Multi-qubit correction for quantum annealers.

We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels-i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value-and successively converges to the ground state. Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as spin-reversal transforms, classical postprocessing techniques, and increased inter-sample delay between successive measurements.

Understanding Cybersecurity Threat Trends Through Dynamic Topic Modeling.

Cybersecurity threats continue to increase and are impacting almost all aspects of modern life. Being aware of how vulnerabilities and their exploits are changing gives helpful insights into combating new threats. Applying dynamic topic modeling to a time-stamped cybersecurity document collection shows how the significance and details of concepts found in them are evolving. We correlate two different temporal corpora, one with reports about specific exploits and the other with research-oriented papers on cybersecurity vulnerabilities and threats. We represent the documents, concepts, and dynamic topic modeling data in a semantic knowledge graph to support integration, inference, and discovery. A critical insight into discovering knowledge through topic modeling is seeding the knowledge graph with domain concepts to guide the modeling process. We use Wikipedia concepts to provide a basis for performing concept phrase extraction and show how using those phrases improves the quality of the topic models. Researchers can query the resulting knowledge graph to reveal important relations and trends. This work is novel because it uses topics as a bridge to relate documents across corpora over time.

Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata.

We introduce the notion of reinforcement quantum annealing (RQA) scheme in which an intelligent agent searches in the space of Hamiltonians and interacts with a quantum annealer that plays the stochastic environment role of learning automata. At each iteration of RQA, after analyzing results (samples) from the previous iteration, the agent adjusts the penalty of unsatisfied constraints and re-casts the given problem to a new Ising Hamiltonian. As a proof-of-concept, we propose a novel approach for casting the problem of Boolean satisfiability (SAT) to Ising Hamiltonians and show how to apply the RQA for increasing the probability of finding the global optimum. Our experimental results on two different benchmark SAT problems (namely factoring pseudo-prime numbers and random SAT with phase transitions), using a D-Wave 2000Q quantum processor, demonstrated that RQA finds notably better solutions with fewer samples, compared to the best-known techniques in the realm of quantum annealing.

Canonical correlation analysis of brain prefrontal activity measured by functional near infra-red spectroscopy (fNIRS) during a moral judgment task.

Individuals differ in the extent to which they make decisions in different moral dilemmas. In this study, we investigated the relationship between functional brain activities during moral decision making and psychopathic personality traits in a healthy population. We measured the hemodynamic activities of the brain by functional near-infrared spectroscopy (fNIRS). FNIRS is an evolving non-invasive neuroimaging modality which is relatively inexpensive, patient friendly and robust to subject movement. Psychopathic traits were evaluated through a self-report questionnaire called the Psychopathic Personality Inventory Revised (PPI-R). We recorded functional brain activities of 30 healthy subjects while they performed a moral judgment (MJ) task. Regularized canonical correlation analysis (R-CCA) was applied to find the relationships between activation in different regions of prefrontal cortex (PFC) and the core psychopathic traits. Our results showed a significant canonical correlation between PFC activation and PPI-R content scale (PPI-R-CS). Specifically, coldheartedness and carefree non-planfulness were the only PPI-R-CS factors that were highly correlated with PFC activation during personal (emotionally salient) MJ, while Machiavellian egocentricity, rebellious nonconformity, coldheartedness, and carefree non-planfulness were the core traits that exhibited the same dynamics as PFC activation during impersonal (more logical) MJ. Furthermore, ventromedial prefrontal cortex (vmPFC) and left lateral PFC were the most positively correlated regions with PPI-R-CS traits during personal MJ, and the right vmPFC and right lateral PFC in impersonal MJ.

The role of prefrontal cortex in a moral judgment task using functional near-infrared spectroscopy.

BACKGROUND: Understanding the neural basis of moral judgment (MJ) and human decision-making has been the subject of numerous studies because of their impact on daily life activities and social norms. Here, we aimed to investigate the neural process of MJ using functional near-infrared spectroscopy (fNIRS), a noninvasive, portable, and affordable neuroimaging modality. METHODS: We examined prefrontal cortex (PFC) activation in 33 healthy participants engaging in MJ exercises. We hypothesized that participants presented with personal (emotionally salient) and impersonal (less emotional) dilemmas would exhibit different brain activation observable through fNIRS. We also investigated the effects of utilitarian and nonutilitarian responses to MJ scenarios on PFC activation. Utilitarian responses are those that favor the greatest good while nonutilitarian responses favor moral actions. Mixed effect models were applied to model the cerebral hemodynamic changes that occurred during MJ dilemmas. RESULTS AND CONCLUSIONS: Our analysis found significant differences in PFC activation during personal versus impersonal dilemmas. Specifically, the left dorsolateral PFC was highly activated during impersonal MJ when a nonutilitarian decision was made. This is consistent with the majority of relevant fMRI studies, and demonstrates the feasibility of using fNIRS, with its portable and motion tolerant capacities, to investigate the neural basis of MJ dilemmas.

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows.

Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) improves programmer productivity when implementing hybrid workflows for multi-core and multi-GPU systems. The Hybrid Task Graph Scheduler (HTGS) is an abstract execution model, framework, and API that increases programmer productivity when implementing hybrid workflows for such systems. HTGS manages dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. Through these abstractions, data motion and memory are explicit; this makes data locality decisions more accessible. To demonstrate the HTGS application program interface (API), we present implementations of two example algorithms: (1) a matrix multiplication that shows how easily task graphs can be used; and (2) a hybrid implementation of microscopy image stitching that reduces code size by ≈ 43% compared to a manually coded hybrid workflow implementation and showcases the minimal overhead of task graphs in HTGS. Both of the HTGS-based implementations show good performance. In image stitching the HTGS implementation achieves similar performance to the hybrid workflow implementation. Matrix multiplication with HTGS achieves 1.3× and 1.8× speedup over the multi-threaded OpenBLAS library for 16k × 16k and 32k × 32k size matrices, respectively.