Semi-optimal Practicable Algorithmic Cooling

Algorithmic Cooling (AC) of spins applies entropy manipulation algorithms in open spin-systems in order to cool spins far beyond Shannon's entropy bound. AC of nuclear spins was demonstrated experimentally, and may contribute to nuclear magnetic resonance (NMR) spectroscopy. Several cooling algorithms were suggested in recent years, including practicable algorithmic cooling (PAC) and exhaustive AC. Practicable algorithms have simple implementations, yet their level of cooling is far from optimal; Exhaustive algorithms, on the other hand, cool much better, and some even reach (asymptotically) an optimal level of cooling, but they are not practicable. We introduce here semi-optimal practicable AC (SOPAC), wherein few cycles (typically 2-6) are performed at each recursive level. Two classes of SOPAC algorithms are proposed and analyzed. Both attain cooling levels significantly better than PAC, and are much more efficient than the exhaustive algorithms. The new algorithms are shown to bridge the gap between PAC and exhaustive AC. In addition, we calculated the number of spins required by SOPAC in order to purify qubits for quantum computation. As few as 12 and 7 spins are required (in an ideal scenario) to yield a mildly pure spin (60% polarized) from initial polarizations of 1% and 10%, respectively. In the latter case, about five more spins are sufficient to produce a highly pure spin (99.99% polarized), which could be relevant for fault-tolerant quantum computing.Comment: 13 pages, 5 figure

Mitigation of Posttraumatic Stress Symptoms From Chronic Terror Attacks on Southern Israel

Since 2000, the southern Israeli town of Sderot and neighboring rural region, Otef Aza, have been frequently exposed to nearly identical terror attacks by Hamas. While only a small minority of Otef Aza residents have been diagnosed with posttraumatic stress disorder (PTSD), more than a third of Sderot residents have been so diagnosed. Factors such as social cohesion and ideology may be the unique factors that protect Otef Aza residents from PTSD; however, a gap in the literature exists as to how these same factors might affect PTSD symptomology in Sderot residents. Orthodox religiosity has also been associated with reduced PTSD symptoms in Sderot; however, previous research on religiosity analyzed demographic characteristics and did not use a measure specifically assessing dimensions of religiosity. The purpose of this quantitative study was to examine the impact of community, ideology, and religiosity on PTSD symptoms among Sderot residents. A survey was distributed to a convenience sample of Sderot residents that 118 participants successfully completed. Standard multiple linear regression revealed that ideology, intrinsic religiosity, nonorganizational religious activity, and fulfillment of needs dimension of sense of community were significant predictors of PTSD symptomatology. Study findings suggested protective factors which could help a large portion of the population. The implications for positive social change for Sderot residents include increased positive interactions, sense of well-being, meaning, and value in their lives

Analytical results for the distribution of shortest path lengths in random networks

We present two complementary analytical approaches for calculating the distribution of shortest path lengths in Erdos-R\'enyi networks, based on recursion equations for the shells around a reference node and for the paths originating from it. The results are in agreement with numerical simulations for a broad range of network sizes and connectivities. The average and standard deviation of the distribution are also obtained. In the case that the mean degree scales as $N^{\alpha}$ with the network size, the distribution becomes extremely narrow in the asymptotic limit, namely almost all pairs of nodes are equidistant, at distance $d=\lfloor 1/\alpha \rfloor$ from each other. The distribution of shortest path lengths between nodes of degree $m$ and the rest of the network is calculated. Its average is shown to be a monotonically decreasing function of $m$, providing an interesting relation between a local property and a global property of the network. The methodology presented here can be applied to more general classes of networks.Comment: 12 pages, 4 figures, accepted to EP

Quantitative Forecasting of Risk for PTSD Using Ecological Factors: A Deep Learning Application

Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning a large population possible. An exhaustive literature review was conducted to identify multiple ecological risk factors for PTSD. A questionnaire assessing these factors was designed and distributed among residents of southern Israel who have been exposed to terror attacks; data were collected from 1,290 residents. A neural network classification algorithm was used to predict the likelihood of a PTSD diagnosis. Assessed by cross-validation, the prediction of PTSD diagnostic status yielded a mean area under receiver operating characteristics curve of .91 (F score = .83). This study is a novel attempt to implement a neural network classification algorithm using ecological risk factors to predict potential risk for PTSD. Preexposure identification of future soldiers and other individuals at risk for PTSD from a large population of candidates is feasible using machine learning methods and readily collected ecological factors

Quantum disentanglers

It is not possible to disentangle a qubit in an unknown state $|\psi>$ from a set of (N-1) ancilla qubits prepared in a specific reference state $|0>$. That is, it is not possible to {\em perfectly} perform the transformation $(|\psi,0...,0\r +|0,\psi,...,0\r +...+ |0,0,...\psi\r) \to |0,...,0>\otimes |\psi>$. The question is then how well we can do? We consider a number of different methods of extracting an unknown state from an entangled state formed from that qubit and a set of ancilla qubits in an known state. Measuring the whole system is, as expected, the least effective method. We present various quantum ``devices'' which disentangle the unknown qubit from the set of ancilla qubits. In particular, we present the optimal universal disentangler which disentangles the unknown qubit with the fidelity which does not depend on the state of the qubit, and a probabilistic disentangler which performs the perfect disentangling transformation, but with a probability less than one.Comment: 8 pages, 1 eps figur

Fiscal-capacity equalization-grants with taxpayers' lobbying

The economic analysis of tax-base equalization-grants from central to local governments suggests that the transfer mechanism distorts fiscal policies by providing incentives to local governments to set excessively high tax rates. In this paper, we extend the analysis by allowing taxpayers to lobby the policy makers for reductions of their own tax burdens. In principle, the distortions spurring from the lobbying activity should mitigate those caused by the equalization program. In contrast, we show that taxpayers lobbying amplifi es the distortions of the equalization mechanism. The degree of fiscal equalization can then be adjusted to alleviate the efficiency costs of lobbying

Gaia DR2 view of the Lupus V-VI clouds: the candidate diskless young stellar objects are mainly background contaminants

Extensive surveys of star-forming regions with Spitzer have revealed populations of disk-bearing young stellar objects. These have provided crucial constraints, such as the timescale of dispersal of protoplanetary disks, obtained by carefully combining infrared data with spectroscopic or X-ray data. While observations in various regions agree with the general trend of decreasing disk fraction with age, the Lupus V and VI regions appeared to have been at odds, having an extremely low disk fraction. Here we show, using the recent Gaia data release 2 (DR2), that these extremely low disk fractions are actually due to a very high contamination by background giants. Out of the 83 candidate young stellar objects (YSOs) in these clouds observed by Gaia, only five have distances of 150 pc, similar to YSOs in the other Lupus clouds, and have similar proper motions to other members in this star-forming complex. Of these five targets, four have optically thick (Class II) disks. On the one hand, this result resolves the conundrum of the puzzling low disk fraction in these clouds, while, on the other hand, it further clarifies the need to confirm the Spitzer selected diskless population with other tracers, especially in regions at low galactic latitude like Lupus V and VI. The use of Gaia astrometry is now an independent and reliable way to further assess the membership of candidate YSOs in these, and potentially other, star-forming regions.Comment: Accepted for publication on Astronomy&Astrophysics Letter

Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations

Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity

Effect of bondcoat roughness on lifetime of APS-TBC systems in dry and wet gases

Low pressure plasma spraying (LPPS) is a process commonly used for deposition of MCrAlY (M=Ni,Co) bondcoats for air plasma spray thermal barrier coatings (APS-TBCs). LPPS produces bondcoats with a high roughness and good oxidation resistance, which are known to play a key role for long lifetimes of APS-TBC’s. An alternative process for the bondcoat deposition is high velocity oxy-fuel (HVOF), which is substantially cheaper than LPPS but even with well optimized spraying parameters generates intrinsically lower bondcoat roughness. In the present work it is shown that a bi-layer MCrAlY-bondcoat consisting of an HVOF-base layer and an upper, thin APS-flashcoat of the same chemical composition can provide cyclic oxidation TBC-lifetimes, which are similar to those obtained with well optimized LPPS bondcoats. The key points for the extended lifetime are the specific roughness profile and microstructure of the flashcoat, which allow good adhesion of the topcoat combined with an excellent oxidation resistance. Testing of the TBC-system with the APS-flashcoat in the atmosphere with increased amount of water vapour relevant for gas-turbine operation on alternative, hydrogen rich fuels revealed some lifetime shortening with respect to the drier test gas. However, even under these more aggressive conditions, the measured cyclic furnace lifetimes of samples with APS-flashcoat are a factor of 2 to 3 longer than those of the reference TBC-system with the state of the art HVOF bondcoat. Depending on the actually prevailing coating system and test conditions, the life times of the coatings were even longer than for coating systems which were completely manufactured using LPPS. In order to correlate the bondcoat roughness profile with the APS-TBC-lifetime an alternative method based on fractal analysis is proposed. Using this method, a more accurate description of complex bondcoat surface morphologies and a better correlation with the TBC-lifetime are obtained than with the commonly used mean roughness amplitude (Ra) approach