Miracle-dz/UniFAR-FTUs huggingface dataset:
Modiﬁed Backoff Algorithms for DFWMAC‘s 1 Distributed Coordination Function

The distributed coordination function (DCF) in the upcoming standard for wireless LANs IEEE 802.11 is based on a CSMA/CA like access protocol. The collision avoidance part applies a rotating backoff window mechanism, that causes unequal collision probability distribution depending on the position of the selected slot within the contention window. We present simple modiﬁcations for the backoff mechanism using weighted probability distributions and a load adaptive scheme for the slots that result in a lower collision probability and therefore in higher throughput of up to 20%
Dermoscopic findings in a case of reactive perforating collagenosis

Reactive perforating collagenosis (RPC) is the most common type of acquired perforating dermatoses (APD), [1] and is characterized by umbilicated papules and plaques with central crusted ulceration. Pruritus is the most common symptom of RPC, and the lesions are most commonly found on the extensor surfaces of the extremities. This entity is histopathologically characterized by invagination of the epidermis and transepidermal elimination of collagen bundles. The pathogenesis of RPC is still somewhat unclear, but it has been reported that trauma resulting from scratching may induce damage to the epidermis or dermal collagen [1,2]. Moreover, RPC is commonly found in association with chronic renal failure and diabetes mellitus. Dermoscopy is a noninvasive technique that has been used in the diagnosis of APD [3], especially for perforating folliculitis (PF). However, to our knowledge, the dermoscopic features of RPC have not been previously described in the literature. Here, we report the dermoscopic features and their histological correlations in RPC as a means to improve the diagnosis of this condition.
Credit Scoring for Micro-Loans

Credit Scores are ubiquitous and instrumental for loan providers and regulators. In this paper we showcase how micro-loan credit system can be developed in real setting. We show what challenges arise and discuss solutions. Particularly, we are concerned about model interpretability and data quality. In the final section, we introduce semi-supervised algorithm that aids model development and evaluate its performance
Quantifying teaching behavior in robot learning from demonstration

Learning from demonstration allows for rapid deployment of robot manipulators to a great many tasks, by relying on a person showing the robot what to do rather than programming it. While this approach provides many opportunities, measuring, evaluating, and improving the person’s teaching ability has remained largely unexplored in robot manipulation research. To this end, a model for learning from demonstration is presented here that incorporates the teacher’s understanding of, and influence on, the learner. The proposed model is used to clarify the teacher’s objectives during learning from demonstration, providing new views on how teaching failures and efficiency can be defined. The benefit of this approach is shown in two experiments ( n = 30 and n = 36 , respectively), which highlight the difficulty teachers have in providing effective demonstrations, and show how ~ 169 –180% improvement in teaching efficiency can be achieved through evaluation and feedback shaped by the proposed framework, relative to unguided teaching.
Competing coherent and dissipative dynamics close to quantum criticality

We investigate the competition of coherent and dissipative dynamics in many-body systems at continuous quantum transitions. We consider dissipative mechanisms that can be effectively described by Lindblad equations for the density matrix of the system. The interplay between the critical coherent dynamics and dissipation is addressed within a dynamic finite-size scaling theory, which allows us to identify the regime where they develop a nontrivial competition. We put forward general scaling behaviors involving the Hamiltonian parameters and the coupling associated with the dissipation. This scaling scenario is supported by a numerical study of the dynamic behavior of a one-dimensional lattice fermion gas undergoing a quantum Ising transition, in the presence of dissipative mechanisms such as local pumping, decaying and dephasing.
Low-Rank Similarity Metric Learning in High Dimensions

Metric learning has become a widespreadly used tool in machine learning. To reduce expensive costs brought in by increasing dimensionality, low-rank metric learning arises as it can be more economical in storage and computation. However, existing low-rank metric learning algorithms usually adopt nonconvex objectives, and are hence sensitive to the choice of a heuristic low-rank basis. In this paper, we propose a novel low-rank metric learning algorithm to yield bilinear similarity functions. This algorithm scales linearly with input dimensionality in both space and time, therefore applicable to high-dimensional data domains. A convex objective free of heuristics is formulated by leveraging trace norm regularization to promote low-rankness. Crucially, we prove that all globally optimal metric solutions must retain a certain low-rank structure, which enables our algorithm to decompose the high-dimensional learning task into two steps: an SVD-based projection and a metric learning problem with reduced dimensionality. The latter step can be tackled efficiently through employing a linearized Alternating Direction Method of Multipliers. The efficacy of the proposed algorithm is demonstrated through experiments performed on four benchmark datasets with tens of thousands of dimensions.
Optimal rates for F-score binary classification

We study the minimax settings of binary classification with F-score under the $\beta$-smoothness assumptions on the regression function $\eta(x) = \mathbb{P}(Y = 1|X = x)$ for $x \in \mathbb{R}^d$. We propose a classification procedure which under the $\alpha$-margin assumption achieves the rate $O(n^{−(1+\alpha)\beta/(2\beta+d)})$ for the excess F-score. In this context, the Bayes optimal classifier for the F-score can be obtained by thresholding the aforementioned regression function $\eta$ on some level $\theta^*$ to be estimated. The proposed procedure is performed in a semi-supervised manner, that is, for the estimation of the regression function we use a labeled dataset of size $n \in \mathbb{N}$ and for the estimation of the optimal threshold $\theta^*$ we use an unlabeled dataset of size $N \in \mathbb{N}$. Interestingly, the value of $N \in \mathbb{N}$ does not affect the rate of convergence, which indicates that it is "harder" to estimate the regression function $\eta$ than the optimal threshold $\theta^*$. This further implies that the binary classification with F-score behaves similarly to the standard settings of binary classification. Finally, we show that the rates achieved by the proposed procedure are optimal in the minimax sense up to a constant factor.
Joint Segmentation and Path Classification of Curvilinear Structures

Detection of curvilinear structures in images has long been of interest. One of the most challenging aspects of this problem is inferring the graph representation of the curvilinear network. Most existing delineation approaches first perform binary segmentation of the image and then refine it using either a set of hand-designed heuristics or a separate classifier that assigns likelihood to paths extracted from the pixel-wise prediction. In our work, we bridge the gap between segmentation and path classification by training a deep network that performs those two tasks simultaneously. We show that this approach is beneficial because it enforces consistency across the whole processing pipeline. We apply our approach on roads and neurons datasets.
Bound-state band reconstruction and resonance in a spin-1/2 Bose gas with one-dimensional spin-orbit coupling

In this work, we study two-body bound states in two-component Bose gas with a one-dimensional (1D) spin-orbit coupling (SOC) induced by Raman lasers. The finite Raman coupling strength generates coupling among three spin channels, resulting in the reconstruction of three bound-state bands. In addition, multiple resonances can be induced at finite scattering lengths. By tuning the interaction in one intra-species channel, one bound-state band can be lifted and three resonances can be achieved at different center-of-mass momenta, which can be observable under current experimental conditions in ${}^{87}$Rb atoms.
Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach

This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting control task, which incurs huge communication energy for exchanging UAV states in real time. We tackle this problem by exploiting a mean-field game (MFG) theoretic control method that requires the UAV state exchanges only once at the initial source. Afterwards, each UAV can control its acceleration by locally solving two partial differential equations (PDEs), known as the Hamilton-Jacobi- Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. This approach, however, brings about huge computation energy for solving the PDEs, particularly under multi-dimensional UAV states. We address this issue by utilizing a machine learning (ML) method where two separate ML models approximate the solutions of the HJB and FPK equations. These ML models are trained and exploited using an online gradient descent method with low computational complexity. Numerical evaluations validate that the proposed ML aided MFG theoretic algorithm, referred to as \emph{MFG learning control}, is effective in collision avoidance with low communication energy and acceptable computation energy.
Interactive Speech Understanding

This paper introduces a robust interactive method for speech understanding. The generalized LR parsing is enhanced in this approach. Parsing proceeds from left to right correcting minor errors. When a very noisy portion is detected, the parser skips that portion using a fake nonterminal symbol. The unidentified portion is resolved by re-utterance of that portion which is parsed very efficiently by using the parse record of the first utterance. The user does not have to speak the whole sentence again. This method is also capable of handling unknown words, which is important in practical systems. Detected unknown words can be incrementally incorporated into the dictionary after the interaction with the user. A pilot system has shown great effectiveness of this approach.
Risk Preferences are Not Time Preferences: Comment

Andreoni and Sprenger (in press) report evidence that distinct utility functions govern choices under certainty and risk. I investigate the robustness of their result to the experimental design. I find that the effect disappears completely when a multiple price list is used instead of a convex time budget design. Also, the effect is reduced by half when sooner and later payment risks are realized using a single lottery instead of two independent lotteries. The result is thus partially driven by intertemporal diversification, suggesting an explanation in terms of concavity of the intertemporal, and not only the atemporal, utility function.
5th Workshop of the ERCIM Working Group on Matrix Computations and Statistics Numerical Methods for Statistics

The stochastic ﬁnite element method (SFEM) is a discretization technique for stochastic partial diﬀerential equations which treats the stochastic components of the PDE in a way that is similar to the standard Galerkin ﬁnite element method for deterministic problems. It does, however, substantially increase the number of degrees of freedom present in the discretization. As a result, the solution of the discretised problem poses even more of a compuational challenge than for the corresponding deterministic PDE. We show that for a popular SFEM formulation the resulting discretization matrices can be brought to block diagonal form by employing ﬁ-nite element element bases constructed from double-orthogonal polynomials and present some numerical examples which demonstrate the resulting computational savings.
Permanent Revocation in Attribute Based Broadcast Encryption

We propose a new and efficient scheme for broadcast encryption. A broadcast encryption system allows a broadcaster to send an encrypted message to a dynamically chosen subset RS, |RS|=n, of a given set of users, such that only users in this subset can decrypt the message. An important component of broadcast encryption schemes is revocation of users by the broadcaster, thereby updating the subset RS. Revocation may be either temporary, for a specific cipher text, or permanent. In the existing public key schemes which support temporary revocation of the users, the broadcaster is required to keep track of the revoked users. We present the first public key broadcast encryption scheme that supports permanent revocation of users. Unlike previous schemes, the broadcaster in our scheme should not keep track of the revoked users (saving memory and computation power). Our scheme is fully collusion-resistant. In other words, even if all the revoked users collude, the revoked user cannot encrypt messages without receiving new keys from the broadcaster. The procedure is based on Cipher-text Policy Attribute-Based Encryption (CP-ABE). The overhead of revocation in our system is constant in all major performance measures including length of private and public keys, computational complexity, user's storage space, and computational complexity of encryption and decryption. The scheme we construct improves on our original scheme in a poster presentation [7] by a factor of O(log n) in all major performance measures.
From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environment

Scenario-based development and test processes are a promising approach for verifying and validating automated driving functions. For this purpose, scenarios have to be generated during the development process in a traceable manner. In early development stages, the operating scenarios of the item to be developed are usually described in an abstract, linguistic way. Within the scope of a simulation-assisted test process, these linguistically described scenarios have to be transformed into a state space representation and converted into data formats which can be used with the respective simulation environment. Currently, this step of detailing scenarios takes a considerable manual effort. Furthermore, a standardized interpretation of the linguistically described scenarios and a consistent transformation into the data formats are not guaranteed due to multiple authors as well as many constraints between the scenario parameters. In this paper, the authors present an approach to automatically detail a keyword-based scenario description for execution in a simulation environment and provide a basis for test case generation. As a first step, the keyword-based description is transformed into a parameter space representation. At the same time, constraints regarding the selection and combination of parameter values are documented for the following process steps (e. g. evolutionary or stochastic test methods). As a second step, the parameter space representation is converted into data formats required by the simulation environment. As an example, the authors use scenarios on German freeways and convert them into the data formats OpenDRIVE (description of the road) and OpenSCENARIO (description of traffic participants and environmental conditions) for execution in the simulation environment Virtual Test Drive.
Common Commercial and Consumer Products Contain Activators of the Aryl Hydrocarbon (Dioxin) Receptor

Activation of the Ah receptor (AhR) by halogenated aromatic hydrocarbons (HAHs), such as 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD, dioxin), can produce a wide variety of toxic and biological effects. While recent studies have shown that the AhR can bind and be activated by structurally diverse chemicals, how widespread of these AhR agonists are in environmental, biological and synthetic materials remains to be determined. Using AhR-based assays, we demonstrate the presence of potent AhR agonists in a variety of common commercial and consumer items. Solvent extracts of paper, rubber and plastic products contain chemicals that can bind to and stimulate AhR DNA binding and/or AhR-dependent gene expression in hepatic cytosol, cultured cell lines, human epidermis and zebrafish embryos. In contrast to TCDD and other persistent dioxin-like HAHs, activation of AhR-dependent gene expression by these extracts was transient, suggesting that the agonists are metabolically labile. Solvent extracts of rubber products produce AhR-dependent developmental toxicity in zebrafish in vivo, and inhibition of expression of the metabolic enzyme CYP1A, significantly increased their toxic potency. Although the identity of the responsible AhR-active chemicals and their toxicological impact remain to be determined, our data demonstrate that AhR active chemicals are widely distributed in everyday products.
Blocking up D-branes : Matrix renormalization ?

Drawing analogies with block spin techniques used to study continuum limits in critical phenomena, we attempt to block up D-branes by averaging over near neighbour elements of their (in general noncommuting) matrix coordinates, i.e.\ in a low energy description. We show that various D-brane (noncommutative) geometries arising in string theory appear to behave sensibly under blocking up, given certain key assumptions in particular involving gauge invariance. In particular, the (gauge-fixed) noncommutative plane, fuzzy sphere and torus exhibit a self-similar structure under blocking up, if some ``counterterm'' matrices are added to the resulting block-algebras. Applying these techniques to matrix representations of more general D-brane configurations, we find that blocking up averages over far-off-diagonal matrix elements and brings them in towards the diagonal, so that the matrices become ``less off-diagonal'' under this process. We describe heuristic scaling relations for the matrix elements under this process. Further, we show that blocking up does not appear to exhibit any ``chaotic'' behaviour, suggesting that there is sensible physics underlying such a matrix coarse-graining. We also discuss briefly interrelations of these ideas with B-fields and noncommutativity.
An Open Source and Open Hardware Deep Learning-Powered Visual Navigation Engine for Autonomous Nano-UAVs

Nano-size unmanned aerial vehicles (UAVs), with few centimeters of diameter and sub-10 Watts of total power budget, have so far been considered incapable of running sophisticated visual-based autonomous navigation software without external aid from base-stations, ad-hoc local positioning infrastructure, and powerful external computation servers. In this work, we present what is, to the best of our knowledge, the first 27g nano-UAV system able to run aboard an end-to-end, closed-loop visual pipeline for autonomous navigation based on a state-of-the-art deep-learning algorithm, built upon the open-source CrazyFlie 2.0 nano-quadrotor. Our visual navigation engine is enabled by the combination of an ultra-low power computing device (the GAP8 system-on-chip) with a novel methodology for the deployment of deep convolutional neural networks (CNNs). We enable onboard real-time execution of a state-of-the-art deep CNN at up to 18Hz. Field experiments demonstrate that the system's high responsiveness prevents collisions with unexpected dynamic obstacles up to a flight speed of 1.5m/s. In addition, we also demonstrate the capability of our visual navigation engine of fully autonomous indoor navigation on a 113m previously unseen path. To share our key findings with the embedded and robotics communities and foster further developments in autonomous nano-UAVs, we publicly release all our code, datasets, and trained networks.
Signaling equilibria in mean-field games

In this paper, we consider both finite and infinite horizon discounted dynamic mean-field games where there is a large population of homogeneous players sequentially making strategic decisions and each player is affected by other players through an aggregate population state. Each player has a private type that only she observes. Such games have been studied in the literature under simplifying assumption that population state dynamics are stationary. In this paper, we consider non-stationary population state dynamics and present a novel backward recursive algorithm to compute Markov perfect equilibrium (MPE) that depend on both, a player's private type, and current (dynamic) population state. Using this algorithm, we study a security problem in cyberphysical system where infected nodes put negative externality on the system, and each node makes a decision to get vaccinated. We numerically compute MPE of the game.
Scheduling Arrivals to Queues: A Single-Server Model with No-Shows

Queueing systems with scheduled arrivals, i.e., appointment systems, are typical for frontal service systems, e.g., health clinics. An aspect of customer behavior that influences the overall efficiency of such systems is the phenomenon of no-shows. The consequences of no-shows cannot be underestimated; e.g., British surveys reveal that in the United Kingdom alone more than 12 million general practitioner (GP) appointments are missed every year, costing the British health service an estimated ₤250 million annually. In this study we answer the following key questions: How should the schedule be computed when there are no-shows? Is it sufficiently accurate to use a schedule designed for the same expected number of customers without no-shows? How important is it to invest in efforts that reduce no-shows---i.e., given that we apply a schedule that takes no-shows into consideration, is the existence of no-shows still costly to the server and customers?
Robust high dimensional learning for Lipschitz and convex losses

We establish risk bounds for Regularized Empirical Risk Minimizers (RERM) when the loss is Lipschitz and convex and the regularization function is a norm. We obtain these results in the i.i. d. setup under subgaussian assumptions on the design. In a second part, a more general framework where the design might have heavier tails and data may be corrupted by outliers both in the design and the response variables is considered. In this situation, RERM performs poorly in general. We analyse an alternative procedure based on median-of-means principles and called "minmax MOM". We show optimal subgaussian deviation rates for these estimators in the relaxed setting. The main results are meta-theorems allowing a wide-range of applications to various problems in learning theory. To show a non-exhaustive sample of these potential applications, it is applied to classification problems with logistic loss functions regularized by LASSO and SLOPE, to regression problems with Huber loss regularized by Group LASSO, Total Variation and Fused LASSO and to matrix completion problems with quantile loss regularized by the nuclear norm. A short simulation study concludes the paper, illustrating in particular robustness properties of regularized minmax MOM procedures.