Classical and quantum fingerprinting with shared randomness and one-sided error

Within the simultaneous message passing model of communication complexity, under a public-coin assumption, we derive the minimum achievable worst-case error probability of a classical fingerprinting protocol with one-sided error. We then present entanglement-assisted quantum fingerprinting protocols attaining worst-case error probabilities that breach this bound.Comment: 10 pages, 1 figur

Single-qubit optical quantum fingerprinting

We analyze and demonstrate the feasibility and superiority of linear optical single-qubit fingerprinting over its classical counterpart. For one-qubit fingerprinting of two-bit messages, we prepare `tetrahedral' qubit states experimentally and show that they meet the requirements for quantum fingerprinting to exceed the classical capability. We prove that shared entanglement permits 100% reliable quantum fingerprinting, which will outperform classical fingerprinting even with arbitrary amounts of shared randomness.Comment: 4 pages, one figur

Molecular mechanisms underlying the control of antigenic variation in African trypanosomes

African trypanosomes escape the host adaptive immune response by switching their dense protective coat of Variant Surface Glycoprotein (VSG). Each cell expresses only one VSG gene at a time from a telomeric expression site (ES). The [`]pre-genomic' era saw the identification of the range of pathways involving VSG recombination in the context of mono-telomeric VSG transcription. A prominent feature of the early post-genomic era is the description of the molecular machineries involved in these processes. We describe the factors and sequences recently linked to mutually exclusive transcription and VSG recombination, and how these act in the control of the key virulence mechanism of antigenic variatio

Structure-Preserving Neural Networks for the N-body Problem

In order to understand when it is useful to build physics constraints into neural networks, we investigate different neural network topologies to solve the N -body problem. Solving the chaotic N -body problem with high accuracy is a challenging task, requiring special numerical integrators that are able to approximate the trajectories with extreme precision. In [1] it is shown that a neural network can be a viable alternative, offering solutions many orders of magnitude faster. Specialized neural network topologies for applications in scientific computing are still rare compared to specialized neural networks for more classical machine learning applications. However, the number of specialized neural networks for Hamiltonian systems has been growing significantly during the last years [3, 5]. We analyze the performance of SympNets introduced in [5], preserving the symplectic structure of the phase space flow map, for the prediction of trajectories in N -body systems. In particular, we compare the accuracy of SympNets against standard multilayer perceptrons, both inside and outside the range of training data. We analyze our findings using a novel view on the topology of SympNets. Additionally, we also compare SympNets against classical symplectic numerical integrators. While the benefits of symplectic integrators for Hamiltonian systems are well understood, this is not the case for SympNets

A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks

Simulating the evolution of the gravitational N-body problem becomes extremely computationally expensive as N increases since the problem complexity scales quadratically with the number of bodies. We study the use of Artificial Neural Networks (ANNs) to replace expensive parts of the integration of planetary systems. Neural networks that include physical knowledge have grown in popularity in the last few years, although few attempts have been made to use them to speed up the simulation of the motion of celestial bodies. We study the advantages and limitations of using Hamiltonian Neural Networks to replace computationally expensive parts of the numerical simulation. We compare the results of the numerical integration of a planetary system with asteroids with those obtained by a Hamiltonian Neural Network and a conventional Deep Neural Network, with special attention to understanding the challenges of this problem. Due to the non-linear nature of the gravitational equations of motion, errors in the integration propagate. To increase the robustness of a method that uses neural networks, we propose a hybrid integrator that evaluates the prediction of the network and replaces it with the numerical solution if considered inaccurate. Hamiltonian Neural Networks can make predictions that resemble the behavior of symplectic integrators but are challenging to train and in our case fail when the inputs differ ~7 orders of magnitude. In contrast, Deep Neural Networks are easy to train but fail to conserve energy, leading to fast divergence from the reference solution. The hybrid integrator designed to include the neural networks increases the reliability of the method and prevents large energy errors without increasing the computing cost significantly. For this problem, the use of neural networks results in faster simulations when the number of asteroids is >70.Comment: Accepted for publication in the Journal of Computational Physic

Burgundian Quintet: Carol Neuleib, Flute; Barry Kolman, Clarinet; Beth Christensen, Oboe; Mary Dalziel, Bassoon; Rodger Burnett, Horn; December 4, 1974

Hayden AuditoriumWednesday EveningDecember 4, 19748:30 p.m

Burgundian Quintet: Carol Neuleib, Flute; Barry Kolman, Clarinet; Beth Christensen, Oboe; Mary Dalziel, Bassoon; Rodger Burnett, Horn; February 2, 1975

Hayden AuditoriumSunday AfternoonFebruary 2, 19754:00 p.m

Antigenic variation in <i>Trypanosoma brucei</i>: joining the DOTs

African trypanosomes, such as &lt;i&gt;Trypanosoma brucei&lt;/i&gt;, are protistan parasites that cause sleeping sickness. Though first described more than a century ago, trypanosomes remain a blight on the health of the human population and on the economy of sub-Saharan Africa. &lt;i&gt;T. brucei&lt;/i&gt; replicates in the bloodstream of infected mammals and traverses the blood-brain barrier to enter the central nervous system in the late, frequently fatal, stages of the disease. Because of its extracellular lifestyle, &lt;i&gt;T. brucei&lt;/i&gt; is continuously exposed to antibody challenge. To circumvent this, the parasite uses antigenic variation of a surface protein named the variant surface glycoprotein (VSG). Around 107 VSG molecules are expressed on the parasite's cell surface, creating a dense coat that prevents adaptive immunity from detecting or accessing invariant antigens. However, antibodies against the expressed VSG are generated, and periodic switches to an immunologically distinct VSG coat are necessary for parasite survival. Such switches are pre-emptive of the immune response and contribute to the pattern of trypanosome growth seen in an infected host (Figure 1): parasite numbers increase, but then drop as VSG-specific antibodies are raised by the host. Cells that have switched to another VSG coat survive this killing and seed the outgrowth of a subsequent peak of parasites, which is again decimated by anti-VSG immune killing. As a survival strategy, antigenic variation succeeds by prolonging the time that the parasite

The Lantern Vol. 33, No. 2, May 1967

• The Plea • Arrival • Nouvelle • Poem on Theme by Leroi Jones • Night Thoughts • Finals • Caught in the Act • Tomorrow • The Rail • The Price • Lost • It\u27s a Svaden Spring • No Thanks to City Hall • Grinding Them to Dust • Four • Five • Nine • Ten • Twelve • Thirty-Six • Psyched Up and Out • Gutted Gloryhttps://digitalcommons.ursinus.edu/lantern/1091/thumbnail.jp