Mattis and Pan reply

Journal ArticleAfter several independent calculations failed to confirm our published1 numbers on the ground-state energy of the s = 1/2 antiferromagnet in two dimensions, we checked our computer programs and found some deplorable errors introduced in proceeding from one dimension to two

Ground-state energy of Heisenberg antiferromagnet for spins s=1/2 and s=1 in d=1 and 2 dimensions

Journal ArticleA simple real-space renormalization method yields the ground-state energy of the Heisenberg antiferromagnet. We find the ground-state energy per spin for s=1/2 (-0.4438 in ID , -0.6723 in 2D ) and s = 1 (-1.388 in ID and -1.907 in 2D) to three-figure accuracy, using properties of relatively small odd-numbered clusters. Our results provide reasonable proof for long-range order in the ground state of the s = 1/2 Heisenberg antiferromagnet in 2D

Weakly supervised deep learning for the detection of domain generation algorithms

Domain generation algorithms (DGAs) have become commonplace in malware that seeks to establish command and control communication between an infected machine and the botmaster. DGAs dynamically and consistently generate large volumes of malicious domain names, only a few of which are registered by the botmaster, within a short time window around their generation time, and subsequently resolved when the malware on the infected machine tries to access them. Deep neural networks that can classify domain names as benign or malicious are of great interest in the real-time defense against DGAs. In contrast with traditional machine learning models, deep networks do not rely on human engineered features. Instead, they can learn features automatically from data, provided that they are supplied with sufficiently large amounts of suitable training data. Obtaining cleanly labeled ground truth data is difficult and time consuming. Heuristically labeled data could potentially provide a source of training data for weakly supervised training of DGA detectors. We propose a set of heuristics for automatically labeling domain names monitored in real traffic, and then train and evaluate classifiers with the proposed heuristically labeled dataset. We show through experiments on a dataset with 50 million domain names that such heuristically labeled data is very useful in practice to improve the predictive accuracy of deep learning-based DGA classifiers, and that these deep neural networks significantly outperform a random forest classifier with human engineered features

Dialkylcarbamoyl Chloride Dressings in the Prevention of Surgical Site Infections after Nonimplant Vascular Surgery

© 2017 Elsevier Inc. Background Dressings coated with dialkylcarbamoyl chloride (DACC) are highly hydrophobic and irreversibly bind multiple types of bacteria, trapping them in the dressing and reducing the number of organisms at the wound surface. We aimed to assess the impact of DACC-coated postoperative dressings on the incidence of surgical site infection (SSI) in nonimplant vascular surgery patients. Methods Two hundred patients undergoing nonimplant vascular surgery were prospectively recruited at a single vascular center. The initial 100 patients had their operative wounds dressed with conventional dressings followed by 100 patients who received DACC-coated postoperative dressings. Wounds were reviewed at day 5 and day 30 to determine the presence of SSI using the ASEPSIS scoring system. The variation in outcomes between groups was assessed using chi-squared test and logistic regression analysis to assess the effects of other variables, which may affect healing. Results Between August 1, 2015 and February 29, 2016, a total of 120 men and 80 women were recruited. The mean age was 63 (range 27–97) years, 92% were current or ex-smokers and 45.5% were diabetic. Rate of SSI at 5 days was significantly lower in the DACC group compared with standard dressings (1% vs. 10%, P < 0.05). There was no difference in the rates of SSI at 30 days. Logistic regression suggested that the type of dressing used was the most prominent predictor variable for the presence of early SSI (P = 0.028, odds ratio = 0.09, 95% confidence interval: 0.01–0.77). Conclusions DACC-coated dressings were associated with a significant reduction in SSI rates in the early postoperative period

Contribution of mixing to upward transport across the tropical tropopause layer (TTL)

During the second part of the TROCCINOX campaign that took place in Brazil in early 2005, chemical species were measured on-board the high-altitude research aircraft Geophysica (ozone, water vapor, NO, NOy, CH4 and CO) in the altitude range up to 20 km (or up to 450 K potential temperature), i.e. spanning the entire TTL region roughly extending between 350 and 420 K. Here, analysis of transport across the TTL is performed using a new version of the Chemical Lagrangian Model of the Stratosphere (CLaMS). In this new version, the stratospheric model has been extended to the earth surface. Above the tropopause, the isentropic and cross-isentropic advection in CLaMS is driven by meteorological analysis winds and heating/cooling rates derived from a radiation calculation. Below the tropopause, the model smoothly transforms from the isentropic to the hybrid-pressure coordinate and, in this way, takes into account the effect of large-scale convective transport as implemented in the vertical wind of the meteorological analysis. As in previous CLaMS simulations, the irreversible transport, i.e. mixing, is controlled by the local horizontal strain and vertical shear rates. Stratospheric and tropospheric signatures in the TTL can be seen both in the observations and in the model. The composition of air above &#8776;350 K is mainly controlled by mixing on a time scale of weeks or even months. Based on CLaMS transport studies where mixing can be completely switched off, we deduce that vertical mixing, mainly driven by the vertical shear in the tropical flanks of the subtropical jets and, to some extent, in the the outflow regions of the large-scale convection, offers an explanation for the upward transport of trace species from the main convective outflow at around 350 K up to the tropical tropopause around 380 K

Contribution of mixing to the upward transport across the TTL

During the second part of the TROCCINOX campaign that took place in Brazil in early 2005, chemical species were measured on-board of the high altitude research aircraft Geophysica (ozone, water vapor, NO, NOy, CH4 and CO) in the altitude range up to 20 km (or up to 450 K potential temperature), i.e. spanning the TTL region roughly extending between 350 and 420 K. Analysis of transport across TTL is performed using a new version of the Chemical Lagrangian Model of the Stratosphere (CLaMS). In this new version, the stratospheric model has been extended to the earth surface. Above the tropopause, the isentropic and cross-isentropic advection in CLaMS is driven by ECMWF winds and heating/cooling rates derived from a radiation calculation. Below the tropopause the model smoothly transforms from the isentropic to hybrid-pressure coordinate and, in this way, takes into account the effect of large-scale convective transport as implemented in the ECMWF vertical wind. As with other CLaMS simulations, the irreversible transport, i.e. mixing, is controlled by the local horizontal strain and vertical shear rates. Stratospheric and tropospheric signatures in the TTL can be seen both in the observation and in the model. The composition of air above ≈350 K is mainly controlled by mixing on a time scale of weeks or even months. Based on CLaMS transport studies where mixing can be completely switched off, we deduce that vertical mixing, mainly driven by the vertical shear in the outflow regions of the large-scale convection and in the vicinity of the subtropical jets, is necessary to understand the upward transport of the tropospheric air from the main convective outflow around 350 K up to the tropical tropopause around 380 K. This mechanism is most effective if the outflow of the mesoscale convective systems interacts with the subtropical jets

Contribution of mixing to the upward transport across the TTL

During the second part of the TROCCINOX campaign that took place in Brazil in early 2005, chemical species were measured on-board of the high altitude research aircraft Geophysica (ozone, water vapor, NO, NOy, CH4 and CO) in the altitude range up to 20 km (or up to 450 K potential temperature), i.e. spanning the TTL region roughly extending between 350 and 420 K. Analysis of transport across TTL is performed using a new version of the Chemical Lagrangian Model of the Stratosphere (CLaMS). In this new version, the stratospheric model has been extended to the earth surface. Above the tropopause, the isentropic and cross-isentropic advection in CLaMS is driven by ECMWF winds and heating/cooling rates derived from a radiation calculation. Below the tropopause the model smoothly transforms from the isentropic to hybrid-pressure coordinate and, in this way, takes into account the effect of large-scale convective transport as implemented in the ECMWF vertical wind. As with other CLaMS simulations, the irreversible transport, i.e. mixing, is controlled by the local horizontal strain and vertical shear rates. Stratospheric and tropospheric signatures in the TTL can be seen both in the observation and in the model. The composition of air above ≈350 K is mainly controlled by mixing on a time scale of weeks or even months. Based on CLaMS transport studies where mixing can be completely switched off, we deduce that vertical mixing, mainly driven by the vertical shear in the outflow regions of the large-scale convection and in the vicinity of the subtropical jets, is necessary to understand the upward transport of the tropospheric air from the main convective outflow around 350 K up to the tropical tropopause around 380 K. This mechanism is most effective if the outflow of the mesoscale convective systems interacts with the subtropical jets

An experimental observation of geometric phases for mixed states using NMR interferometry

Examples of geometric phases abound in many areas of physics. They offer both fundamental insights into many physical phenomena and lead to interesting practical implementations. One of them, as indicated recently, might be an inherently fault-tolerant quantum computation. This, however, requires to deal with geometric phases in the presence of noise and interactions between different physical subsystems. Despite the wealth of literature on the subject of geometric phases very little is known about this very important case. Here we report the first experimental study of geometric phases for mixed quantum states. We show how different they are from the well understood, noiseless, pure-state case.Comment: 4 pages, 3 figure

Dissipative preparation of W states in trapped ion systems

We present protocols for dissipative entanglement of three trapped-ion qubits and discuss a scheme that uses sympathetic cooling as the dissipation mechanism. This scheme relies on tailored destructive interference to generate any one of six entangled W states in a three-ion qubit space. Using a beryllium-magnesium ion crystal as an example system, we theoretically investigate the protocol's performance and the effects of likely error sources, including thermal secular motion of the ion crystal, calibration imperfections, and spontaneous photon scattering. We estimate that a fidelity of $\sim$ 98 % may be achieved in typical trapped ion experiments with $\sim$ 1 ms interaction time. These protocols avoid timescale hierarchies for faster preparation of entangled states