Miracle-dz/UniFAR-FTUs huggingface dataset:
The Value-of-Information in Matching With Queues

We consider the problem of optimal matching with queues in dynamic systems and investigate the value-of-information. In such systems, operators match tasks and resources stored in queues, with the objective of maximizing the system utility of the matching reward profile, minus the average matching cost. This problem appears in many practical systems and the main challenges are the no-underflow constraints, and the lack of matching-reward information and system dynamics statistics. We develop two online matching algorithms: Learning-aided Reward optimAl Matching (<inline-formula> <tex-math notation="LaTeX">$\mathtt {LRAM}$ </tex-math></inline-formula>) and Dual-<inline-formula> <tex-math notation="LaTeX">$\mathtt {LRAM}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\mathtt {DRAM}$ </tex-math></inline-formula>) to effectively resolve both challenges. Both algorithms are equipped with a learning module for estimating the matching-reward information, while <inline-formula> <tex-math notation="LaTeX">$\mathtt {DRAM}$ </tex-math></inline-formula> incorporates an additional module for learning the system dynamics. We show that both algorithms achieve an <inline-formula> <tex-math notation="LaTeX">$O(\epsilon +\delta _{r})$ </tex-math></inline-formula> close-to-optimal utility performance for any <inline-formula> <tex-math notation="LaTeX">$\epsilon >0$ </tex-math></inline-formula>, while <inline-formula> <tex-math notation="LaTeX">$\mathtt {DRAM}$ </tex-math></inline-formula> achieves a faster convergence speed and a better delay compared with <inline-formula> <tex-math notation="LaTeX">$\mathtt {LRAM}$ </tex-math></inline-formula>, i.e., <inline-formula> <tex-math notation="LaTeX">$O(\delta _{\pi }/\epsilon + \log (1/\epsilon )^{2})$ </tex-math></inline-formula> delay and <inline-formula> <tex-math notation="LaTeX">$O(\delta _{\pi }/\epsilon )$ </tex-math></inline-formula> convergence under <inline-formula> <tex-math notation="LaTeX">$\mathtt {DRAM}$ </tex-math></inline-formula> compared with <inline-formula> <tex-math notation="LaTeX">$O(1/\epsilon )$ </tex-math></inline-formula> delay and convergence under <inline-formula> <tex-math notation="LaTeX">$\mathtt {LRAM}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\delta _{r}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\delta _{\pi }$ </tex-math></inline-formula> are maximum estimation errors for reward and system dynamics). Our results show that the information of different system components can play very different roles in algorithm performance and provide a novel way for designing the joint learning-control algorithms.
BarQL: Collaborating Through Change

Applications such as Google Docs, Office 365, and Dropbox show a growing trend towards incorporating multi-user live collaboration functionality into web applications. These collaborative applications share a need to efficiently express shared state, and a common strategy for doing so is a shared log abstraction. Extensive research efforts on log abstractions by the database, programming languages, and distributed systems communities have identified a variety of optimization techniques based on the algebraic properties of updates (i.e., pairwise commutativity, subsumption, and idempotence). Although these techniques have been applied to specific applications and use-cases, to the best of our knowledge, no attempt has been made to create a general framework for such optimizations in the context of a non-trivial update language. In this paper, we introduce mutation languages, a low-level framework for reasoning about the algebraic properties of state updates, or mutations. We define BarQL, a general purpose state-update language, and show how mutation languages allow us to reason about the algebraic properties of updates expressed in BarQ L .
Normalized Wasserstein for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptation: Supplementary material

For NW measure to normalize mode proportions appropriately, we need a good estimate of the number of mode proportions. Theorem 1 provides conditions under which the mode proportions can provable be estimated. Let PX and PY be two mixture distributions whose NW measure we wish to compute. Let PX and PY have n1 and n2 modes respectively, with r modes overlapping. Let k∗ = n1 + n2 − r. We make the following assumptions • (A1) If mode i in distribution X and mode j in distribution Y belong to the same mixture component, then their Wasserstein distance is ≤ i.e., if Xi and Yj correspond to the same component, W (PXi ,PYj ) < . • (A2) The minimum Wasserstein distance between any two modes of one mixture distribution is at least δ i.e., W (PXi ,PXj ) > δ and W (PYi ,PYj ) > δ ∀i 6= j. Also, non-overlapping modes between X and Y are separated by δ i.e., for non-overlapping modes Xi and Yj , W (PXi ,PYj ) > δ. This ensures that modes are well-separated.
An agent-based learning towards decentralized and coordinated traffic signal control

Adaptive traffic signal control is a promising technique for alleviating traffic congestion. Reinforcement Learning (RL) has the potential to tackle the optimal traffic control problem for a single agent. However, the ultimate goal is to develop integrated traffic control for multiple intersections. Integrated traffic control can be efficiently achieved using decentralized controllers. Multi-Agent Reinforcement Learning (MARL) is an extension of RL techniques that makes it possible to decentralize multiple agents in a non-stationary environments. Most of the studies in the field of traffic signal control consider a stationary environment, an approach whose shortcomings are highlighted in this paper. A Q-Learning-based acyclic signal control system that uses a variable phasing sequence is developed. To investigate the appropriate state model for different traffic conditions, three models were developed, each with different state representation. The models were tested on a typical multiphase intersection to minimize the vehicle delay and were compared to the pre-timed control strategy as a benchmark. The Q-Learning control system consistently outperformed the widely used Webster pre-timed optimized signal control strategy under various traffic conditions.
More reliable estimates of divergence times in Pan using complete mtDNA sequences and accounting for population structure

References http://rstb.royalsocietypublishing. org/content/365/1556/3277.full. html#ref-list-1 This article cites 71 articles, 31 of which can be accessed free Rapid response http://rstb.royalsocietypublishing. org/letters/submit/royptb;365/1556/3277 Respond to this article Subject collections (2062 articles) evolution (104 articles) bioinformatics (275 articles) taxonomy and systematics Articles on similar topics can be found in the following collections
Joint covariance estimation with mutual linear structure

We consider the joint estimation of structured covariance matrices. We assume the structure is unknown and perform the estimation using heterogeneous training sets. More precisely, we are given groups of measurements coming from centered normal populations with different covariance matrices. Assuming that all these covariance matrices span a low dimensional affine subspace in the space of symmetric matrices, our aim is to determine this structure. It is then utilized to improve the covariance estimation. We provide an algorithm discovering and exploring the underlying covariance structure and analyze its error bounds. Numerical simulations are presented to illustrate the performance benefits of the proposed algorithm.
A Numerical Simulation Framework for the Design , Management and Optimization of CO 2 Sequestration in Subsurface Formations

The overall objective of the project is the development of a numerical framework for the simulation of subsurface CO 2 sequestration processes. The following important research areas were pursued simultaneously during this three-year GCEP project: (1) accurate modeling of CO 2 trapping by dissolution and residual trapping, (2) analysis and construction of consistent and high-order adaptive implicit discretization schemes, (3) development of an adaptive multiscale finite-volume formulation for nonlinear multiphase flow and transport, (4) development of a stochastic framework for constructing Darcy-scale CO2 transport equations that are consistent with the pore scale physics, and (5) extension of GPRS (General-Purpose Research Simulator) for modeling chemical reactions coupled with compositional flow and transport. Dissolution trapping is studied for a layer of super-critical CO 2 that overlies a saline saturated porous medium in a structural trap. Dissolution of the CO 2 into the underlying brine leads to a local density increase, and that results in buoyancy driven fingering. Stability analysis based on the dominant-mode of the self-similar diffusion operator resolves the critical time and wavelength associated with the onset of the instability quite accurately. The nonlinear evolution of the instability is analyzed using high-accuracy direct numerical simulation. The nonlinear solutions of the early time period show excellent agreement with the predictions from our linear-stability theory. At later times, macroscopic fingers display intense nonlinear interactions that significantly influence both the propagation speeds and the overall mixing rate. Analysis for typical aquifers shows that for a permeability variation of 1− 3000 mD, the critical time can vary from 2000 yrs to about10 days, while the critical wavelength varies from 200 m to0.3 m. The behavior of CO2 gravity currents in sloping aquifers in the presence of residual trapping is investigated using a simple mathematical model that accounts for residual trapping in the wake of the migrating gravity current. The analysis provides insight into the length and time scales associated with the competition between up-dip migration of the current and residual trapping of the CO 2 in its wake. The dependence of the max-
Mixtures of Gaussian process priors

Nonparametric Bayesian approaches based on Gaussian processes have recently become popular in the empirical learning community. They encompass many classical methods of statistics, like Radial Basis Functions or various splines, and are technically convenient because Gaussian integrals can be calculated analytically. Restricting to Gaussian processes, however, forbids for example the implemention of genuine nonconcave priors. Mixtures of Gaussian process priors, on the other hand, allow the flexible implementation of complex and situation specific, also nonconcave"a priori"information. This is essential for tasks with, compared to their complexity, a small number of available training data. The paper concentrates on the formalism for Gaussian regression problems where prior mixture models provide a generalisation of classical quadratic, typically smoothness related, regularisation approaches being more flexible without having a much larger computational complexity.
Differential Network Analysis via the Lasso Penalized D-Trace Loss

Biological networks often change under different environmental and genetic conditions. Understanding how these networks change becomes an important problem in biological studies. In this paper, we model the network change as the difference of two precision matrices and propose a novel loss function for estimating the precision matrix difference. Under a new irrepresentability condition, we show that the new loss function with the lasso penalty can give consistent estimates in high-dimensional setting for sub-Gaussian and polynomial-tailed distributions. An efficient algorithm is developed based on the alternating direction method to solve the optimization problem. Simulation studies and a real data analysis about colorectal cancer show that the proposed method outperforms other available methods.
How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies

With the introduction of machine learning in high-stakes decision making, ensuring algorithmic fairness has become an increasingly important problem to solve. In response to this, many mathematical definitions of fairness have been proposed, and a variety of optimisation techniques have been developed, all designed to maximise a defined notion of fairness. However, fair solutions are reliant on the quality of the training data, and can be highly sensitive to noise. Recent studies have shown that robustness (the ability for a model to perform well on unseen data) plays a significant role in the type of strategy that should be used when approaching a new problem and, hence, measuring the robustness of these strategies has become a fundamental problem. In this work, we therefore propose a new criterion to measure the robustness of various fairness optimisation strategies - the robustness ratio. We conduct multiple extensive experiments on five bench mark fairness data sets using three of the most popular fairness strategies with respect to four of the most popular definitions of fairness. Our experiments empirically show that fairness methods that rely on threshold optimisation are very sensitive to noise in all the evaluated data sets, despite mostly outperforming other methods. This is in contrast to the other two methods, which are less fair for low noise scenarios but fairer for high noise ones. To the best of our knowledge, we are the first to quantitatively evaluate the robustness of fairness optimisation strategies. This can potentially can serve as a guideline in choosing the most suitable fairness strategy for various data sets.
Erasure/List Random Coding Error Exponents Are Not Universally Achievable

We study the problem of universal decoding for unknown discrete memoryless channels in the presence of erasure/list option at the decoder, in the random coding regime. In particular, we harness a universal version of Forney's classical erasure/list decoder developed in earlier studies, which is based on the competitive minimax methodology, and guarantees universal achievability of a certain fraction of the optimum random coding error exponents. In this paper, we derive an exact single-letter expression for the maximum achievable fraction. Examples are given in which the maximal achievable fraction is strictly less than unity, which imply that, in general, there is no universal erasure/list decoder, which achieves the same random coding error exponents as the optimal decoder for a known channel. This is in contrast to the situation in ordinary decoding (without the erasure/list option), where optimum exponents are universally achievable, as is well known. It is also demonstrated that previous lower bounds derived for the maximal achievable fraction are not tight in general. We then analyze a generalized random coding ensemble, which incorporate a training sequence, in conjunction with a suboptimal practical decoder (“plug-in” decoder), which first estimates the channel using the available training sequence, and then decodes the remaining symbols of the codeword using the estimated channel. One of the implications of our results is setting the stage for a reasonable criterion of optimal training. Finally, we compare the performance of the “plug-in” decoder and the universal decoder, in terms of the achievable error exponents, and show that the latter is noticeably better than the former.
On conditions for linearity of optimal estimation

When is optimal estimation linear? It is well-known that, in the case of a Gaussian source contaminated with Gaussian noise, a linear estimator minimizes the mean square estimation error. This paper analyzes more generally the conditions for linearity of optimal estimators. Given a noise (or source) distribution, and a specified signal to noise ratio (SNR), we derive conditions for existence and uniqueness of a source (or noise) distribution that renders the Lp norm optimal estimator linear. We then show that, if the noise and source variances are equal, then the matching source is distributed identically to the noise. Moreover, we prove that the Gaussian source-channel pair is unique in that it is the only source-channel pair for which the MSE optimal estimator is linear at more than one SNR values.
An Adaptive Approach to Recoverable Mutual Exlcusion

Mutual exclusion (ME) is one of the most commonly used techniques to handle conflicts in concurrent systems. Traditionally, mutual exclusion algorithms have been designed under the assumption that a process does not fail while acquiring/releasing a lock or while executing its critical section. However, failures do occur in real life, potentially leaving the lock in an inconsistent state. This gives rise to the problem of \emph{recoverable mutual exclusion (RME)} that involves designing a mutual exclusion algorithm that can tolerate failures, while maintaining safety and liveness properties. One of the important measures of performance of any ME algorithm, including an RME algorithm, is the number of \emph{remote memory references (RMRs)} made by a process (for acquiring and releasing a lock as well as recovering the lock structure after a failure). The best known RME algorithm solves the problem for $n$ processes in sub-logarithmic number of RMRs, given by $\mathcal{O}(\frac{\log n}{\log \log n})$, irrespective of the number of failures in the system. In this work, we present a new algorithm for solving the RME problem whose RMR complexity gradually \emph{adapts} to the number of failures that have occurred in the system "recently". In the absence of failures, our algorithm generates only $\mathcal{O}(1)$ RMRs. Furthermore, its RMR complexity is given by $\mathcal{O}(\min\{ \sqrt{F}, \frac{\log n}{\log \log n} \})$ where $F$ is the total number of failures in the "recent" past. In addition to read and write instructions, our algorithm uses compare-and-swap (\CAS{}) and fetch-and-store (\FAS{}) hardware instructions, both of which are commonly available in most modern processors.
Minimizing the Stabbing Number of Matchings , Trees , and Triangulations ∗

The (axis-parallel) stabbing number of a given set of line se gm nts is the maximum number of segments that can be intersected by any one (axis-paralle l) line. This paper deals with finding perfect matchings, spanning trees, or triangulations of mi ni um stabbing number for a given set of vertices. The complexity of finding a spanning tree of m ini um stabbing number is one of the original 30 questions on “The Open Problems Project” l is of outstanding problems in computational geometry by Demaine, Mitchell, and O’Rourke . We showNP-hardness of stabbing problems by means of a general proof te chnique. For matchings, this also implies a non-trivial lower bound on th e approximability. On the positive side, we propose a cut-based integer programming formulation for minimizing the stabbing number of matchings and spanning trees. From the corresponding linea r programming relaxation, we obtain polynomial-time lower bounds, and show that there always is an optimal fractional solution that contains an edge of at least constant weight. We conjecture t hat the resulting iterated rounding scheme constitutes a constant-factor approximation algor ithm. ACM Classification: F.2. 2 Nonnumerical Algorithms and Problems. AMS Classification: 68Q17, 68U05, 90C27.
On sampling graphical Markov models

We consider sampling and enumeration problems for Markov equivalence classes. We create and analyze a Markov chain for uniform random sampling on the DAGs inside a Markov equivalence class. Though the worst case is exponentially slow mixing, we find a condition on the Markov equivalence class for polynomial time mixing. We also investigate the ratio of Markov equivalence classes to DAGs and a Markov chain of He, Jia, and Yu for random sampling of sparse Markov equivalence classes.
Effect of breed and dairy system on milk composition and udder health traits in multi-breed dairy herds

The aim of the present study was to investigate the effect of breed and dairy system on milk composition and udder health (UH) traits in mu lti-breed dairy herds. Individual milk samples (n=1,516) were collected from 41 multi -breed herds located in Trento province (in the North-East Italian Alps). Six bree ds were involved: Brown Swiss (n=661), Holstein Friesian (n=473), Jersey (n=45), Simmental (n=158), Grey Alpine (n=75) and Rendena (n=104). Four different farming systems were identified: “Original Traditional” (lactating cows that are moved to high land pastures during summer; n=9), “Traditional without summer pastures” (n=11) “Tradi tional with silages” (n=2), and “Modern” (n=19). Analysis of variance was performed on milk composit ion and UH traits using the MIXED procedure of SAS. Orthogonal contrasts were estimated between least squares means of traits for the breed and the dairy system effects. Relevant differences have been highlighted between the six b reeds regarding the milk composition traits, while breed differences for UH traits were negligible. The dairy system management revealed a limited influence on all cons idered traits.
Comparison between Standard and Adaptive MCMC via their diffusion limits

Adaptive Markov Chain Monte Carlo (AMCMC) is a class of MCMC algorithms where the proposal distribution changes at every iteration of the chain. In this case it is important to verify that such an MC indeed has a stationary distribution. In this paper we discuss a diffusion approximation to a discrete time AMCMC and that of Standard MCMC (SMCMC). This diffusion approximation is different when compared to the diffusion approximation as in Gelman, Gilks and Roberts (1997) where the state space increases in dimension to $\infty$. In our approach the time parameter is sped up in such a way that the limiting distribution is to a non-trivial continuous time diffusion process. We provide the tables and plots related to that of SMCMC and AMCMC for comparison.
Guessing what ' s on your mind : Using the N 400 in Brain Computer Interfaces

A method is explored for using a simple neurophysiological brain responses to determine a deeper underlying brain state. The response chosen for the research is the N400 potential, a linguistical dectection method for priming. The goal is to construct a BCI that can determine what the user is `thinking about', where `thinking about' is de ned as being primed on. Existing N400 research primes the subject by showing an explicit priming stimulus, such as a word or picture, before showing a probe stimulus. The amplitude of the N400 potential, which is an ERP component occurring roughly 400ms after the presentation of the probe, is modulated by the semantic/associative relatedness of the prime and probe stimuli. Four experiments were performed which explore the possibility for achieving the N400 e ect without showing priming stimuli. Two experiments repeat earlier work, which uses conventional means of eliciting the N400 e ect. One experiment uses trials consisting of 7 words sentences where the last word was either semantically congruent with the rst 6 words or not. The other experiments used word pairs, of which the rst word acted as prime and the latter as a probe. The other two experiments are variations of the previous experiments. Instead of each trial consisting of a prime and a probe, one of two physical objects was given to the subject before the experiment, which the subject was instructed to think about. During the experiment itself, no more priming stimuli are presented, but multiple probe words are shown corresponding to both possible objects. The results indicate that a subject can prime himself on a physical object by actively thinking about it during the experiment, as opposed to being shown explicit priming stimuli. This opens the door to the possibility of BCI that can be controlled by simply thinking about a word. The nature of the N400 allows support for a very large vocabulary, as the probing scheme can perform a binary search.