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Autonomous agents operating around human actors must consider how their behaviors might affect those humans, even when not directly interacting with them. To this end, it is often beneficial to be predictable and appear naturalistic. Existing methods to address this problem use human actor intent modeling or imitation learning techniques, but these approaches rarely capture all possible motivations for human behavior or require significant amounts of data. In contrast, we propose a technique for modeling naturalistic behavior as a set of convex hulls computed over a relatively small dataset of human behavior. Given this set, we design an optimization-based filter which projects arbitrary trajectories into it to make them more naturalistic for autonomous agents to execute while also satisfying dynamics constraints. We demonstrate our methods on real-world human driving data from the inD intersection dataset (Bock et al., 2020).

The high photoluminescence efficiency, color purity, extended gamut, and solution processability make low-dimensional hybrid perovskites attractive for light-emitting diode (PeLED) applications. However, controlling the microstructure of these materials to improve the device performance remains challenging. Here, the development of highly efficient green PeLEDs based on blends of the quasi-2D (q2D) perovskite, PEA2Cs4Pb5Br16, and the wide bandgap organic semiconductor 2,7 dioctyl[1] benzothieno[3,2-b]benzothiophene (C8-BTBT) is reported. The presence of C8-BTBT enables the formation of single-crystal-like q2D PEA2Cs4Pb5Br16 domains that are uniform and highly luminescent. Combining the PEA2Cs4Pb5Br16:C8-BTBT with self-assembled monolayers (SAMs) as hole-injecting layers (HILs), yields green PeLEDs with greatly enhanced performance characteristics, including external quantum efficiency up to 18.6%, current efficiency up to 46.3 cd/A, the luminance of 45 276 cd m^-2, and improved operational stability compared to neat PeLEDs. The enhanced performance originates from multiple synergistic effects, including enhanced hole-injection enabled by the SAM HILs, the single crystal-like quality of the perovskite phase, and the reduced concentration of electronic defects. This work highlights perovskite:organic blends as promising systems for use in LEDs, while the use of SAM HILs creates new opportunities toward simpler and more stable PeLEDs.

2D Ruddlesden - Popper perovskites are promising candidates for energy harvesting applications due to their tunable optical properties and excellent ambient stability. Moreover, they are solution-processable and compatible with upscalable manufacturing via various printing techniques. Unfortunately, such methods often induce large degrees of heterogeneity due to poorly controlled crystallization. Here, we address this issue by blending the well-known 2D perovskite (PEA)2PbBr4 with an organic small-molecule, namely C8-BTBT, employed as an additive with different blending ratios. Using terahertz (THz) absorption and temperature-dependent photoluminescence (PL) spectroscopy techniques we observe that with the C8-BTBT additive the photophysical properties are altered while the perovskite structure in the film remains unaffected. More precisely, the inclusion of trace amounts of C8-BTBT in the hybrid films results in defect passivation at perovskite platelet boundaries and at the surfaces, as indicated by increased carrier lifetimes and substantially increased photoluminescence quantum yields (PLQY). This in turn improves the responsivity of photodetectors using the 2D perovskite as active layer. Our study highlights a straightforward strategy for fabricating high-quality 2D perovskites via large-area processing techniques.

This paper presents ZePoP, a leader election protocol for distributed systems, optimizing a delay-based closeness centrality. We design the protocol specifically for the Peer to Peer(P2P) applications, where the leader peer (node) is responsible for collecting, processing, and redistributing data or control signals satisfying some timing constraints. The protocol elects an optimal leader node in the dynamically changing network and constructs a Data Collection and Distribution Tree (DCDT) rooted at the leader node. The elected optimal leader is closest to all nodes in the system compared to other nodes. We validate the proposed protocol through theoretical proofs as well as experimental results.

Very few of the innovations in deep networking have seen data center scale implementation. Because the Data Center network's extreme scale performance requires hardware implementation, which is only accessible to a few. However, the emergence of reconfigurable match-action table (RMT) paradigm-based switches have finally opened up the development life cycle of data plane devices. The P4 language is the dominant language choice for programming these devices. Now, Network operators can implement the desired feature over white box RMT switches. The process involves an innovator writing new algorithms in the P4 language and getting them compiled for the target hardware. However, there is still a roadblock. After designing an algorithm, the P4 program's compilation technology is not fully open-source. Thus, it is very difficult for an average researcher to get deep insight into the performance of his/her innovation when executed at the silicon level. There is no open-source compiler backend available for this purpose. Proprietary compiler backends provided by different hardware vendors are available for this purpose. However, they are closed-source and do not provide access to the internal mapping mechanisms. Which inhibits experimenting with new mapping algorithms and innovative instruction sets for reconfigurable match-action table architecture. This paper describes our work toward an open-source compiler backend for compiling P416 targeted for the V1Model architecture-based programmable switches., Comment: arXiv admin note: substantial text overlap with arXiv:2208.12892

The P4 language has become the dominant choice for programming the reconfigurable match-action table based programmable switches. V1Model architecture is the most widely available realization of this paradigm. The open-source compiler frontend developed by the P4 consortium can execute syntax analysis and derive a hardware-independent representation of a program written using the latest version of P4 (also known as P416 ). A compiler backend is required to map this intermediate representation to the hardware resources of a V1Model switch. However, there is no open-source compiler backend available to check the realizability of a P416 program over a V1Model switch. Proprietary tools provided by different hardware vendors are available for this purpose. However, they are closed source and do not provide access to the internal mapping mechanisms. Which inhibits experimenting with new mapping algorithms and innovative instruction sets for reconfigurable match-action table architecture. Moreover, the proprietary compiler backends are costly and come with various non-disclosure agreements. These factors pose serious challenges to programmable switch-related research. In this work, we present an open-source P416 compiler backend for the V1Model architecture-based programmable switches. It uses heuristic-based mapping algorithms to map a P416 program over the hardware resources of a V1Model switch. It allows developers to rapidly prototype different mapping algorithms. It also gives various resource usage statistics of a P416 program, enabling comparison among multiple P416 schemes.

This work presents P4TE, an in-band traffic monitoring, load-aware packet forwarding, and flow rate controlling mechanism for traffic engineering in fat-tree topology-based data center networks using PISA switches. It achieves sub-RTT reaction time to change in network conditions, improved flow completion time, and balanced link utilization. Unlike the classical probe-based monitoring approach, P4TE uses an in-band monitoring approach to identify traffic events in the data plane. Based on these events, it re-adjusts the priorities of the paths. It uses a heuristic-based load-aware forwarding path selection mechanism to respond to changing network conditions and control the flow rate by sending feedback to the end hosts. It is implementable on emerging v1model.p4 architecture-based programmable switches and capable of maintaining the line-rate performance. Our evaluation shows that P4TE uses a small amount of resources in the PISA pipeline and achieves an improved flow completion time than ECMP and HULA.

Efficiencies of organic solar cells have practically doubled since the development of non-fullerene acceptors (NFAs). However, generic chemical design rules for donor-NFA combinations are still needed. Such rules are proposed by analyzing inhomogeneous electrostatic fields at the donor-acceptor interface. It is shown that an acceptor-donor-acceptor molecular architecture, and molecular alignment parallel to the interface, results in energy level bending that destabilizes the charge transfer state, thus promoting its dissociation into free charges. By analyzing a series of PCE10:NFA solar cells, with NFAs including Y6, IEICO, and ITIC, as well as their halogenated derivatives, it is suggested that the molecular quadrupole moment of ca 75 Debye A balances the losses in the open circuit voltage and gains in charge generation efficiency.

Non-fullerene acceptor (NFA)-based ternary bulk heterojunction solar cells (TSC) are the most efficient organic solar cells (OSCs) today due to their broader absorption and quantum efficiencies (QE) often surpassing those of corresponding binary blends. We study how the energetics driving charge transfer at the electron donor:electron acceptor (D/A) interfaces impact the QE in blends of PBDB-T-2F donor with several pairs of lower bandgap NFAs. As in binary blends, the ionization energy offset between donor and acceptor ({\Delta}IE) controls the QE and maximizes for {\Delta}IE > 0.5 eV. However, {\Delta}IE is not controlled by the individual NFAs IEs but by their average, weighted for their blending ratio. Using this property, we improved the QE of a PBDB-T-2F:IEICO binary blend that had an insufficient {\Delta}IE for charge generation by adding a deep IE third component: IT-4F. Combining two NFAs enables to optimize the D/A energy alignment and cells' QE without molecular engineering., Comment: S Karuthedath and S H K Paleti contributed equally. MS: 35 pages, 9 figures. SI: 21 pages, 23 figures - updates: added a model scheme as Fig1, updated T1 EQE and blends PL in Fig 2 (former 1). Added Figure (5): charge generation upon acceptor excitation. Corrected {\Delta}IEs in fig7 and 9 (a few tens of meV off). Added more blends to show generality (2 groups, 10 blends compositions)

In this work, we defined an attack vector for networks utilizing the Internet of Medical Things (IoMT) devices and compute the probability distribution of IoMT security threats based on Markov chain and Common Vulnerability Scoring System (CVSS). IoMT is an emerging technology that improves patients' quality of life by permitting personalized e-health services without restrictions on time and site. The IoMT consists of embedded objects, sensors, and actuators that transmit and receive medical data. These Medical devices are vulnerable to different types of security threats, and thus, they pose a significant risk to patient's privacy and safety. Because security is a critical factor for successfully merging IoMT into pervasive healthcare systems, there is an urgent need for new security mechanisms to prevent threats on the IoMT edge network. Toward this direction, the first step is defining an attack vector that an attacker or unauthorized user can take advantage of to penetrate and tamper with medical data. In this article, we specify a threat model for the IoMT edge network. We identify any vulnerabilities or weaknesses within the IoMT network that allow unauthorized privileges and threats that can utilize these weaknesses to compromise the IoMT edge network. Finally, we compute the probability distribution of IoMT threats based on the Markov transition probability matrix.