to the extensive kernel machine literature. While other kernels are known to have strong theoretical properties, such as being characteristic kernels, no similar result is available for any decision forest based kernel. We show that a decision forest induced proximity can be made into a characteristic kernel, which can be used within an independence test to obtain a universally consistent test. We therefore empirically evaluate this kernel on a suite of 12 high-dimensional independence test settings: the decision forest induced kernel is shown to typically achieve substantially higher power than other methods.
Next-generation applications in healthcare digital libraries using semantic service composition and coordination

Healthcare digital libraries (DLs) increasingly make use of dedicated services to access functionality and/or data. Semantic (web) services enhance single services and facilitate compound services, thereby supporting advanced applications on top of a DL. The traditional process management approach tends to focus on process definition at build time rather than on actual service events in run time, and to anticipate failures in order to define appropriate strategies. This paper presents a novel approach where service coordination is distributed among a set of agents. A dedicated component plans compound semantic services on demand for a particular application. In failure, the planner is reinvoked to define contin- gency strategies. Finally, matchmaking is effected at runtime by choosing the appropriate service provider. These combined technologies will provide key support for highly flexible next-generation DL applications. Such technologies are under development within CASCOM.
Towards Adversarially Robust Deepfake Detection: An Ensemble Approach

Detecting deepfakes remains an open problem. Current detection methods fail against an adversary who adds imperceptible adversarial perturbations to the deep-fake to evade detection. We propose D isjoint D eepfake D etection ( D3 ), a deep-fake detector designed to improve adversarial robustness beyond de facto solutions such as adversarial training. D3 uses an ensemble of models over disjoint subsets of the frequency spectrum to signiﬁcantly improve robustness. Our key insight is to leverage a redundancy in the frequency domain and apply a saliency partitioning technique to disjointly distribute frequency components across multiple models. We formally prove that these disjoint ensembles lead to a reduction in the dimensionality of the input subspace where adversarial deepfakes lie. We then empirically validate the D3 method against white-box attacks and black-box attacks and ﬁnd that D3 signiﬁcantly outperforms existing state-of-the-art defenses applied to deepfake detection.
Localized LRR on Grassmann Manifold: An Extrinsic View

Subspace data representation has recently become a common practice in many computer vision tasks. Low-rank representation (LRR) is one of the most successful models for clustering vectorial data according to their subspace structures. This paper explores the possibility of extending LRR for subspace data on Grassmann manifold. Rather than directly embedding the Grassmann manifold into the symmetric matrix space, an extrinsic view is taken to build the self-representation in the local area of the tangent space at each Grassmannian point, resulting in a localized LRR method on Grassmann manifold. A novel algorithm for solving the proposed model is investigated and implemented. The performance of the new clustering algorithm is assessed through experiments on several real-world data sets including MNIST handwritten digits, ballet video clips, SKIG action clips, and DynTex++ data set and highway traffic video clips. The experimental results show that the new method outperforms a number of state-of-the-art clustering methods.
The Surprising Effectiveness of MAPPO in Cooperative, Multi-Agent Games

Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is signiﬁcantly less utilized than off-policy learning algorithms in multi-agent problems. In this work, we investigate Multi-Agent PPO (MAPPO), a multi-agent PPO variant which adopts a centralized value function. Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks, and the Hanabi Challenge, with minimal hyperparam-eter tuning and without any domain-speciﬁc algorithmic modiﬁcations or architectures. In the majority of environments, we ﬁnd that compared to off-policy baselines, MAPPO achieves better or comparable sample complexity as well as substantially faster running time. Finally, we present 5 factors most inﬂuential to MAPPO’s practical performance with ablation studies.