Shot noise limited characterization of femtosecond light pulses

Probing the evolution of physical systems at the femto- or attosecond timescale with light requires accurate characterization of ultrashort optical pulses. The time profiles of such pulses are usually retrieved by methods utilizing optical nonlinearities, which require significant signal powers and operate in a limited spectral range\cite{Trebino_Review_of_Scientific_Instruments97,Walmsley_Review_09}. We present a linear self-referencing characterization technique based on time domain localization of the pulse spectral components, operated in the single-photon regime. Accurate timing of the spectral slices is achieved with standard single photon detectors, rendering the technique applicable in any spectral range from near infrared to deep UV. Using detection electronics with about $70$ ps response, we retrieve the temporal profile of a picowatt pulse train with $\sim10$ fs resolution, setting a new scale of sensitivity in ultrashort pulse characterization.Comment: Supplementary information contained in raw dat

Time-frequency representation of earthquake accelerograms and inelastic structural response records using the adaptive chirplet decomposition and empirical mode decomposition

In this paper, the adaptive chirplet decomposition combined with the Wigner-Ville transform and the empirical mode decomposition combined with the Hilbert transform are employed to process various non-stationary signals (strong ground motions and structural responses). The efficacy of these two adaptive techniques for capturing the temporal evolution of the frequency content of specific seismic signals is assessed. In this respect, two near-field and two far-field seismic accelerograms are analyzed. Further, a similar analysis is performed for records pertaining to the response of a 20-story steel frame benchmark building excited by one of the four accelerograms scaled by appropriate factors to simulate undamaged and severely damaged conditions for the structure. It is shown that the derived joint time–frequency representations of the response time histories capture quite effectively the influence of non-linearity on the variation of the effective natural frequencies of a structural system during the evolution of a seismic event; in this context, tracing the mean instantaneous frequency of records of critical structural responses is adopted. The study suggests, overall, that the aforementioned techniques are quite viable tools for detecting and monitoring damage to constructed facilities exposed to seismic excitations

Spherical harmonic transform with GPUs

We describe an algorithm for computing an inverse spherical harmonic transform suitable for graphic processing units (GPU). We use CUDA and base our implementation on a Fortran90 routine included in a publicly available parallel package, S2HAT. We focus our attention on the two major sequential steps involved in the transforms computation, retaining the efficient parallel framework of the original code. We detail optimization techniques used to enhance the performance of the CUDA-based code and contrast them with those implemented in the Fortran90 version. We also present performance comparisons of a single CPU plus GPU unit with the S2HAT code running on either a single or 4 processors. In particular we find that use of the latest generation of GPUs, such as NVIDIA GF100 (Fermi), can accelerate the spherical harmonic transforms by as much as 18 times with respect to S2HAT executed on one core, and by as much as 5.5 with respect to S2HAT on 4 cores, with the overall performance being limited by the Fast Fourier transforms. The work presented here has been performed in the context of the Cosmic Microwave Background simulations and analysis. However, we expect that the developed software will be of more general interest and applicability

Print-Scan Resilient Text Image Watermarking Based on Stroke Direction Modulation for Chinese Document Authentication

Print-scan resilient watermarking has emerged as an attractive way for document security. This paper proposes an stroke direction modulation technique for watermarking in Chinese text images. The watermark produced by the idea offers robustness to print-photocopy-scan, yet provides relatively high embedding capacity without losing the transparency. During the embedding phase, the angle of rotatable strokes are quantized to embed the bits. This requires several stages of preprocessing, including stroke generation, junction searching, rotatable stroke decision and character partition. Moreover, shuffling is applied to equalize the uneven embedding capacity. For the data detection, denoising and deskewing mechanisms are used to compensate for the distortions induced by hardcopy. Experimental results show that our technique attains high detection accuracy against distortions resulting from print-scan operations, good quality photocopies and benign attacks in accord with the future goal of soft authentication

Quantum-dot based photonic quantum networks

Quantum dots embedded in photonic nanostructures have in recent years proven to be a very powerful solid-state platform for quantum optics experiments. The combination of near-unity radiative coupling of a single quantum dot to a photonic mode and the ability to eliminate decoherence processes imply that an unprecedented light-matter interface can be obtained. As a result, high-cooperativity photon-emitter quantum interfaces can be constructed opening a path-way to deterministic photonic quantum gates for quantum-information processing applications. In the present manuscript, I review current state-of-the-art on quantum dot devices and their applications for quantum technology. The overarching long-term goal of the research field is to construct photonic quantum networks where remote entanglement can be distributed over long distances by photons

Data-driven Job Search Engine Using Skills and Company Attribute Filters

According to a report online, more than 200 million unique users search for jobs online every month. This incredibly large and fast growing demand has enticed software giants such as Google and Facebook to enter this space, which was previously dominated by companies such as LinkedIn, Indeed and CareerBuilder. Recently, Google released their "AI-powered Jobs Search Engine", "Google For Jobs" while Facebook released "Facebook Jobs" within their platform. These current job search engines and platforms allow users to search for jobs based on general narrow filters such as job title, date posted, experience level, company and salary. However, they have severely limited filters relating to skill sets such as C++, Python, and Java and company related attributes such as employee size, revenue, technographics and micro-industries. These specialized filters can help applicants and companies connect at a very personalized, relevant and deeper level. In this paper we present a framework that provides an end-to-end "Data-driven Jobs Search Engine". In addition, users can also receive potential contacts of recruiters and senior positions for connection and networking opportunities. The high level implementation of the framework is described as follows: 1) Collect job postings data in the United States, 2) Extract meaningful tokens from the postings data using ETL pipelines, 3) Normalize the data set to link company names to their specific company websites, 4) Extract and ranking the skill sets, 5) Link the company names and websites to their respective company level attributes with the EVERSTRING Company API, 6) Run user-specific search queries on the database to identify relevant job postings and 7) Rank the job search results. This framework offers a highly customizable and highly targeted search experience for end users.Comment: 8 pages, 10 figures, ICDM 201

Subradiant states of quantum bits coupled to a one-dimensional waveguide

The properties of coupled emitters can differ dramatically from those of their individual constituents. Canonical examples include sub- and super-radiance, wherein the decay rate of a collective excitation is reduced or enhanced due to correlated interactions with the environment. Here, we systematically study the properties of collective excitations for regularly spaced arrays of quantum emitters coupled to a one-dimensional (1D) waveguide. We find that, for low excitation numbers, the modal properties are well-characterized by spin waves with a definite wavevector. Moreover, the decay rate of the most subradiant modes obeys a universal scaling with a cubic suppression in the number of emitters. Multi-excitation subradiant eigenstates can be built from fermionic combinations of single excitation eigenstates; such "fermionization" results in multiple excitations that spatially repel one another. We put forward a method to efficiently create and measure such subradiant states, which can be realized with superconducting qubits. These measurement protocols probe both real-space correlations (using on-site dispersive readout) and temporal correlations in the emitted field (using photon correlation techniques).Comment: 21 pages, 9 figure