Exploring the Vacuum Geometry of N=1 Gauge Theories

Using techniques of algorithmic algebraic geometry, we present a new and efficient method for explicitly computing the vacuum space of N=1 gauge theories. We emphasize the importance of finding special geometric properties of these spaces in connecting phenomenology to guiding principles descending from high-energy physics. We exemplify the method by addressing various subsectors of the MSSM. In particular the geometry of the vacuum space of electroweak theory is described in detail, with and without right-handed neutrinos. We discuss the impact of our method on the search for evidence of underlying physics at a higher energy. Finally we describe how our results can be used to rule out certain top-down constructions of electroweak physics.Comment: 35 pages, 2 figures, LaTe

Vacuum Geometry and the Search for New Physics

We propose a new guiding principle for phenomenology: special geometry in the vacuum space. New algorithmic methods which efficiently compute geometric properties of the vacuum space of N=1 supersymmetric gauge theories are described. We illustrate the technique on subsectors of the MSSM. The fragility of geometric structure that we find in the moduli space motivates phenomenologically realistic deformations of the superpotential, while arguing against others. Special geometry in the vacuum may therefore signal the presence of string physics underlying the low-energy effective theory.Comment: 8 pages, LaTeX; v2: revised title, minor changes in wording, reference adde

Modified SPLICE and its Extension to Non-Stereo Data for Noise Robust Speech Recognition

In this paper, a modification to the training process of the popular SPLICE algorithm has been proposed for noise robust speech recognition. The modification is based on feature correlations, and enables this stereo-based algorithm to improve the performance in all noise conditions, especially in unseen cases. Further, the modified framework is extended to work for non-stereo datasets where clean and noisy training utterances, but not stereo counterparts, are required. Finally, an MLLR-based computationally efficient run-time noise adaptation method in SPLICE framework has been proposed. The modified SPLICE shows 8.6% absolute improvement over SPLICE in Test C of Aurora-2 database, and 2.93% overall. Non-stereo method shows 10.37% and 6.93% absolute improvements over Aurora-2 and Aurora-4 baseline models respectively. Run-time adaptation shows 9.89% absolute improvement in modified framework as compared to SPLICE for Test C, and 4.96% overall w.r.t. standard MLLR adaptation on HMMs.Comment: Submitted to Automatic Speech Recognition and Understanding (ASRU) 2013 Worksho

Transition from confined to bulk dynamics in symmetric star-linear polymer mixtures

We report on the linear viscoelastic properties of mixtures comprising multiarm star (as model soft colloids) and long linear chain homopolymers in a good solvent. In contrast to earlier works, we investigated symmetric mixtures (with a size ratio of 1) and showed that the polymeric and colloidal responses can be decoupled. The adopted experimental protocol involved probing the linear chain dynamics in different star environments. To this end, we studied mixtures with different star mass fraction, which was kept constant while linear chains were added and their entanglement plateau modulus ($G_p$) and terminal relaxation time ($\tau_d$) were measured as functions of their concentration. Two distinct scaling regimes were observed for both $G_p$ and $\tau_d$: at low linear polymer concentrations, a weak concentration dependence was observed, that became even weaker as the fraction of stars in the mixtures increased into the star glassy regime. On the other hand, at higher linear polymer concentrations, the classical entangled polymer scaling was recovered. Simple scaling arguments show that the threshold crossover concentration between the two regimes corresponds to the maximum osmotic star compression and signals the transition from confined to bulk dynamics. These results provide the needed ingredients to complete the state diagram of soft colloid-polymer mixtures and investigate their dynamics at large polymer-colloid size ratios. They also offer an alternative way to explore aspects of the colloidal glass transition and the polymer dynamics in confinement. Finally, they provide a new avenue to tailor the rheology of soft composites.Comment: 9 Figure

Restriction enzymes use a 24 dimensional coding space to recognize 6 base long DNA sequences

Restriction enzymes recognize and bind to specific sequences on invading bacteriophage DNA. Like a key in a lock, these proteins require many contacts to specify the correct DNA sequence. Using information theory we develop an equation that defines the number of independent contacts, which is the dimensionality of the binding. We show that EcoRI, which binds to the sequence GAATTC, functions in 24 dimensions. Information theory represents messages as spheres in high dimensional spaces. Better sphere packing leads to better communications systems. The densest known packing of hyperspheres occurs on the Leech lattice in 24 dimensions. We suggest that the single protein EcoRI molecule employs a Leech lattice in its operation. Optimizing density of sphere packing explains why 6 base restriction enzymes are so common.Comment: Version 1: 31 pages, 3 figures, 1 table; Version 2: 33 pages, 3 figures, 1 table, responses to reviewers, new ref

The Library of Babel

We show that heavy pure states of gravity can appear to be mixed states to almost all probes. Our arguments are made for $\rm{AdS}_5$ Schwarzschild black holes using the field theory dual to string theory in such spacetimes. Our results follow from applying information theoretic notions to field theory operators capable of describing very heavy states in gravity. For certain supersymmetric states of the theory, our account is exact: the microstates are described in gravity by a spacetime ``foam'', the precise details of which are invisible to almost all probes.Comment: 7 pages, 1 figure, Essay receiving honorable mention in the 2005 Gravity Research Foundation essay competitio

Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction

Prediction-based active perception has shown the potential to improve the navigation efficiency and safety of the robot by anticipating the uncertainty in the unknown environment. The existing works for 3D shape prediction make an implicit assumption about the partial observations and therefore cannot be used for real-world planning and do not consider the control effort for next-best-view planning. We present Pred-NBV, a realistic object shape reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model trained on the ShapeNet dataset, and an information and control effort-based next-best-view method to address these issues. Pred-NBV shows an improvement of 25.46% in object coverage over the traditional methods in the AirSim simulator, and performs better shape completion than PoinTr, the state-of-the-art shape completion model, even on real data obtained from a Velodyne 3D LiDAR mounted on DJI M600 Pro.Comment: 6 pages, 4 figures, 2 tables. Accepted to IROS 202

MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction

We propose MAP-NBV, a prediction-guided active algorithm for 3D reconstruction with multi-agent systems. Prediction-based approaches have shown great improvement in active perception tasks by learning the cues about structures in the environment from data. But these methods primarily focus on single-agent systems. We design a next-best-view approach that utilizes geometric measures over the predictions and jointly optimizes the information gain and control effort for efficient collaborative 3D reconstruction of the object. Our method achieves 22.75% improvement over the prediction-based single-agent approach and 15.63% improvement over the non-predictive multi-agent approach. We make our code publicly available through our project website: http://raaslab.org/projects/MAPNBV/Comment: 7 pages, 7 figures, 2 tables. Submitted to MRS 202

Significance of tissue microbiopsies in fine needle aspiration cytology

Background: Fine Needle Aspiration Cytology smears prepared through conventional method, often contain well preserved viable tissue fragments which are intact (Tissue Micro biopsies). They will provide information on the tissue architecture and contribute to the tumour ontogeny.Methods: A prospective study of significance of tissue micro biopsies in FNAC were studied and interpreted in the Cytopathology laboratory of Department of Pathology, Tirunelveli Medical College, Tirunelveli. 100 cases with clinically palpable Swellings were studied.Results: Out of 100 cases, 82% of cases were coming under the category of conventional FNAC, 10% of the cases were USG guided and 8% were falling under CT guided FNAC. The organs with highest yield of micro biopsies were lymph nodes 34 cases (34%) followed by breast 24 cases, thyroid 11 cases, lung 8 cases, salivary gland 7 cases, liver and bone and soft tissue 4 cases each, abdominal mass 3cases, pancreas 2 cases, and single case each of ovary, spleen, anterior mediastinum. Of the total 100 cases, 56% of the cases were malignant and 44% of the cases were benign. Among the 56 malignant tumours 41(73.2%) cases were primary tumours and 15cases (26.8%) were metastatic tumours.Conclusions: FNA smears containing micro biopsies help in diagnosis, typing of tumour and predicting possible primary sites in cases of metastatic tumours which were not possible by cytology alone. Hence, this technique can be used to increase the diagnostic accuracy of FNAC if put into practice