Phase locking below rate threshold in noisy model neurons

The property of a neuron to phase-lock to an oscillatory stimulus before adapting its spike rate to the stimulus frequency plays an important role for the auditory system. We investigate under which conditions neurons exhibit this phase locking below rate threshold. To this end, we simulate neurons employing the widely used leaky integrate-and-fire (LIF) model. Tuning parameters, we can arrange either an irregular spontaneous or a tonic spiking mode. When the neuron is stimulated in both modes, a significant rise of vector strength prior to a noticeable change of the spike rate can be observed. Combining analytic reasoning with numerical simulations, we trace this observation back to a modulation of interspike intervals, which itself requires spikes to be only loosely coupled. We test the limits of this conception by simulating an LIF model with threshold fatigue, which generates pronounced anticorrelations between subsequent interspike intervals. In addition we evaluate the LIF response for harmonic stimuli of various frequencies and discuss the extension to more complex stimuli. It seems that phase locking below rate threshold occurs generically for all zero mean stimuli. Finally, we discuss our findings in the context of stimulus detection

Development of a lightweight centralized authentication mechanism for the internet of things driven by fog

The rapid development of technology has made the Internet of Things an integral element of modern society. Modern Internet of Things’ implementations often use Fog computing, an offshoot of the Cloud computing that offers localized processing power at the network’s periphery. The Internet of Things serves as the inspiration for the decentralized solution known as Fog computing. Features such as distributed computing, low latency, location awareness, on-premise installation, and support for heterogeneous hardware are all facilitated by Fog computing. End-to-end security in the Internet of Things is challenging due to the wide variety of use cases and the disparate resource availability of participating entities. Due to their limited resources, it is out of the question to use complex cryptographic algorithms for this class of devices. All Internet of Things devices, even those connected to servers online, have constrained resources such as power and processing speed, so they would rather not deal with strict security measures. This paper initially examines distributed Fog computing and creates a new authentication framework to support the Internet of Things environment. The following authentication architecture is recommended for various Internet of Things applications, such as healthcare systems, transportation systems, smart buildings, smart energy, etc. The total effectiveness of the method is measured by considering factors such as the cost of communication and the storage overhead incurred by the offered integrated authentication protocol. It has been proven that the proposed technique will reduce communication costs by at least 11%

A New Strong Adversary Model for RFID Authentication Protocols

Radio Frequency Identification (RFID) systems represent a key technology for ubiquitous computing and for the deployment of the Internet of Things (IoT). In RFID technology, authentication protocols are often necessary in order to confirm the identity of the parties involved (i.e. RFID readers, RFID tags and/or database servers). In this article, we analyze the security of a mutual authentication protocol proposed by Wang and Ma. Our security analysis clearly shows major security pitfalls in this protocol. Firstly, we show two approaches that an adversary may use to mislead an honest reader into thinking that it is communicating with a legitimate database. Secondly, we show how an adversary that has compromised some tags can impersonate an RFID reader to a legitimate database. Furthermore, we present a new adversary model, which pays heed on cases missed by previous proposals. In contrast to previous models where the communication between an RFID reader and a back-end server is through a secure channel, our model facilitates the security analysis of more general schemes where this communication channel (RFID reader-to-server) is insecure. This model determines whether the compromise of RFID tags has any impact on the security of the readerto-server communication or vice versa. In a secure protocol, the possible compromise of RFID tags should not affect the RFID reader-server communication. In this paper, we show that compromising of RFID tags in Wang and Ma protocol has a direct impact on the reader-server security. Finally, we propose a new authentication protocol that offers an adequate security level and is resistant against the mentioned security risks. The security proofs of the proposed protocol are supported with Gong-Needham-Yahalom (GNY) logic and Scyther tool, which are formal methods to evaluate the security of a cryptographic protocol

Task scheduling mechanisms for fog computing: A systematic survey

In the Internet of Things (IoT) ecosystem, some processing is done near data production sites at higher speeds without the need for high bandwidth by combining Fog Computing (FC) and cloud computing. Fog computing offers advantages for real-time systems that require high speed internet connectivity. Due to the limited resources of fog nodes, one of the most important challenges of FC is to meet dynamic needs in real-time. Therefore, one of the issues in the fog environment is the optimal assignment of tasks to fog nodes. An efficient scheduling algorithm should reduce various qualitative parameters such as cost and energy consumption, taking into account the heterogeneity of fog nodes and the commitment to perform tasks within their deadlines. This study provides a detailed taxonomy to gain a better understanding of the research issues and distinguishes important challenges in existing work. Therefore, a systematic overview of existing task scheduling techniques for cloud-fog environment, as well as their benefits and drawbacks, is presented in this article. Four main categories are introduced to study these techniques, including machine learning-based, heuristic-based, metaheuristic-based, and deterministic mechanisms. A number of papers are studied in each category. This survey also compares different task scheduling techniques in terms of execution time, resource utilization, delay, network bandwidth, energy consumption, execution deadline, response time, cost, uncertainty, and complexity. The outcomes revealed that 38% of the scheduling algorithms use metaheuristic-based mechanisms, 30% use heuristic-based, 23% use machine learning algorithms, and the other 9% use deterministic methods. The energy consumption is the most significant parameter addressed in most articles with a share of 19%. Finally, a number of important areas for improving the task scheduling methods in the FC in the future are presented

Selection Criteria for Drug-Eluting Versus Bare-Metal Stents and the Impact of Routine Angiographic Follow-Up 2-Year Insights From the HORIZONS-AMI (Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction) Trial

ObjectivesWe sought to identify patients with ST-segment elevation myocardial infarction most likely to benefit from drug-eluting stents (DES), and to evaluate the impact of routine angiographic follow-up on the apparent differences between stent types.BackgroundDES might have greatest utility in patients who would benefit most from their antirestenotic properties.MethodsWe randomly assigned 3,006 patients with ST-segment elevation myocardial infarction to paclitaxel-eluting stents (PES) or to bare-metal stents (BMS). Events were assessed at 12 months and 24 months, with a subset undergoing routine angiographic follow-up at 13 months. Using well-known risk factors for restenosis and target lesion revascularization (TLR), risk groups were formed to examine the absolute differences between PES and BMS.ResultsCompared with BMS, PES reduced TLR at 12 months from 7.4% to 4.5% (p = 0.003). Insulin-treated diabetes mellitus (hazard ratio: 3.12), reference vessel diameter ≤3.0 mm (hazard ratio: 2.89), and lesion length ≥30 mm (hazard ratio: 2.49) were independent predictors of 12-month TLR after BMS. In patients with 2 or 3 of these baseline risk factors, PES compared with BMS markedly reduced 12-month TLR (19.8% vs. 8.1%, p = 0.003). In patients with 1 of these risk factors, the 12-month rates of TLR were modestly reduced by PES (7.3% vs. 4.3%, p = 0.02). The 12-month TLR rates were low and similar for both stents in patients with 0 risk factors (3.3% vs. 3.2%, p = 0.93). Routine 13-month angiographic follow-up resulted in a marked increase in TLR procedures (more so with BMS) so that the absolute incremental benefit of PES compared with BMS doubled from 2.9% at 12 months to 6.0% at 24 months, a difference evident in all risk strata.ConclusionsPatients at high risk for TLR after BMS in ST-segment elevation myocardial infarction for whom DES are of greatest benefit may be identified. Conversely, DES may be of less clinical benefit for patients at lower risk for TLR after BMS. Routine angiographic follow-up increases the perceived clinical benefits of DES, and must be avoided to accurately estimate absolute treatment effects. (Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction [HORIZONS-AMI]; NCT00433966

Clinical Outcomes Following Stent Thrombosis Occurring In-Hospital Versus Out-of-Hospital Results From the HORIZONS-AMI (Harmonizing Outcomes with Revascularization and Stents in Acute Myocardial Infarction) Trial

ObjectivesThe study sought to determine whether rapid access to medical care and reperfusion results in a better prognosis in patients with in-hospital compared with out-of-hospital stent thrombosis (ST) in patients with ST-segment elevation myocardial infarction (STEMI) in the HORIZONS-AMI (Harmonizing Outcomes with Revascularization and Stents in Acute Myocardial Infarction) trial.BackgroundWhether the prognosis of in-hospital and out-of-hospital ST are similar is uncertain, with conflicting data reported from prior studies.MethodsA total of 3,602 STEMI patients undergoing primary percutaneous coronary intervention (PCI) were randomized to bivalirudin (n = 1,800) versus unfractionated heparin (UFH) plus a glycoprotein IIb/IIIa inhibitor (GPI) (UFH+GPI; n = 1,802). Stents were implanted in 3,202 patients, 156 (4.9%) of whom developed Academic Research Consortium definite/probable ST during 3-year follow-up. We investigated the 1-year clinical outcomes after ST in 54 patients with in-hospital ST compared with 102 patients with out-of-hospital ST.ResultsOne year after the ST event, patients with in-hospital compared with out-of-hospital ST had significantly greater mortality (27.8% vs. 10.8%, p < 0.01); most deaths in both groups occurred within 1 week of the ST event. Patients with in-hospital ST also had higher rates of major bleeding (21.2% vs. 6.0%, p < 0.01), but a lower rate of myocardial infarction (56.6% vs. 77.5%, p < 0.01). Subgroup analysis within both in-hospital and out-of-hospital ST groups indicated that subacute ST had the highest mortality. By multivariable analysis, 1-year mortality was significantly increased in patients with in-hospital compared with out-of-hospital ST (adjusted hazard ratio: 4.62, 95% confidence interval: 1.98 to 10.77, p < 0.01). Additional correlates of increased mortality after an ST event included diabetes and randomization to UFH+GPI (vs. bivalirudin).ConclusionsFollowing primary PCI for STEMI, more than one-third of all ST events during 3-year follow-up occurred during the index hospital phase. Mortality and major bleeding were significantly higher after in-hospital ST compared with out-of-hospital ST. (Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction; NCT00433966

Deep Learning-Based Intrusion Detection Systems: A Systematic Review

Nowadays, the ever-increasing complication and severity of security attacks on computer networks have inspired security researchers to incorporate different machine learning methods to protect the organizations’ data and reputation. Deep learning is one of the exciting techniques which recently are vastly employed by the IDS or intrusion detection systems to increase their performance in securing the computer networks and hosts. This survey article focuses on the deep learning-based intrusion detection schemes and puts forward an in-depth survey and classification of these schemes. It first presents the primary background concepts about IDS architecture and various deep learning techniques. It then classifies these schemes according to the type of deep learning methods utilized in each of them. It describes how deep learning networks are utilized in the intrusion detection process to recognize intrusions accurately. Finally, a complete analysis of the investigated IDS frameworks is provided, and concluding remarks and future directions are highlighted

Multiple impact therapy : evaluation and design for future study

The theoretical underpinnings of Washington County Children\u27s Services Division (CSD) Immediate Conflict-Resolution Family Treatment Program include the systems theory of family therapy with a focus on communication and roles. One of the many approaches to helping families in crisis, it incorporates theories regarding assessment of and intervention in families in crisis. Finally, while it draws upon several different approaches to family therapy, the Washington County program is most closely related to Multiple Impact Therapy (MIT). Thus, a review of relevant literature must address portions of the above enumerated theories that illuminate the thinking behind the Immediate Conflict- Resolution Family Treatment Program. While each of the four components of the literature review (systems theory, family crisis theory, assessment of families in crisis, and Multiple Impact Therapy) represents a topic area of breadth and complexity, the aspects of each topic area which seem most relevant to Washington County\u27s MIT project have been reviewed

Application of the Convection–Dispersion Equation to Modelling Oral Drug Absorption

Models of systemic drug absorption after oral administration are frequently based on a direct or a delayed first-order rate process. In practice, the use of the first-order approach to predict drug concentrations in blood plasma frequently yields a considerable mismatch between predicted and measured concentration profiles. This is particularly true for the upswing of the plasma concentration after oral administration. The current investigation explores an alternative model to describe the absorption rate based on the convection–dispersion equation describing the transport of chemicals through the GI tract. This equation is governed by two parameters, transport velocity and dispersion coefficient. One solution of this equation for a specific set of initial and boundary conditions was used to model absorption of paracetamol in a 22-year-old man after oral administration. The GI-tract passage rate in this subject was influenced by co-administration of drugs that stimulate or delay gastric emptying. The transport-limited absorption function is more accurate in describing the plasma concentration versus time curve after oral administration than the first-order model. Additionally, it provides a mechanistic explanation for the observed curve through the differences in GI-tract passage rate

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License