Dynamical Supersymmetry Breaking from Simple Quivers

We construct a simple local model of dynamical supersymmetry breaking. The model is a one generation SU(5) that arises from a IIB Z_N orientifold. It does not admit a runaway direction and is argued to stabilize the blowup mode related to the corresponding U(1) factor. The theory demonstrates the existence of a new class of "blowup" fractional branes. We further discuss a compact realization of the quiver on a Calabi-Yau 3-fold which enables one to add fluxes and stabilize the complex structure moduli.Comment: 4 pages, revtex4; An error was corrected following [arXiv:0707.0298

The development of the idea of imminent Russian surprise attack

Thesis (M.A.)--Boston UniversityThe problem of this thesis is to trace the development of American attitudes toward Russia from the closing phase of World War II to the point at which the fear of the imminent danger of Russian surprise attack was a basic part of this attitude. Although Americans generally had been negatively disposed toward Russia before the Second World War, during the first year of the alliance this attitude underwent a drastic change. Both governmental and public opinion by 1943 were overwhelmingly favorable toward the Soviet Union. Statements by government officials, articles by journalists, and public opinion polls indicated a genuine admiration for Russia and an expectation that future relations between the two countries would be characterized by mutual respect and co-operation. There was a sub-stratum of hostility and distrust in some quarters, but it represented a distinct minority. This optimism on the,part of the American people and their government continued into the closing phases of the war. Americans were willing to concede to Russia the territories she deman4ed and agreed that Russia should have friendly governments in the states of Eastern Europe. Under the terms of the Yalta Conference in early 1945, these "friendly governments" in Russia's western neighbors would be established by the occupying forces, would be representative of all democratic elements in the population, and would hold free elections as soon as possible [TRUNCATED

Constraining Modular Inflation in the MSSM from Giant Q-Ball Formation

We discuss constraints on which flat directions can have large vacuum expectation values (VEVs) after inflation. We show that only flat directions which are not charged under B-L and develop positive pressure due to renormalization group effects can have large VEVs of order \Mp. For example, within the MSSM only the $H_uH_d$ flat direction is found to be viable. This strongly constrains the embedding of a broad class of inflationary models in the MSSM or some other supersymmetric extension of the SM. For flat directions with negative pressure, the condensate fragments into very large Q-balls which we call Q-giants. We discuss the formation, evolution and reheating of these Q-giants and show that they decay too late. The analysis requires taking into account new phases of the flat directions, which have been overlooked in the formation and dynamics of the Q-balls. These constraints may be ameliorated by invoking a short period of thermal inflation. The latter, however, is viable in a very narrow window of parameter space and requires fine tuning.Comment: 40 pages, 3 figure

Don't Be So Sure! Boosting ASR Decoding via Confidence Relaxation

Automatic Speech Recognition (ASR) systems frequently use a search-based decoding strategy aiming to find the best attainable transcript by considering multiple candidates. One prominent speech recognition decoding heuristic is beam search, which seeks the transcript with the greatest likelihood computed using the predicted distribution. While showing substantial performance gains in various tasks, beam search loses some of its effectiveness when the predicted probabilities are highly confident, i.e., the predicted distribution is massed for a single or very few classes. We show that recently proposed Self-Supervised Learning (SSL)-based ASR models tend to yield exceptionally confident predictions that may hamper beam search from truly considering a diverse set of candidates. We perform a layer analysis to reveal and visualize how predictions evolve, and propose a decoding procedure that improves the performance of fine-tuned ASR models. Our proposed approach does not require further training beyond the original fine-tuning, nor additional model parameters. In fact, we find that our proposed method requires significantly less inference computation than current approaches. We propose aggregating the top M layers, potentially leveraging useful information encoded in intermediate layers, and relaxing model confidence. We demonstrate the effectiveness of our approach by conducting an empirical study on varying amounts of labeled resources and different model sizes, showing consistent improvements in particular when applied to low-resource scenarios.Comment: Accepted to AAAI 202

First Direct Detection Limits on sub-GeV Dark Matter from XENON10

The first direct detection limits on dark matter in the MeV to GeV mass range are presented, using XENON10 data. Such light dark matter can scatter with electrons, causing ionization of atoms in a detector target material and leading to single- or few-electron events. We use 15 kg-days of data acquired in 2006 to set limits on the dark-matter-electron scattering cross section. The strongest bound is obtained at 100 MeV where sigma_e < 3 x 10^{-38} cm^2 at 90% CL, while dark matter masses between 20 MeV and 1 GeV are bounded by sigma_e < 10^{-37} cm^2 at 90% CL. This analysis provides a first proof-of-principle that direct detection experiments can be sensitive to dark matter candidates with masses well below the GeV scale.Comment: Submitted to PR

Deep-STORM: super-resolution single-molecule microscopy by deep learning

We present an ultra-fast, precise, parameter-free method, which we term Deep-STORM, for obtaining super-resolution images from stochastically-blinking emitters, such as fluorescent molecules used for localization microscopy. Deep-STORM uses a deep convolutional neural network that can be trained on simulated data or experimental measurements, both of which are demonstrated. The method achieves state-of-the-art resolution under challenging signal-to-noise conditions and high emitter densities, and is significantly faster than existing approaches. Additionally, no prior information on the shape of the underlying structure is required, making the method applicable to any blinking data-set. We validate our approach by super-resolution image reconstruction of simulated and experimentally obtained data.Comment: 7 pages, added code download reference and DOI for the journal versio