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We calculate the properties of a one-dimensional $Z_2$ lattice gauge theory in different Gauss law sectors, corresponding to different configurations of static charges set by the orientations of the gauge spins. Importantly, in quantum simulator experiments these sectors can be accessed without adding any additional physical particles or changing the Hamiltonian: The Gauss law sectors are simply set by the initial conditions. We study the interplay between conservation laws and interactions when the static charges are chosen to form periodic patterns. We classify the different Gauss law sectors and use the density matrix renormalization group to calculate the ground state compressibility, density profiles, charge density wave order parameters, and single particle correlation functions as a function of matter density. We find confined and deconfined phases, charge density waves, correlated insulators, and supersolids., Comment: 11 pages, 11 figures ; Minor revisions in response to referee comments and typo corrections

Hypothesis: Measuring rotational and translational Brownian motion of single spherical particles reveals dissipations due to the interaction between the particle and the environment. Experiments: In this article, we show experiments where the in-plane translational and two rotational drag coefficients of a single spherical Brownian particle can be measured. These particle drags are functions of the particle size and the particle-wall distance, and of the viscous dissipations at play. We measure drag coefficients for Janus particles close to a solid wall and close to a lipid bilayer membrane. Findings: For a particle close to wall, we show that according to hydrodynamic models, particlewall distance and particle size can be determined. For a particle partially wrapped by lipid membranes, in absence of strong binding interactions, translational and rotational drags are significantly larger than the ones of non-wrapped particles. Beside the effect of the membrane viscosity, we show that dissipations in the deformed membrane cap region strongly contribute to the drag coefficients.

We address the problem of constraint encoding explosion which hinders the applicability of state merging in symbolic execution. Specifically, our goal is to reduce the number of disjunctions and if-then-else expressions introduced during state merging. The main idea is to dynamically partition the symbolic states into merging groups according to a similar uniform structure detected in their path constraints, which allows to efficiently encode the merged path constraint and memory using quantifiers. To address the added complexity of solving quantified constraints, we propose a specialized solving procedure that reduces the solving time in many cases. Our evaluation shows that our approach can lead to significant performance gains.

In the past decades, automated high-content microscopy demonstrated its ability to deliver large quantities of image-based data powering the versatility of phenotypic drug screening and systems biology applications. However, as the sizes of image-based datasets grew, it became infeasible for humans to control, avoid and overcome the presence of imaging and sample preparation artefacts in the images. While novel techniques like machine learning and deep learning may address these shortcomings through generative image inpainting, when applied to sensitive research data this may come at the cost of undesired image manipulation. Undesired manipulation may be caused by phenomena such as neural hallucinations, to which some artificial neural networks are prone. To address this, here we evaluate the state-of-the-art inpainting methods for image restoration in a high-content fluorescence microscopy dataset of cultured cells with labelled nuclei. We show that architectures like DeepFill V2 and Edge Connect can faithfully restore microscopy images upon fine-tuning with relatively little data. Our results demonstrate that the area of the region to be restored is of higher importance than shape. Furthermore, to control for the quality of restoration, we propose a novel phenotype-preserving metric design strategy. In this strategy, the size and count of the restored biological phenotypes like cell nuclei are quantified to penalise undesirable manipulation. We argue that the design principles of our approach may also generalise to other applications., Comment: 8 pages, 3 figures, conference proceedings

(MnNiSi)1-(Fe2Ge)x composition (x=0.34) alloy was prepared by arc melting, crushed, and sieved to approximately <32 microns. They were utilized in examining the possible magnetic and structural changes when exposed to a dosage of a continuous sweeping rate of ~>120 Gy/min and an absorbed dose of 35 kGy of X-ray radiation. This study reports observable trends in X-ray diffraction and magnetic measurements. Magnetization in the magnetization vs. temperature (both heating and cooling) measurements showed an increase from 2.72 emu/g to 4.01 emu/g in the irradiated sample. The Magnetization vs. magnetic field loops exhibited irradiation-induced magnetic hysteresis. The irradiated sample also exhibited an observable change in its coercivity of {\Delta}Hc = 14.7% at 200 K. A maximum entropy change {\Delta}Smag of ~ 11.139 J/kg*K and a Tave peak of 317.5 K was achieved for the pristine sample in comparison to {\Delta}Smag of ~ 11.349 J/kg*K and a Tave peak of 312.5 K for the irradiated sample. These presented results provided deeper insights into tuning the effect of irradiation to the magnetic properties of (MnNiSi)1-(Fe2Ge)x for composition (x=0.34) that can be utilized in a wide range of magnetocaloric applications in high-energy radiation environments. The irradiation applied to the sample did not induce any structural or magnetic phase changes in the selected composition but modified the magnetic properties marginally.

We study a 2D measurement-only random circuit motivated by the Bacon-Shor error correcting code. We find a rich phase diagram as one varies the relative probabilities of measuring nearest neighbor Pauli XX and ZZ check operators. In the Bacon-Shor code, these checks commute with a group of stabilizer and logical operators, which therefore represent conserved quantities. Described as a subsystem symmetry, these conservation laws lead to a continuous phase transition between an X-basis and Z-basis spin glass order. The two phases are separated by a critical point where the entanglement entropy between two halves of an L X L system scales as L ln L, a logarithmic violation of the area law. We generalize to a model where the check operators break the subsystem symmetries (and the Bacon-Shor code structure). In tension with established heuristics, we find that the phase transition is replaced by a smooth crossover, and the X- and Z-basis spin glass orders spatially coexist. Additionally, if we approach the line of subsystem symmetries away from the critical point in the phase diagram, some spin glass order parameters jump discontinuously, Comment: 11 pages, 7 figures ; Minor revisions in response to referee comments and corrected typos

Entry of micro- or nano-sized objects into cells or vesicles made of lipid membranes occur in many processes such as entry of viruses in host cells, microplastics pollution, drug delivery or biomedical imaging. Here, we investigated the microparticle crossing of lipid membranes in giant unilamellar vesicles in the absence of strong binding interactions (e.g. streptavidin-biotin binding). In these conditions, we observed that organic and inorganic particles can always penetrate inside the vesicles provided that an external picoNewton force is applied and for relatively low membrane tensions. In the limit of a vanishing adhesion, we pointed out the role of the membrane area reservoir and show that a force minimum exists when the particle size is comparable to the bendocapillary length.

Fe$_{3}$Ga$_{4}$ displays a complex magnetic phase diagram that is sensitive and tunable with both electronic and crystallographic structure changes. In order to explore this tunability, vanadium-doped (Fe$_{1-x}$V$_{x}$)$_{3}$Ga$_{4}$ has been synthesized and characterized. High-resolution synchrotron X-ray diffraction and Rietveld refinement show that samples up to 20\% V-doping remain isostructural to Fe$_{3}$Ga$_{4}$ and display a linear increase in unit cell volume as doping is increased. Magnetic measurements reveal a suppression of the antiferromagnetic helical spin-density wave (SDW) with V-doping, revealed by changes in both the low-temperature ferromagnetic-antiferromagentic (FM-AFM) transition (T$_{1}$) and high-temperature AFM-FM transition (T$_{2}$). At 7.5\% V-doping, the metamagnetic behavior of the helical AFM SDW phase is no longer observed. These results offer an avenue to effective tuning of the magnetic order in Fe$_{3}$Ga$_{4}$ for devices, as well as increased understanding of the magnetism in this system.

Lowering costs by driving high utilization across deep learning workloads is a crucial lever for cloud providers. We present Singularity, Microsoft's globally distributed scheduling service for highly-efficient and reliable execution of deep learning training and inference workloads. At the heart of Singularity is a novel, workload-aware scheduler that can transparently preempt and elastically scale deep learning workloads to drive high utilization without impacting their correctness or performance, across a global fleet of AI accelerators (e.g., GPUs, FPGAs). All jobs in Singularity are preemptable, migratable, and dynamically resizable (elastic) by default: a live job can be dynamically and transparently (a) preempted and migrated to a different set of nodes, cluster, data center or a region and resumed exactly from the point where the execution was preempted, and (b) resized (i.e., elastically scaled-up/down) on a varying set of accelerators of a given type. Our mechanisms are transparent in that they do not require the user to make any changes to their code or require using any custom libraries that may limit flexibility. Additionally, our approach significantly improves the reliability of deep learning workloads. We show that the resulting efficiency and reliability gains with Singularity are achieved with negligible impact on the steady-state performance. Finally, our design approach is agnostic of DNN architectures and handles a variety of parallelism strategies (e.g., data/pipeline/model parallelism)., Comment: Revision: Fixed some typos

Motivated by recent experiments, we model the dynamics of a condensed Bose gas in a rotating anisotropic trap, where the equations of motion are analogous to those of charged particles in a magnetic field. As the rotation rate is ramped from zero to the trapping frequency, the condensate stretches along one direction and is squeezed along another, becoming long and thin. When the trap anisotropy is slowly switched off on a particular timescale, the condensate is left in the lowest Landau level. We use a time dependent variational approach to quantify these dynamics and give intuitive arguments about the structure of the condensate wavefunction. This preparation of a lowest Landau level condensate can be an important first step in realizing bosonic analogs of quantum Hall states., Comment: 8 pages, 5 figures; Minor revisions in response to referee comments and corrected typos

The effects of post-synthesis annealing temperature on arc-melted samples of Fe$_{3}$Ga$_{4}$ has been studied to investigate changes in crystallographic and magnetic properties induced by annealing. Results show a significant trend in the evolution of the (incommensurate spin density wave) ISDW-FM (ferromagnetic) transition temperature as a function of the refined unit cell volume in annealed samples. Strikingly, this trend allowed for the tuning of the transition temperature down to room-temperature (300 K) whilst maintaining a sharp transition in temperature, opening the door to the use of Fe$_{3}$Ga$_{4}$ in functional devices. Crystallographic analysis through Rietveld refinement of high-resolution x-ray diffraction data has showed that arc-melted stoichiometric Fe$_{3}$Ga$_{4}$ is multi-phase regardless of annealing temperature with a minor phase of FeGa$_{3}$ decreasing in phase fraction at higher annealing temperature. In order to validate the trend in ISDW-FM transition temperature with regard to unit cell volume, high pressure magnetometry was performed. This showed that the FM-ISDW ($\sim$ 68 K) and ISDW-FM ($\sim$ 360 K) transition temperatures could be tuned, increased and decreased respectively, linearly with external pressure. Thus, external pressure and the ensuing crystallographic changes minimize the temperature range of the stability of the ISDW pointing toward the importance of structural properties on the mechanism for the formation of the intermediate ISDW phase. These results show how this model system can be tuned as well as highlighting the need for future high-pressure crystallography and related single crystal measurements to understand the mechanism and nature of the intermediate ISDW phase to be exploited in future devices.

We present a sequence of driven-dissipative protocols for controlling cold atoms in tilted optical lattices. These experimentally accessible examples are templates that demonstrate how dissipation can be used to manipulate quantum many-body systems. We consider bosonic atoms trapped in a tilted optical lattice, immersed in a superfluid bath, and excited by coherent Raman lasers. With these ingredients, we are able to controllably transport atoms in the lattice and produce self-healing quantum states: a Mott insulator and the topologically ordered spin-1 AKLT state., Comment: Published version after response to referee comments

Existing simulations designed for cultural and interpersonal skill training rely on pre-defined responses with a menu option selection interface. Using a multiple-choice interface and restricting trainees' responses may limit the trainees' ability to apply the lessons in real life situations. This systems also uses a simplistic evaluation model, where trainees' selected options are marked as either correct or incorrect. This model may not capture sufficient information that could drive an adaptive feedback mechanism to improve trainees' cultural awareness. This paper describes the design of a dialogue-based simulation for cultural awareness training. The simulation, built around a disaster management scenario involving a joint coalition between the US and the Chinese armies. Trainees were able to engage in realistic dialogue with the Chinese agent. Their responses, at different points, get evaluated by different multi-label classification models. Based on training on our dataset, the models score the trainees' responses for cultural awareness in the Chinese culture. Trainees also get feedback that informs the cultural appropriateness of their responses. The result of this work showed the following; i) A feature-based evaluation model improves the design, modeling and computation of dialogue-based training simulation systems; ii) Output from current automatic speech recognition (ASR) systems gave comparable end results compared with the output from manual transcription; iii) A multi-label classification model trained as a cultural expert gave results which were comparable with scores assigned by human annotators.

Motivated by recent experiments, we explore the kinetics of Bose-Einstein condensation in the upper band of a double well optical lattice. These experiments engineer a non-equilibrium situation in which the highest energy state in the band is macroscopically occupied. The system subsequently relaxes and the condensate moves to the lowest energy state. We model this process, finding that the kinetics occurs in three phases: The condensate first evaporates, forming a highly non-equilibrium gas with no phase coherence. Energy is then redistributed among the noncondensed atoms. Finally the atoms recondense. We calculate the time-scales for each of these phases, and explain how this scenario can be verified through future experiments., Comment: 12 Pages, 4 Figures; Revisions in response to referee comments and corrected typographical errors

Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics, and industrial robotics. Failures of such systems can lead to loss of life or property, which necessitates stringent verification and validation for providing high assurance. Though formal verification approaches are being investigated, testing remains the primary technique for assessing the dependability of such systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining test oracle data---the expected output, a.k.a. label, for a given input---is high, which significantly impacts the amount and quality of testing that can be performed. Thus, prioritizing input data for testing DNNs in meaningful ways to reduce the cost of labeling can go a long way in increasing testing efficacy. This paper proposes using gauges of the DNN's sentiment derived from the computation performed by the model, as a means to identify inputs that are likely to reveal weaknesses. We empirically assessed the efficacy of three such sentiment measures for prioritization---confidence, uncertainty, and surprise---and compare their effectiveness in terms of their fault-revealing capability and retraining effectiveness. The results indicate that sentiment measures can effectively flag inputs that expose unacceptable DNN behavior. For MNIST models, the average percentage of inputs correctly flagged ranged from 88% to 94.8%.