Atomic quantum gases in Kagom\'e lattices

We demonstrate the possibility of creating and controlling an ideal and \textit{trimerized} optical Kagom\'e lattice, and study the low temperature physics of various atomic gases in such lattices. In the trimerized Kagom\'e lattice, a Bose gas exhibits a Mott transition with fractional filling factors, whereas a spinless interacting Fermi gas at 2/3 filling behaves as a quantum magnet on a triangular lattice. Finally, a Fermi-Fermi mixture at half filling for both components represents a frustrated quantum antiferromagnet with a resonating-valence-bond ground state and quantum spin liquid behavior dominated by continuous spectrum of singlet and triplet excitations. We discuss the method of preparing and observing such quantum spin liquid employing molecular Bose condensates.Comment: 4 pages, 1 figure. Missing affiliations adde

Atomic Fermi gas in the trimerized Kagom\'e lattice at the filling 2/3

We study low temperature properties of an atomic spinless interacting Fermi gas in the trimerized Kagom\'e lattice for the case of two fermions per trimer. The system is described by a quantum spin 1/2 model on the triangular lattice with couplings depending on bonds directions. Using exact diagonalizations we show that the system exhibits non-standard properties of a {\it quantum spin-liquid crystal}, combining a planar antiferromagnetic order with an exceptionally large number of low energy excitations.Comment: 4 pages & 4 figures + 2 tables, better version of Fig.

Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response

Metastatic Merkel cell carcinoma (MCC) and cutaneous squamous cell carcinoma (cSCC) are rare and both show impressive responses to immune checkpoint inhibitor treatment. However, at least 40% of patients do not respond to these expensive and potentially toxic drugs. Development of predictive biomarkers of response and rational, effective combination treatment strategies in these rare, often frail patient populations is challenging. This review discusses the pathophysiology and treatment of MCC and cSCC, with a particular focus on potential biomarkers of response to immunotherapy, and discusses how transfer learning using big data collected from patients with common tumours can be used in combination with deep phenotyping of rare tumours to develop predictive biomarkers and elucidate novel treatment targets

Atomic Bose-Fermi mixtures in an optical lattice

A mixture of ultracold bosons and fermions placed in an optical lattice constitutes a novel kind of quantum gas, and leads to phenomena, which so far have been discussed neither in atomic physics, nor in condensed matter physics. We discuss the phase diagram at low temperatures, and in the limit of strong atom-atom interactions, and predict the existence of quantum phases that involve pairing of fermions with one or more bosons, or, respectively, bosonic holes. The resulting composite fermions may form, depending on the system parameters, a normal Fermi liquid, a density wave, a superfluid liquid, or an insulator with fermionic domains. We discuss the feasibility for observing such phases in current experiments.Comment: 4 pages, 1 eps figure, misprints correcte

Optimal directed searches for continuous gravitational waves

Wide parameter space searches for long lived continuous gravitational wave signals are computationally limited. It is therefore critically important that available computational resources are used rationally. In this paper we consider directed searches, i.e. targets for which the sky position is known accurately but the frequency and spindown parameters are completely unknown. Given a list of such potential astrophysical targets, we therefore need to prioritize. On which target(s) should we spend scarce computing resources? What parameter space region in frequency and spindown should we search? Finally, what is the optimal search set-up that we should use? In this paper we present a general framework that allows to solve all three of these problems. This framework is based on maximizing the probability of making a detection subject to a constraint on the maximum available computational cost. We illustrate the method for a simplified problem