Re-Opening Dallas: A Short-Term Evaluation of COVID-19 Regulations and Crime

We investigated the relationship between COVID-19 stay-at-home regulations and property and violent crime indexes in Dallas, TX during the first 6 months of 2020. We tested for changes in property and violent crime trends using four key “intervention” dates: the stay-at-home order issued by Judge Clay Jenkins (March 24), the start of Governor Abbott’s phase one of re-opening (May 1), a second phase of more widespread re-openings (May 18), and a third phase of increased capacity limits for businesses (June 3). Our analyses point to two main findings: (1) the time between the initial stay-at-home policy and the phase one re-opening was associated with an increase in the trend of both violent and property crime (although at lower levels than pre-pandemic); and (2) the third phase of re-opening the City of Dallas was associated with higher daily counts of property and violent crime. Our findings suggest that policy makers need to consider policies not only related to police enforcement but also allocation of other social services, particularly when such a sudden policy (e.g., stay-at-home order) is implemented.

Improving Security Using Modified S-Box for AES Cryptographic Primitives

The growing network traffic rate in wireless communication demands extended network capacity. Current crypto core methodologies are already reaching the maximum achievable network capacity limits. The combination of AES with other crypto cores and inventing new optimization models have emerged. In this paper, some of the prominent issues related to the existing AES core system, namely, lack of data rate, design complexity, reliability, and discriminative properties. In addition to that, this work also proposes a biometric key generation for AES core that constitutes simpler arithmetic such as substitution, modulo operation, and cyclic shifting for diffusion and confusion metrics which explore cipher transformation level. It is proved that in AES as compared to all other functions S-Box component directly influences the overall system performance both in terms of power consumption overhead, security measures, and path delay, etc.

Low Intrinsic Aerobic Capacity Limits Recovery Response to Hindlimb Ischemia

Introduction: In this study, we determined the influence of intrinsic exercise capacity on the vascular adaptive responses to hind limb ischemia. High Capacity Running, HCR; Low Capacity Running, LCR, rats were used to assess intrinsic aerobic capacity effects on adaptive responses to ischemia.Methods: Muscle samples from both ischemic and non-ischemic limb in both strains were compared, histologically for the muscle-capillary relationship, and functionally using microspheres to track blood flow and muscle stimulation to test fatigability. PCR was used to identify the differences in gene expression between the phenotypes following occlusive ischemia.Results: Prior to ligation, there were not significant differences between the phenotypes in the exhaustion time with high frequency pacing. Following ligation, LCR decreased significantly in the exhaustion time compare with HCRs (437 ± 47 vs. 824 ± 56, p < 0.001). The immediate decrease in flow was significantly more severe in LCRs than HCRs (52.5 vs. 37.8%, p < 0.001). VEGF, eNOS, and ANG2 (but not ANG1) gene expression were decreased in LCRs vs. HCRs before occlusion, and increased significantly in LCRs 14D after occlusion, but not in HCRs. LCR capillary density (CD) was significantly lower at all time points after occlusion (LCR 7D = 564.76 ± 40.5, LCR 14D = 507.48 ± 54.2, both p < 0.05 vs. HCR for respective time point). NCAF increased significantly in HCR and LCR in response to ischemia.Summary: These results suggest that LCR confers increased risk for ischemic injury and is subject to delayed and less effective adaptive response to ischemic stress.

Almost Linear Time Algorithms for Some Problems on Dynamic Flow Networks

Motivated by evacuation planning, several problems regarding dynamic flow networks have been studied in recent years. A dynamic flow network consists of an undirected graph with positive edge lengths, positive edge capacities, and positive vertex weights. The road network in an area can be treated as a graph where the edge lengths are the distances along the roads and the vertex weights are the number of people at each site. An edge capacity limits the number of people that can enter the edge per unit time. In a dynamic flow network, when particular points on edges or vertices called sinks are given, all of the people are required to evacuate from the vertices to the sinks as quickly as possible. This chapter gives an overview of two of our recent results on the problem of locating multiple sinks in a dynamic flow path network such that the max/sum of evacuation times for all the people to sinks is minimized, and we focus on techniques that enable the problems to be solved in almost linear time.

No Coincidence, George: Capacity-Limits as the Curse of Compositionality

In one of the most influential articles in cognitive science, George Miller (1956) describes his “persecution” by similarities in three cognitive capacity-limits: the number of items that can be held in short-term memory, the number of stimuli that can be ordinally ranked, and the number of items in a visual display that can be quickly and accurately reported. Although Miller wondered whether these limits owe to a common source, he ultimately concluded that the likeness was coincidental. Here, we challenge that conclusion. Cognitive systems face a tradeoff between maximizing the number of possibilities they can represent (maximizing entropy), and precisely fitting the data observed thus far (minimizing energy). Equipping a cognitive system with an inductive bias to maximize entropy on different timescales enables one of the hallmarks of cognitive function— efficient generalization— but leads to the limits in information processing that haunted Miller.

On Models and Capacity Bounds for DNA-based Storage Channels

In recent years, DNA-based systems have become a promising medium for long-term data storage. There are two layers of errors in DNA-based storage systems. The first is the dropouts of the DNA strands, which has been characterized in the shuffling-sampling channel. The second is insertions, deletions, and substitutions of nucleotides in individual DNA molecules. In this paper, we describe a DNA noisy synchronization error channel to characterize the errors in individual DNA molecules. We derive non-trivial lower and upper capacity bounds of the DNA noisy synchronization error channel based on information theory. By cascading these two channels, we provide theoretical capacity limits of the DNA storage system. These results reaffirm that DNA is a reliable storage medium with high storage density potential.

On Models and Capacity Bounds for DNA-based Storage Channels

In recent years, DNA-based systems have become a promising medium for long-term data storage. There are two layers of errors in DNA-based storage systems. The first is the dropouts of the DNA strands, which has been characterized in the shuffling-sampling channel. The second is insertions, deletions, and substitutions of nucleotides in individual DNA molecules. In this paper, we describe a DNA noisy synchronization error channel to characterize the errors in individual DNA molecules. We derive non-trivial lower and upper capacity bounds of the DNA noisy synchronization error channel based on information theory. By cascading these two channels, we provide theoretical capacity limits of the DNA storage system. These results reaffirm that DNA is a reliable storage medium with high storage density potential.