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Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. Current mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. We introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed. Extensive simulations show that compared to the current mechanism deployed in by SCE, the DR-VCG mechanism achieves higher participation, increased reliability, and significantly reduced total expenses. Comment: full version of paper accepted to IJCAI'17

We quantify the luminosity contribution of active galactic nuclei (AGN) to the 12 $\mu$m, mid-infrared (MIR; 5-38 $\mu$m), and the total IR (5-1000 $\mu$m) emission in the local AGN detected in the all-sky 70-month Swift/Burst Alert Telescope (BAT) ultra hard X-ray survey. We decompose the IR spectral energy distributions (SEDs) of 587 objects into AGN and starburst components using AGN torus and star-forming galaxy templates. This enables us to recover the AGN torus emission also for low-luminosity end, down to $\log (L_{14-150}/{\rm erg}~{\rm s}^{-1}) \simeq 41$, which typically have significant host galaxy contamination. We find that the luminosity contribution of the AGN to the 12 $\mu$m, the MIR, and the total IR band is an increasing function of the 14-150 keV luminosity. We also find that for the most extreme cases, the IR pure-AGN emission from the torus can extend up to 90 $\mu$m. The obtained total IR AGN luminosity through the IR SED decomposition enables us to estimate the fraction of the sky obscured by dust, i.e., the dust covering factor. We demonstrate that the median of the dust covering factor is always smaller than that of the X-ray obscuration fraction above the AGN bolometric luminosity of $\log (L_{\rm bol}/{\rm erg}~{\rm s}^{-1}) \simeq 42.5$. Considering that X-ray obscuration fraction is equivalent to the covering factor coming from both the dust and gas, it indicates that an additional neutral gas component, along with the dusty torus, is responsible for the absorption of X-ray emission. Comment: 21 pages, 15 figures, accepted for publication in ApJ. The full list of Table 1 is available at http://www.kusastro.kyoto-u.ac.jp/~ichikawa/Table1_MR_20181107.txt

The fluctuations in wind power entering an electrical grid (Irish grid) were analyzed and found to exhibit correlated fluctuations with a self-similar structure, a signature of large-scale correlations in atmospheric turbulence. The statistical structure of temporal correlations for fluctuations in generated and forecast time series was used to quantify two types of forecast error: a timescale error ($e_{\tau}$) that quantifies the deviations between the high frequency components of the forecast and the generated time series, and a scaling error ($e_{\zeta}$) that quantifies the degree to which the models fail to predict temporal correlations in the fluctuations of the generated power. With no $a$ $priori$ knowledge of the forecast models, we suggest a simple memory kernel that reduces both the timescale error ($e_{\tau}$) and the scaling error ($e_{\zeta}$).

We analyze the performance of a data-assimilation algorithm based on a linear feedback control when used with observational data that contains measurement errors. Our model problem consists of dynamics governed by the two-dimension incompressible Navier-Stokes equations, observational measurements given by finite volume elements or nodal points of the velocity field and measurement errors which are represented by stochastic noise. Under these assumptions, the data-assimilation algorithm consists of a system of stochastically forced Navier-Stokes equations. The main result of this paper provides explicit conditions on the observation density (resolution) which guarantee explicit asymptotic bounds, as the time tends to infinity, on the error between the approximate solution and the actual solutions which is corresponding to these measurements, in terms of the variance of the noise in the measurements. Specifically, such bounds are given for the the limit supremum, as the time tends to infinity, of the expected value of the $L^2$-norm and of the $H^1$ Sobolev norm of the difference between the approximating solution and the actual solution. Moreover, results on the average time error in mean are stated.

Priority queues are data structures which store keys in an ordered fashion to allow efficient access to the minimal (maximal) key. Priority queues are essential for many applications, e.g., Dijkstra's single-source shortest path algorithm, branch-and-bound algorithms, and prioritized schedulers. Efficient multiprocessor computing requires implementations of basic data structures that can be used concurrently and scale to large numbers of threads and cores. Lock-free data structures promise superior scalability by avoiding blocking synchronization primitives, but the \emph{delete-min} operation is an inherent scalability bottleneck in concurrent priority queues. Recent work has focused on alleviating this obstacle either by batching operations, or by relaxing the requirements to the \emph{delete-min} operation. We present a new, lock-free priority queue that relaxes the \emph{delete-min} operation so that it is allowed to delete \emph{any} of the $\rho+1$ smallest keys, where $\rho$ is a runtime configurable parameter. Additionally, the behavior is identical to a non-relaxed priority queue for items added and removed by the same thread. The priority queue is built from a logarithmic number of sorted arrays in a way similar to log-structured merge-trees. We experimentally compare our priority queue to recent state-of-the-art lock-free priority queues, both with relaxed and non-relaxed semantics, showing high performance and good scalability of our approach. Comment: Short version as ACM PPoPP'15 poster

The feasibility of realizing overscreened Kondo effect in ultra-cold Fermi gas of atoms with spin $s \ge \tfrac{3}{2}$ in the presence of a localized magnetic impurity atom is proved realistic. Specifying to a system of ultra cold $^{22}$Na Fermi gas and a trapped $^{197}$Au impurity, the mechanism of exchange interaction between the Na and Au atoms is elucidated and the exchange constant is found to be antiferromagnetic. The corresponding exchange Hamiltonian is derived, and the Kondo temperature is estimated at the order of $ 1 \mu$K. Within a weak-coupling renormalization group scheme, it is shown that the coupling renormalizes to the non-Fermi liquid fixed point. Comment: 9 pages, 5 figures

Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expensive and requires vast amounts of training data. Selling such pre-trained models can, therefore, be a lucrative business model. Unfortunately, once the models are sold they can be easily copied and redistributed. To avoid this, a tracking mechanism to identify models as the intellectual property of a particular vendor is necessary. In this work, we present an approach for watermarking Deep Neural Networks in a black-box way. Our scheme works for general classification tasks and can easily be combined with current learning algorithms. We show experimentally that such a watermark has no noticeable impact on the primary task that the model is designed for and evaluate the robustness of our proposal against a multitude of practical attacks. Moreover, we provide a theoretical analysis, relating our approach to previous work on backdooring.

Supernova (SN) 2008D/XRT 080109 is considered to be the only direct detection of a shock breakout from a regular SN to date. While a breakout interpretation was favored by several papers, inconsistencies remain between the observations and current SN shock breakout theory. Most notably, the duration of the luminous X-ray pulse is considerably longer than expected for a spherical breakout through the surface of a type Ibc SN progenitor, and the X-ray radiation features, mainly its flat spectrum and its luminosity evolution, are enigmatic. We apply a recently developed theoretical model for the observed radiation from a Wolf-Rayet SN exploding through a thick wind and show that it naturally explains all the observed features of SN 2008D X-ray emission, including the energetics, the spectrum and the detailed luminosity evolution. We find that the inferred progenitor and SN parameters are typical for an exploding Wolf-Rayet. A comparison of the wind density found at the breakout radius to the density at much larger radii, as inferred by late radio observations, suggests an enhanced mass loss rate taking effect about ten days or less prior to the SN explosion. This finding joins accumulating evidence for a possible late phase in the stellar evolution of massive stars, involving vigorous mass loss a short time before the SN explosion. Comment: 5 pages, 1 figure, submitted to apjL

Let $G$ be a group. Write $G^{*}=G\setminus \{1\}$. An element $x$ of $G^{*}$ will be called deficient if $ \langle x\rangle < C_G(x)$ and it will be called non-deficient if $\langle x\rangle = C_G(x).$ If $x\in G$ is deficient (non-deficient), then the conjugacy class $x^G$ of $x$ in $G$ will be also called deficient (non-deficient). Let $j$ be a non-negative integer. We shall say that the group $G$ has defect $j$, denoted by $G\in D(j)$ or by the phrase "$G$ is a $D(j)$-group", if exactly $j$ non-trivial conjugacy classes of $G$ are deficient. We first determine all finite $D(0)$-groups and $D(1)$-groups. Then we deal with arbitrary $D(0)$-groups and $D(1)$-groups: we find properties of arbitrary $D(0)$-groups and $D(1)$-groups, which force these groups to be finite.

We construct a universally Bayes consistent learning rule that satisfies differential privacy (DP). We first handle the setting of binary classification and then extend our rule to the more general setting of density estimation (with respect to the total variation metric). The existence of a universally consistent DP learner reveals a stark difference with the distribution-free PAC model. Indeed, in the latter DP learning is extremely limited: even one-dimensional linear classifiers are not privately learnable in this stringent model. Our result thus demonstrates that by allowing the learning rate to depend on the target distribution, one can circumvent the above-mentioned impossibility result and in fact, learn \emph{arbitrary} distributions by a single DP algorithm. As an application, we prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal \emph{labeled} sample complexity of $\tilde{O}(d/\varepsilon)$ labeled examples (and with an unlabeled sample complexity that can depend on the target distribution).