A detailed approach to autonomous vehicle control through Ros and Pixhawk controllers

A Polaris MRZR military utility vehicle was used as a testing platform to develop a novel, low cost yet feature-rich, approach to adding remote operation and autonomous driving capability to a military vehicle. The main concept of operation adapts steering and throttle output from a low cost commercially available Pixhawk autopilot controller and translates the signal into the necessary inputs for the Robot Operating System (ROS) based drive by wire system integrated into the MRZR. With minimal modification these enhancements could be applied to any vehicle with similar ROS integration. This paper details the methods and testing approach used to develop this autonomous driving capability.

Robust and Blind Audio Watermarking Scheme Based on Genetic Algorithm in Dual Transform Domain

In order to protect the copyright of audio media in cyberspace, a robust and blind audio watermarking scheme based on the genetic algorithm (GA) is proposed in a dual transform domain. A formula for calculating the embedding depth is developed, and two embedding depths with different values are used to represent the “1” and “0” states of the binary watermark, respectively. In the extracting process, the embedding depth in each audio fragment will be calculated and compared with the average embedding depth to determine the watermark bit by bit, so this scheme can blindly extract the watermark without the original audio. GA will be applied to optimize the algorithm parameters for meeting the performance requirements in different applications. Besides, the embedding rule is further optimized to enhance the transparency based on the principle of minimal modification to the audio. Experimental results prove that the payload capacity reaches 172.27 bps, the bit error rate (BER) is 0.1% under the premise that its transparency is higher than 25 dB, and its robustness is strong against many attacks. Significantly, this scheme can adaptively select the algorithm parameters to satisfy the specific performance requirements.

On the NS-DSSB unidirectional estimates in the SAMPL6 SAMPLing challenge

In the context of the recent SAMPL6 SAMPLing challenge (Rizzi et al. 2020 in J Comput Aided Mol Des 34:601–633) aimed at assessing convergence properties and reproducibility of molecular dynamics binding free energy methodologies, we propose a simple explanation of the severe errors observed in the nonequilibrium switch double-system-single-box (NS-DSSB) approach when using unidirectional estimates. At the same time, we suggest a straightforward and minimal modification of the NS-DSSB protocol for obtaining reliable unidirectional estimates for the process where the ligand is decoupled in the bound state and recoupled in the bulk.

Multi-centered black holes, scaling solutions and pure-Higgs indices from localization

The Coulomb Branch Formula conjecturally expresses the refined Witten index for N=4 Quiver Quantum Mechanics as a sum over multi-centered collinear black hole solutions, weighted by so-called `single-centered' or `pure-Higgs' indices, and suitably modified when the quiver has oriented cycles. On the other hand, localization expresses the same index as an integral over the complexified Cartan torus and auxiliary fields, which by Stokes' theorem leads to the famous Jeffrey-Kirwan residue formula. Here, by evaluating the same integral using steepest descent methods, we show the index is in fact given by a sum over deformed multi-centered collinear solutions, which encompasses both regular and scaling collinear solutions. As a result, we confirm the Coulomb Branch Formula for Abelian quivers in the presence of oriented cycles, and identify the origin of the pure-Higgs and minimal modification terms as coming from collinear scaling solutions. For cyclic Abelian quivers, we observe that part of the scaling contributions reproduce the stacky invariants for trivial stability, a mathematically well-defined notion whose physics significance had remained obscure.

New Algorithms for Japanese Residency Matching

We study the Japanese Residency Matching Program (JRMP) in which hospitals are partitioned into disjoint regions and both hospitals and regions are subject to quotas. To achieve a balanced distribution of doctors across regions, hard bounds are imposed by the government to limit the number of doctors who can be placed in each region. However, such hard bounds lead to inefficiency in terms of wasted vacant positions. In this paper, we propose two suitable algorithms to reduce waste with minimal modification to the current system and show that they are superior to the algorithm currently deployed in JRMP by comparing them theoretically and empirically.

ABCDP: Approximate Bayesian Computation with Differential Privacy

We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework.

A Delay-Based Machine Learning Model for DMA Attack Mitigation

Direct Memory Access (DMA) is a state-of-the-art technique to optimize the speed of memory access and to efficiently use processing power during data transfers between the main system and a peripheral device. However, this advanced feature opens security vulnerabilities of access compromise and to manipulate the main memory of the victim host machine. The paper outlines a lightweight process that creates resilience against DMA attacks minimal modification to the configuration of the DMA protocol. The proposed scheme performs device identification of the trusted PCIe devices that have DMA capabilities and constructs a database of profiling time to authenticate the trusted devices before they can access the system. The results show that the proposed scheme generates a unique identifier for trusted devices and authenticates the devices. Furthermore, a machine learning–based real-time authentication scheme is proposed that enables runtime authentication and share the results of the time required for training and respective accuracy.

Learning without loss

We explore a new approach for training neural networks where all loss functions are replaced by hard constraints. The same approach is very successful in phase retrieval, where signals are reconstructed from magnitude constraints and general characteristics (sparsity, support, etc.). Instead of taking gradient steps, the optimizer in the constraint based approach, called relaxed–reflect–reflect (RRR), derives its steps from projections to local constraints. In neural networks one such projection makes the minimal modification to the inputs x, the associated weights w, and the pre-activation value y at each neuron, to satisfy the equation $x\cdot w=y$ x ⋅ w = y . These projections, along with a host of other local projections (constraining pre- and post-activations, etc.) can be partitioned into two sets such that all the projections in each set can be applied concurrently—across the network and across all data in the training batch. This partitioning into two sets is analogous to the situation in phase retrieval and the setting for which the general purpose RRR optimizer was designed. Owing to the novelty of the method, this paper also serves as a self-contained tutorial. Starting with a single-layer network that performs nonnegative matrix factorization, and concluding with a generative model comprising an autoencoder and classifier, all applications and their implementations by projections are described in complete detail. Although the new approach has the potential to extend the scope of neural networks (e.g. by defining activation not through functions but constraint sets), most of the featured models are standard to allow comparison with stochastic gradient descent.

Open reduction and internal fixation of metacarpal fractures using a thermoplastic splint as a surgical instrument

Adequate positioning of the hand is a critical step in hand fracture operative repair that can impact both the clinical outcome and the efficiency of the operation. In this paper, we introduce the use of a thermoplastic splint with an added thumb stabilizing component as a means to increase the surgeon’s autonomy and to streamline the patient care pathway. The thermoplastic splint is custom fabricated preoperatively by the specialist hand therapist. The splint is used prior, during, and post operation with minimal modification. The thumb component assists maintaining the forearm in a stable pronated position whilst drilling and affixing metal work. This is demonstrated in the video of removal of metal work and open reduction and internal fixation of a metacarpal fracture.

Revisiting Russell’s troodontid: autecology, physiology, and speculative tool use

Dale Russell described the osteology, morphology, and ecology of the small theropod “Stenonychosaurus inequalis” in two papers, speculating on its life habits, brain power, vision, movement, feeding, and hand capabilities. Russell even pondered a tool-using dinosauroid, the hypothetical troodontid descendant if the lineage had survived the Cretaceous–Paleogene extinction event. We revisit the life habits of the North American troodontids Troodon formosus and Latenivenatrix mcmasterae in part by reviewing various trace fossils of T. formosus discovered in Montana. These fossils include egg clutches, a nest, and recently discovered regurgitalites. We also contemplate the possibility of dinosaur tool use. Troodon likely constructed earthen nests in the same way that ratites and other birds did to create their nesting scrapes through backward hindlimb kicks. The more complex clutch architecture suggests dexterous movement of the eggs, potentially requiring manual manipulation. Functionally, reproductive traces support elevated body temperatures and a metabolic output that approach but do not equal that of modern birds. Brooding would require very high energy investment from the adult. The regurgitalites largely contain multi-individual aggregations of the marsupialiform Alphadon and support Russell’s hypotheses of troodontids as crepuscular to nocturnal, intelligent, small game hunters with elevated metabolism and enhanced vision. Tool use in a few crocodilians and widely among extant birds suggests a reasonable possibility of this behavior in nonavian dinosaurs. Whether an avian-comparable encephalization quotient and freed forelimbs would make North American troodontids good candidates for exhibiting such behavior remains an open and speculative question. However, given the minimal modification made to tools by modern archosaurs, recognition of fossil tools poses a challenging problem.