Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Atomic Transaction Commit

Modern data stores achieve scalability by partitioning data into shards and fault-tolerance by replicating each shard across several servers. A key component of such systems is a Transaction Certification Service (TCS), which atomically commits a transaction spanning multiple shards. Existing TCS protocols require 2f+1 crash-stop replicas per shard to tolerate f failures. In this paper we present atomic commit protocols that require only f+1 replicas and reconfigure the system upon failures using an external reconfiguration service. We furthermore rigorously prove that these protocols correctly implement a recently proposed TCS specification. We present protocols in two different models---the standard asynchronous message-passing model and a model with Remote Direct Memory Access (RDMA), which allows a machine to access the memory of another machine over the network without involving the latter's CPU. Our protocols are inspired by a recent FARM system for RDMA-based transaction processing. Our work codifies the core ideas of FARM as distributed TCS protocols, rigorously proves them correct and highlights the trade-offs required by the use of RDMA.
Model Trees for Personalization

As more commerce and media consumption are being conducted online, a wealth of new opportunities are emerging for personalized advertising. We propose a general methodology, Model Trees for Personalization (MTP), for tackling a broad class of personalized decision-making problems including personalized advertising. The MTPs learn an interpretable market segmentation driven by differences in user behavior. Using this methodology, we design two new algorithms for fundamental problems in personalized advertising -- Choice Model Trees (CMTs) for the user ad-response prediction problem, and Isotonic Regression Model Trees (IRMTs) for the bid landscape forecasting problem. We provide a customizable, computationally-efficient, and open-source code base for training MTPs in Python. We train our IRMT algorithm on historical bidding data from three different ad exchanges and show that the IRMT achieves 5-29% improvement in bid landscape forecasting accuracy over the model that a leading demand-side platform (DSP) provider currently uses in production.
Embedded hyper-parameter tuning by Simulated Annealing

We propose a new metaheuristic training scheme that combines Stochastic Gradient Descent (SGD) and Discrete Optimization in an unconventional way. Our idea is to define a discrete neighborhood of the current SGD point containing a number of "potentially good moves" that exploit gradient information, and to search this neighborhood by using a classical metaheuristic scheme borrowed from Discrete Optimization. In the present paper we investigate the use of a simple Simulated Annealing (SA) metaheuristic that accepts/rejects a candidate new solution in the neighborhood with a probability that depends both on the new solution quality and on a parameter (the temperature) which is modified over time to lower the probability of accepting worsening moves. We use this scheme as an automatic way to perform hyper-parameter tuning, hence the title of the paper. A distinctive feature of our scheme is that hyper-parameters are modified within a single SGD execution (and not in an external loop, as customary) and evaluated on the fly on the current minibatch, i.e., their tuning is fully embedded within the SGD algorithm. The use of SA for training is not new, but previous proposals were mainly intended for non-differentiable objective functions for which SGD is not applied due to the lack of gradients. On the contrary, our SA method requires differentiability of (a proxy of) the loss function, and leverages on the availability of a gradient direction to define local moves that have a large probability to improve the current solution. Computational results on image classification (CIFAR-10) are reported, showing that the proposed approach leads to an improvement of the final validation accuracy for modern Deep Neural Networks such as ResNet34 and VGG16.
Learning to Explain: Answering Why-Questions via Rephrasing

Providing plausible responses to why questions is a challenging but critical goal for language based human-machine interaction. Explanations are challenging in that they require many different forms of abstract knowledge and reasoning. Previous work has either relied on human-curated structured knowledge bases or detailed domain representation to generate satisfactory explanations. They are also often limited to ranking pre-existing explanation choices. In our work, we contribute to the under-explored area of generating natural language explanations for general phenomena. We automatically collect large datasets of explanation-phenomenon pairs which allow us to train sequence-to-sequence models to generate natural language explanations. We compare different training strategies and evaluate their performance using both automatic scores and human ratings. We demonstrate that our strategy is sufficient to generate highly plausible explanations for general open-domain phenomena compared to other models trained on different datasets.
Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse Systems

SLAM based techniques are often adopted for solving the navigation problem for the drones in GPS denied environment. Despite the widespread success of these approaches, they have not yet been fully exploited for automation in a warehouse system due to expensive sensors and setup requirements. This paper focuses on the use of low-cost monocular camera-equipped drones for performing warehouse management tasks like inventory scanning and position update. The methods introduced are at par with the existing state of warehouse environment present today, that is, the existence of a grid network for the ground vehicles, hence eliminating any additional infrastructure requirement for drone deployment. As we lack scale information, that in itself forbids us to use any 3D techniques, we focus more towards optimizing standard image processing algorithms like the thick line detection and further developing it into a fast and robust grid localization framework. In this paper, we show different line detection algorithms, their significance in grid localization and their limitations. We further extend our proposed implementation towards a real-time navigation stack for an actual warehouse inspection case scenario. Our line detection method using skeletonization and centroid strategy works considerably even with varying light conditions, line thicknesses, colors, orientations, and partial occlusions. A simple yet effective Kalman Filter has been used for smoothening the {\rho} and {\theta} outputs of the two different line detection methods for better drone control while grid following. A generic strategy that handles the navigation of the drone on a grid for completion of the allotted task is also developed. Based on the simulation and real-life experiments, the final developments on the drone localization and navigation in a structured environment are discussed.
Higher-order moment portfolio optimization via difference-of-convex programming and sums-of-squares

We are interested in developing DC (Difference-of-Convex) programming approach for solving higher-order moment (Mean-Variance-Skewness-Kurtosis) portfolio selection problem. The portfolio selection with higher moments can be formulated as a nonconvex quartic multivariate polynomial optimization. Based on the recent development in Difference-of-Convex-Sums-of-Squares (DCSOS) decomposition techniques for polynomial optimization, we can reformulate this problem as a DC program which can be solved by a well-known DC algorithm - DCA. We have also proposed an improved DC algorithm called Boosted-DCA (BDCA) based on an Armijo type line search to accelerate the convergence of DCA. We introduce this acceleration technique to both DC algorithm based on DCSOS decomposition proposed in this paper and the DC algorithm based on universal DC decomposition proposed in our previous paper. Results in numerical simulation show good performance of our proposed algorithms in portfolio optimization.
Magnon-phonon interactions in magnetic insulators

We address the theory of magnon-phonon interactions and compute the corresponding quasiparticle and transport lifetimes in magnetic insulators, with a focus on yttrium iron garnet at intermediate temperatures from anisotropy- and exchange-mediated magnon-phonon interactions, the latter being derived from the volume dependence of the Curie temperature. We find in general weak effects of phonon scattering on magnon transport and the Gilbert damping of the macrospin Kittel mode. The magnon transport lifetime differs from the quasiparticle lifetime at shorter wavelengths.
Continual learning improves Internet video streaming

We describe Fugu, a continual learning algorithm for bitrate selection in streaming video. Each day, Fugu retrains a neural network from its experience in deployment over the prior week. The neural network predicts how long it would take to transfer each available version of the upcoming video chunks, given recent history and internal TCP statistics. We evaluate Fugu with Puffer, a public website we built that streams live TV using Fugu and existing algorithms. Over a nine-day period in January 2019, Puffer streamed 8,131 hours of video to 3,719 unique users. Compared with buffer-based control, MPC, RobustMPC, and Pensieve, Fugu performs better on several metrics: it stalls 5-13x less and has better and more stable video quality, and users who were randomly assigned to Fugu streamed for longer on average before quitting or reloading. We find that TCP statistics can aid bitrate selection, that congestion-control and bitrate-selection algorithms are best selected jointly, that probabilistic transfer-time estimates that consider chunk size outperform point estimates of throughput, and that both stalls and video quality have an influence on how long users choose to keep a video stream playing. Fugu's results suggest that continual learning of network algorithms in situ is a promising area of research. To support further investigation, we plan to operate Puffer for several years and will open it to researchers for evaluating new congestion-control and bitrate-selection approaches.
Transfer Learning in the Field of Renewable Energies - A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid

In recent years, transfer learning gained particular interest in the field of vision and natural language processing. In the research field of vision, e.g., deep neural networks and transfer learning techniques achieve almost perfect classification scores within minutes. Nonetheless, these techniques are not yet widely applied in other domains. Therefore, this article identifies critical challenges and shows potential solutions for power forecasts in the field of renewable energies. It proposes a framework utilizing transfer learning techniques in wind power forecasts with limited or no historical data. On the one hand, this allows evaluating the applicability of transfer learning in the field of renewable energy. On the other hand, by developing automatic procedures, we assure that the proposed methods provide a framework that applies to domains in organic computing as well.
On Provisioning Cellular Networks for Distributed Inference

Wireless traffic attributable to machine learning (ML) inference workloads is increasing with the proliferation of applications and smart wireless devices leveraging ML inference. Owing to limited compute capabilities at these "edge" devices, achieving high inference accuracy often requires coordination with a remote compute node or "cloud" over the wireless cellular network. The accuracy of this distributed inference is, thus, impacted by the communication rate and reliability offered by the cellular network. In this paper, an analytical framework is proposed to characterize inference accuracy as a function of cellular network design. Using the developed framework, it is shown that cellular network should be provisioned with a minimum density of access points (APs) to guarantee a target inference accuracy, and the inference accuracy achievable at asymptotically high AP density is limited by the air-interface bandwidth. Furthermore, the minimum accuracy required of edge inference to deliver a target inference accuracy is shown to be inversely proportional to the density of APs and the bandwidth.
Color Constancy Convolutional Autoencoder

In this paper, we study the importance of pretraining for the generalization capability in the color constancy problem. We propose two novel approaches based on convolutional autoencoders: an unsupervised pre-training algorithm using a fine-tuned encoder and a semi-supervised pre-training algorithm using a novel composite-loss function. This enables us to solve the data scarcity problem and achieve competitive, to the state-of-the-art, results while requiring much fewer parameters on ColorChecker RECommended dataset. We further study the over-fitting phenomenon on the recently introduced version of INTEL-TUT Dataset for Camera Invariant Color Constancy Research, which has both field and non-field scenes acquired by three different camera models.
A Deterministic Algorithm for Counting Colorings with 2-Delta Colors

We give a polynomial time deterministic approximation algorithm (an FPTAS) for counting the number of q-colorings of a graph of maximum degree Delta, provided only that q ≥ 2Delta. This substantially improves on previous deterministic algorithms for this problem, the best of which requires q ≥ 2.58Delta, and matches the natural bound for randomized algorithms obtained by a straightforward application of Markov chain Monte Carlo. In the case when the graph is also triangle-free, we show that our algorithm applies under the weaker condition q ≥ α∆+β, where α ≈ 1.764 and β = β(α) are absolute constants. Our result applies more generally to list colorings, and to the partition function of the anti-ferromagnetic Potts model. The core of our argument is the establishment of a region in the complex plane in which the Potts model partition function (a classical graph polynomial) has no zeros. This result, which substantially sharpens previous work on the same problem, is of independent interest. Our algorithms follow immediately from zero-freeness via the “polynomial interpolation" method of Barvinok. Interestingly, our method for identifying the zero-free region leverages probabilistic and combinatorial ideas that have been used in the analysis of Markov chains.
A low-cost CMOS compatible serpentine-structured polysilicon-based microbolometer array

This work reports the design, fabrication and initial characterization of novel low-cost CMOS compatible microbolometers and a 16/spl times/16 demonstration focal plane array based on the new technology. The bolometers are fabricated as part of a standard CMOS process with maskless postprocessing that includes frontside isotropic silicon dry etching in order to release the serpentine microstructures of the bolometers. FPA readout is based on differential current integration per column with an internal 5bit DAC for non-uniformity correction of each pixel. Fabricated devices are measured and compared with design and simulation.
Fluids with anisotropic viscosity

Motivated by rotating fluids, we study incompressible fluids with anisotropic viscosity. We use anisotropic spaces that enable us to prove existence theorems for less regular initial data than usual. In the case of rotating fluids, in the whole space, we prove Strichartz-type anisotropic, dispersive estimates which allow us to prove global wellposedness for fast enough rotation.
For Whom the Bell (Curve) Tolls: A to F, Trade Your Grade Based on the Net Present Value of Friendships with Financial Incentives

We discuss a possible solution to an unintended consequence of having grades, certificates, rankings, and other diversions in the act of transferring knowledge, zooming in specifically on the topic of grading on a curve. We conduct a thought experiment, taking a chapter (and some more? ) from the financial markets (where we trade pollution, etc.), to create a marketplace, where we can trade our grade, in a structure similar to the interest rate swap. We connect this to broader problems that creep up when we attach ourselves to artificial labels. The policy and philosophical implications of our arguments suggest that all trophies we collect (including certificates, grades, medals, etc.) should be viewed as personal or private equity (borrowing another widely used term in finance) and that we should not use them to determine the outcomes in any selection criteria except those with a cutoff point—not for jobs, higher studies, or financial scholarships, other than for entertainment or spectator sports. We suggest alternate methods for grading and performance assessment and suggest the use of tests similar to the Turing test for intelligence for evaluation of teaching and learning. TOPICS: Performance measurement, private equity
The Mechanism of the Origin and Development of Lightning From Initiating Event to Initial Breakdown Pulses (v.2)

Based on experimental results of recent years, this article presents a qualitative description of a possible mechanism (termed the Mechanism) covering the main stages of lightning initiation, starting before and including the initiating event, followed by the initial electric field change (IEC), followed by the first few initial breakdown pulses (IBPs). The Mechanism assumes initiation occurs in a region of ~1 km3 with average electric field E > 0.3 MV/(m·atm), which contains, because of turbulence, numerous small “Eth volumes” of ~10−4–10−3 m3 with E ≥ 3 MV/(m·atm). The Mechanism allows for lightning initiation by either of two observed types of events: a high‐power, very high frequency (VHF) event such as a Narrow Bipolar Event or a weak VHF event. According to the Mechanism, both types of initiating events are caused by a group of relativistic runaway electron avalanche particles (where the initial electrons are secondary particles of an extensive air shower) passing through many Eth volumes, thereby causing the nearly simultaneous launching of many positive streamer flashes. Due to ionization‐heating instability, unusual plasma formations (UPFs) appear along the streamers' trajectories. These UPFs combine into three‐dimensional (3‐D) networks of hot plasma channels during the IEC, resulting in its observed weak current flow. The subsequent development and combination of two (or more) of these 3‐D networks of hot plasma channels then causes the first IBP. Each subsequent IBP is caused when another 3‐D network of hot plasma channels combines with the chain of networks caused by earlier IBPs.
Extinction time of logistic branching processes
in a Brownian environment

In this paper, we study the extinction time of logistic branching processes which are perturbed by an independent random environment driven by a Brownian motion. Our arguments use a Lamperti-type representation which is interesting on its own right and provides a one to one correspondence between the latter family of processes and the family of Feller diffusions which are perturbed by an independent spectrally positive Levy process. When the independent random perturbation (of the Feller diffusion) is driven by a subordinator then the logistic branching processes in a Brownian environment converges to a specified distribution; otherwise, it becomes extinct a.s. In the latter scenario, and following a similar approach to [Lambert, Ann. Appl. Probab, 2005], we provide the expectation and the Laplace transform of the absorption time, as a functional of the solution to a Ricatti differential equation. In particular, the latter characterises the law of the process coming down from infinity.
Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model

The rough Bergomi (rBergomi) model, introduced recently in Bayer et al. [Pricing under rough volatility. Quant. Finance, 2016, 16(6), 887–904], is a promising rough volatility model in quantitative finance. It is a parsimonious model depending on only three parameters, and yet remarkably fits empirical implied volatility surfaces. In the absence of analytical European option pricing methods for the model, and due to the non-Markovian nature of the fractional driver, the prevalent option is to use the Monte Carlo (MC) simulation for pricing. Despite recent advances in the MC method in this context, pricing under the rBergomi model is still a time-consuming task. To overcome this issue, we have designed a novel, hierarchical approach, based on: (i) adaptive sparse grids quadrature (ASGQ), and (ii) quasi-Monte Carlo (QMC). Both techniques are coupled with a Brownian bridge construction and a Richardson extrapolation on the weak error. By uncovering the available regularity, our hierarchical methods demonstrate substantial computational gains with respect to the standard MC method. They reach a sufficiently small relative error tolerance in the price estimates across different parameter constellations, even for very small values of the Hurst parameter. Our work opens a new research direction in this field, i.e. to investigate the performance of methods other than Monte Carlo for pricing and calibrating under the rBergomi model.
MISO is Making a Comeback With Better Proofs and Rates

MISO, also known as Finito, was one of the first stochastic variance reduced methods discovered, yet its popularity is fairly low. Its initial analysis was significantly limited by the so-called Big Data assumption. Although the assumption was lifted in subsequent work using negative momentum, this introduced a new parameter and required knowledge of strong convexity and smoothness constants, which is rarely possible in practice. We rehabilitate the method by introducing a new variant that needs only smoothness constant and does not have any extra parameters. Furthermore, when removing the strong convexity constant from the stepsize, we present a new analysis of the method, which no longer uses the assumption that every component is strongly convex. This allows us to also obtain so far unknown nonconvex convergence of MISO. To make the proposed method efficient in practice, we derive minibatching bounds with arbitrary uniform sampling that lead to linear speedup when the expected minibatch size is in a certain range. Our numerical experiments show that MISO is a serious competitor to SAGA and SVRG and sometimes outperforms them on real datasets.
Evaluating the Responsiveness of Accelerometry to Detect Change in Physical Activity

The responsiveness to change of the Actical and ActiGraph accelerometers was assessed in children and adolescents. Participants (N = 208) aged 6 to 16 years completed two simulated free-living protocols, one with primarily light-to-moderate physical activity (PA) and one with mostly moderate-to-vigorous PA. Time in sedentary, light, moderate, and vigorous PA was estimated using 8 previously developed cut-points (4 for Actical and 4 for ActiGraph) and 5-sec, 15-sec, and 30-sec epochs. Accelerometer responsiveness for detecting differences in PA between protocols was assessed using standardized response means (SRMs). SRM values ≥. 8 represented high responsiveness to change. Both accelerometers showed high responsiveness for all PA intensities (SRMs = 1.2–4.7 for Actical and 1.1–3.3 for ActiGraph). All cut-points and epoch lengths yielded high responsiveness, and choice of cut-points and epoch length had little effect on responsiveness. Thus, both the Actical and ActiGraph can detect change in PA in a simulated free-living setting, irrespective of cut-point selection or epoch length.