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Time Division Multiplexing (TDM) of cryogenic signal lines is a promising technique that can significantly reduce the required space, minimize the cooldown time, and increase the number of measurable quantum devices per cooldown. Here, we report the TDM of supercurrent with a 1-input-8-outputs voltage-actuated hybrid superconducting demultiplexer for the first time. The device comprises 14 ON/OFF InAsOI-based superconducting Josephson Field Effect Transistors (JoFETs) routed with Al traces. Each JoFET features Al as a superconductor and HfO2 as a gate insulator, and it can entirely suppress the switching current and increase the normal-state resistance by 20 times with a gate voltage of -4.5 V. The superconducting demultiplexer operates up to 100 MHz at 50 mK, features an insertion loss of ~ 0 dB in the superconducting state, and an OFF/ON ratio of ~ 17.5 dB in a 50-Ohm-matched cryogenic measurement setup. The frequency operation range can be extended by designing the demultiplexer with a proper microwave signal transport layout minimizing, at the same time, the impact of the parasitic electrical elements. These achievements open up the practical implementation of superconducting TDM as a key to drastically reducing I/O lines, costs, and space occupation in a cryostat, enabling the scalability of superconducting electronics., Comment: 13 pages, 5 figures, supporting information at the end of the paper. arXiv admin note: text overlap with arXiv:2405.07630

Dielectrics featuring a high relative permittivity, i.e., high-k dielectrics, have become the standard insulators in gate architectures, enhancing the electrical performance of both room temperature and cryogenic electronics. This study delves into the cryogenic (3 K) performance of high-k dielectrics commonly used as gate insulators. We fabricated Al2O3 and HfO2 layers via Atomic Layer Deposition (ALD) and we extrapolated relative permittivity (k) and dielectric strength (EBD) from AC (100 Hz to 100 kHz) and DC measurements on metal-insulator-metal capacitors. Our findings reveal a strong dependence of HfO2 cryogenic performance on the ALD growth temperature, while the latter shows a negligible impact on Al2O3. We estimated a ~9 % and ~14 % reduction of the relative permittivity of HfO2 and Al2O3, respectively, from 300 K to 3 K. Additionally, we designed and fabricated Al2O3/HfO2 bilayers and we checked their properties at cryogenic temperatures. The study also investigates the impact of the patterning method, namely, UV or electron-beam lithography (acceleration voltage of 10, 20, or 30 kV), on the high-k dielectric properties., Comment: 22 pages, 4 figures, supporting information at the end of the paper

In the preclinical translational studies, drug candidates with remarkable anti-epileptic efficacy demonstrate long-term suppression of spontaneous recurrent seizures (SRSs), particularly convulsive seizures (CSs), in mouse models of chronic epilepsy. However, the current methods for monitoring CSs have limitations in terms of invasiveness, specific laboratory settings, high cost, and complex operation, which hinder drug screening efforts. In this study, a camera-based system for automated detection of CSs in chronically epileptic mice is first established to screen potential anti-epilepsy drugs.

We study coisotropic deformations of a compact regular coisotropic submanifold $C$ in a contact manifold $(M,\xi)$. Our main result states that $C$ is rigid among nearby coisotropic submanifolds whose characteristic foliation is diffeomorphic to that of $C$. When combined with a classical rigidity result for foliations, this yields conditions under which $C$ is rigid among all nearby coisotropic submanifolds., Comment: 31 pages, comments welcome

The rapid advancement of energy-efficient parallel ultra-low-power (ULP) ucontrollers units (MCUs) is enabling the development of autonomous nano-sized unmanned aerial vehicles (nano-UAVs). These sub-10cm drones represent the next generation of unobtrusive robotic helpers and ubiquitous smart sensors. However, nano-UAVs face significant power and payload constraints while requiring advanced computing capabilities akin to standard drones, including real-time Machine Learning (ML) performance and the safe co-existence of general-purpose and real-time OSs. Although some advanced parallel ULP MCUs offer the necessary ML computing capabilities within the prescribed power limits, they rely on small main memories (<1MB) and ucontroller-class CPUs with no virtualization or security features, and hence only support simple bare-metal runtimes. In this work, we present Shaheen, a 9mm2 200mW SoC implemented in 22nm FDX technology. Differently from state-of-the-art MCUs, Shaheen integrates a Linux-capable RV64 core, compliant with the v1.0 ratified Hypervisor extension and equipped with timing channel protection, along with a low-cost and low-power memory controller exposing up to 512MB of off-chip low-cost low-power HyperRAM directly to the CPU. At the same time, it integrates a fully programmable energy- and area-efficient multi-core cluster of RV32 cores optimized for general-purpose DSP as well as reduced- and mixed-precision ML. To the best of the authors' knowledge, it is the first silicon prototype of a ULP SoC coupling the RV64 and RV32 cores in a heterogeneous host+accelerator architecture fully based on the RISC-V ISA. We demonstrate the capabilities of the proposed SoC on a wide range of benchmarks relevant to nano-UAV applications. The cluster can deliver up to 90GOp/s and up to 1.8TOp/s/W on 2-bit integer kernels and up to 7.9GFLOp/s and up to 150GFLOp/s/W on 16-bit FP kernels.

We define \emph{$0$-shifted} and \emph{$+1$-shifted contact structures} on differentiable stacks, thus laying the foundations of \emph{shifted Contact Geometry}. As a side result we show that the kernel of a multiplicative $1$-form on a Lie groupoid (might not exist as a Lie groupoid but it) always exists as a differentiable stack, and it is naturally equipped with a stacky version of the curvature of a distribution. Contact structures on orbifolds provide examples of $0$-shifted contact structures, while prequantum bundles over $+1$-shifted symplectic groupoids provide examples of $+1$-shifted contact structures. Our shifted contact structures are related to shifted symplectic structures via a Symplectic-to-Contact Dictionary., Comment: 48 pages. Several improvements have been made. Final version to appear in Int. Math. Res. Not. IMRN

Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can leverage longer-range interactions between nodes is hindered by the issues of over-smoothing and over-squashing. In particular, the latter is attributed to the graph topology which guides the message-passing, causing a node representation to become insensitive to information contained at distant nodes. Many graph rewiring methods have been proposed to remedy or mitigate this problem. However, properly evaluating the benefits of these methods is made difficult by the coupling of over-squashing with other issues strictly related to model training, such as vanishing gradients. Therefore, we propose an evaluation setting based on message-passing models that do not require training to compute node and graph representations. We perform a systematic experimental comparison on real-world node and graph classification tasks, showing that rewiring the underlying graph rarely does confer a practical benefit for message-passing.

Node classification tasks on graphs are addressed via fully-trained deep message-passing models that learn a hierarchy of node representations via multiple aggregations of a node's neighbourhood. While effective on graphs that exhibit a high ratio of intra-class edges, this approach poses challenges in the opposite case, i.e. heterophily, where nodes belonging to the same class are usually further apart. In graphs with a high degree of heterophily, the smoothed representations based on close neighbours computed by convolutional models are no longer effective. So far, architectural variations in message-passing models to reduce excessive smoothing or rewiring the input graph to improve longer-range message passing have been proposed. In this paper, we address the challenges of heterophilic graphs with Graph Echo State Network (GESN) for node classification. GESN is a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. Our experiments show that reservoir models are able to achieve better or comparable accuracy with respect to most fully trained deep models that implement ad hoc variations in the architectural bias or perform rewiring as a preprocessing step on the input graph, with an improvement in terms of efficiency/accuracy trade-off. Furthermore, our analysis shows that GESN is able to effectively encode the structural relationships of a graph node, by showing a correlation between iterations of the recursive embedding function and the distribution of shortest paths in a graph., Comment: 15 pages, 10 figures. arXiv admin note: text overlap with arXiv:2212.06538

In the companion paper arXiv:2110.05298, we developed the deformation theory of symplectic foliations, focusing on geometric aspects. Here, we address some algebraic questions that arose naturally. We show that the $L_{\infty}$-algebra constructed there is independent of the choices made, and we prove that the gauge equivalence of Maurer-Cartan elements corresponds to the equivalence by isotopies of symplectic foliations., Comment: 26 pages

On-chip DNN inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy and flexibility requirements. Heterogeneous clusters are promising solutions to meet the challenge, combining the flexibility of DSP-enhanced cores with the performance and energy boost of dedicated accelerators. We present DARKSIDE, a System-on-Chip with a heterogeneous cluster of 8 RISC-V cores enhanced with 2-b to 32-b mixed-precision integer arithmetic. To boost performance and efficiency on key compute-intensive Deep Neural Network (DNN) kernels, the cluster is enriched with three digital accelerators: a specialized engine for low-data-reuse depthwise convolution kernels (up to 30 MAC/cycle); a minimal overhead datamover to marshal 1-b to 32-b data on-the-fly; a 16-b floating point Tensor Product Engine (TPE) for tiled matrix-multiplication acceleration. DARKSIDE is implemented in 65nm CMOS technology. The cluster achieves a peak integer performance of 65 GOPS and a peak efficiency of 835 GOPS/W when working on 2-b integer DNN kernels. When targeting floating-point tensor operations, the TPE provides up to 18.2 GFLOPS of performance or 300 GFLOPS/W of efficiency - enough to enable on-chip floating-point training at competitive speed coupled with ultra-low power quantized inference., Comment: 11 pages, 15 figures

Space Cyber-Physical Systems (S-CPS) such as spacecraft and satellites strongly rely on the reliability of onboard computers to guarantee the success of their missions. Relying solely on radiation-hardened technologies is extremely expensive, and developing inflexible architectural and microarchitectural modifications to introduce modular redundancy within a system leads to significant area increase and performance degradation. To mitigate the overheads of traditional radiation hardening and modular redundancy approaches, we present a novel Hybrid Modular Redundancy (HMR) approach, a redundancy scheme that features a cluster of RISC-V processors with a flexible on-demand dual-core and triple-core lockstep grouping of computing cores with runtime split-lock capabilities. Further, we propose two recovery approaches, software-based and hardware-based, trading off performance and area overhead. Running at 430 MHz, our fault-tolerant cluster achieves up to 1160 MOPS on a matrix multiplication benchmark when configured in non-redundant mode and 617 and 414 MOPS in dual and triple mode, respectively. A software-based recovery in triple mode requires 363 clock cycles and occupies 0.612 mm2, representing a 1.3% area overhead over a non-redundant 12-core RISC-V cluster. As a high-performance alternative, a new hardware-based method provides rapid fault recovery in just 24 clock cycles and occupies 0.660 mm2, namely ~9.4% area overhead over the baseline non-redundant RISC-V cluster. The cluster is also enhanced with split-lock capabilities to enter one of the redundant modes with minimum performance loss, allowing execution of a mission-critical or a performance section, with <400 clock cycles overhead for entry and exit. The proposed system is the first to integrate these functionalities on an open-source RISC-V-based compute device, enabling finely tunable reliability vs. performance trade-offs.

The increasing interest in TinyML, i.e., near-sensor machine learning on power budgets of a few tens of mW, is currently pushing toward enabling TinyML-class training as opposed to inference only. Current training algorithms, based on various forms of error and gradient backpropagation, rely on floating-point matrix operations to meet the precision and dynamic range requirements. So far, the energy and power cost of these operations has been considered too high for TinyML scenarios. This paper addresses the open challenge of near-sensor training on a few mW power budget and presents RedMulE - Reduced-Precision Matrix Multiplication Engine, a low-power specialized accelerator conceived for multi-precision floating-point General Matrix-Matrix Operations (GEMM-Ops) acceleration, supporting FP16, as well as hybrid FP8 formats, with {sign, exponent, mantissa}=({1,4,3}, {1,5,2}). We integrate RedMule into a Parallel Ultra-Low-Power (PULP) cluster containing eight energy-efficient RISC-V cores sharing a tightly-coupled data memory and implement the resulting system in a 22 nm technology. At its best efficiency point (@ 470 MHz, 0.65 V), the RedMulE-augmented PULP cluster achieves 755 GFLOPS/W and 920 GFLOPS/W during regular General Matrix-Matrix Multiplication (GEMM), and up to 1.19 TFLOPS/W and 1.67 TFLOPS/W when executing GEMM-Ops, respectively, for FP16 and FP8 input/output tensors. In its best performance point (@ 613 MHz, 0.8 V), RedMulE achieves up to 58.5 GFLOPS and 117 GFLOPS for FP16 and FP8, respectively, with 99.4% utilization of the array of Computing Elements and consuming less than 60 mW on average, thus enabling on-device training of deep learning models in TinyML application scenarios while retaining the flexibility to tackle other classes of common linear algebra problems efficiently.

Recent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-squashing' phenomenon. As a remedy, graph rewiring mechanisms have been proposed as preprocessing steps. Graph Echo State Networks (GESNs) are a reservoir computing model for graphs, where node embeddings are recursively computed by an untrained message-passing function. In this paper, we show that GESNs can achieve a significantly better accuracy on six heterophilic node classification tasks without altering the graph connectivity, thus suggesting a different route for addressing the over-squashing problem., Comment: Extended Abstract. Presented at the First Learning on Graphs Conference (LoG 2022), Virtual Event, December 9-12, 2022

IoT applications span a wide range in performance and memory footprint, under tight cost and power constraints. High-end applications rely on power-hungry Systems-on-Chip (SoCs) featuring powerful processors, large LPDDR/DDR3/4/5 memories, and supporting full-fledged Operating Systems (OS). On the contrary, low-end applications typically rely on Ultra-Low-Power ucontrollers with a "close to metal" software environment and simple micro-kernel-based runtimes. Emerging applications and trends of IoT require the "best of both worlds": cheap and low-power SoC systems with a well-known and agile software environment based on full-fledged OS (e.g., Linux), coupled with extreme energy efficiency and parallel digital signal processing capabilities. We present HULK-V: an open-source Heterogeneous Linux-capable RISC-V-based SoC coupling a 64-bit RISC-V processor with an 8-core Programmable Multi-Core Accelerator (PMCA), delivering up to 13.8 GOps, up to 157 GOps/W and accelerating the execution of complex DSP and ML tasks by up to 112x over the host processor. HULK-V leverages a lightweight, fully digital memory hierarchy based on HyperRAM IoT DRAM that exposes up to 512 MB of DRAM memory to the host CPU. Featuring HyperRAMs, HULK-V doubles the energy efficiency without significant performance loss compared to featuring power-hungry LPDDR memories, requiring expensive and large mixed-signal PHYs. HULK-V, implemented in Global Foundries 22nm FDX technology, is a fully digital ultra-low-cost SoC running a 64-bit Linux software stack with OpenMP host-to-PMCA offload within a power envelope of just 250 mW., Comment: This paper has been accepted as full paper at DATE23 https://www.date-conference.com/date-2023-accepted-papers#Regular-Papers

Graph Echo State Networks (GESN) have already demonstrated their efficacy and efficiency in graph classification tasks. However, semi-supervised node classification brought out the problem of over-smoothing in end-to-end trained deep models, which causes a bias towards high homophily graphs. We evaluate for the first time GESN on node classification tasks with different degrees of homophily, analyzing also the impact of the reservoir radius. Our experiments show that reservoir models are able to achieve better or comparable accuracy with respect to fully trained deep models that implement ad hoc variations in the architectural bias, with a gain in terms of efficiency., Comment: Accepted for oral presentation at ESANN 2022

The fast proliferation of extreme-edge applications using Deep Learning (DL) based algorithms required dedicated hardware to satisfy extreme-edge applications' latency, throughput, and precision requirements. While inference is achievable in practical cases, online finetuning and adaptation of general DL models are still highly challenging. One of the key stumbling stones is the need for parallel floating-point operations, which are considered unaffordable on sub-100 mW extreme-edge SoCs. We tackle this problem with RedMulE (Reduced-precision matrix Multiplication Engine), a parametric low-power hardware accelerator for FP16 matrix multiplications - the main kernel of DL training and inference - conceived for tight integration within a cluster of tiny RISC-V cores based on the PULP (Parallel Ultra-Low-Power) architecture. In 22 nm technology, a 32-FMA RedMulE instance occupies just 0.07 mm^2 (14% of an 8-core RISC-V cluster) and achieves up to 666 MHz maximum operating frequency, for a throughput of 31.6 MAC/cycle (98.8% utilization). We reach a cluster-level power consumption of 43.5 mW and a full-cluster energy efficiency of 688 16-bit GFLOPS/W. Overall, RedMulE features up to 4.65x higher energy efficiency and 22x speedup over SW execution on 8 RISC-V cores.

We develop the deformations theory of a Dirac--Jacobi structure within a fixed Courant--Jacobi algebroid. Using the description of split Courant--Jacobi algebroids as degree $2$ contact $\mathbb{N} Q$ manifolds and Voronov's higher derived brackets, each Dirac--Jacobi structure is associated with a cubic $L_\infty$ algebra for any choice of a complementary almost Dirac--Jacobi structure. This $L_\infty$ algebra governs the deformations of the Dirac--Jacobi structure: there is a one-to-one correspondence between the MC elements of this $L_\infty$ algebra and the small deformations of the Dirac-Jacobi structure. Further, by Cattaneo and Sch\"atz's equivalence of higher derived brackets, this $L_\infty$ algebra does not depend (up to $L_\infty$-isomorphisms) on the choice of the complementary almost Dirac--Jacobi structure. These same ideas apply to get a new proof of the independence of the $L_\infty$ algebra of Dirac structure from the choice of a complementary almost Dirac structure (a result proved using other techniques by Gualtieri, Matviichuk and Scott)., Comment: 34 pages, comments welcome!

Dynamic temporal graphs represent evolving relations between entities, e.g. interactions between social network users or infection spreading. We propose an extension of graph echo state networks for the efficient processing of dynamic temporal graphs, with a sufficient condition for their echo state property, and an experimental analysis of reservoir layout impact. Compared to temporal graph kernels that need to hold the entire history of vertex interactions, our model provides a vector encoding for the dynamic graph that is updated at each time-step without requiring training. Experiments show accuracy comparable to approximate temporal graph kernels on twelve dissemination process classification tasks., Comment: Accepted for oral presentation at ESANN 2021

We develop the deformation theory of symplectic foliations, i.e. regular foliations equipped with a leafwise symplectic form. The main result of this paper is that each symplectic foliation has an attached $L_\infty$-algebra controlling its deformation problem. Indeed, viewing symplectic foliations as regular Poisson structures, we establish a one-to-one correspondence between the small deformations of a given symplectic foliation and the Maurer-Cartan elements of the associated $L_\infty$-algebra. Using this, we show that infinitesimal deformations of symplectic foliations can be obstructed. Further, we relate symplectic foliations with foliations on one side and with (arbitrary) Poisson structures on the other, showing that obstructed infinitesimal deformations of the former may give rise to unobstructed infinitesimal deformations of the latter., Comment: Final version accepted for publication. Comments welcome

Adopting the omni-Lie algebroid approach to Dirac-Jacobi structures, we propose and investigate a notion of weak dual pairs in Dirac-Jacobi geometry. Their main motivating examples arise from the theory of multiplicative precontact structures on Lie groupoids. Among other properties of weak dual pairs, we prove two main results. 1) We show that the property of fitting in a weak dual pair defines an equivalence relation for Dirac-Jacobi manifolds. So, in particular, we get the existence of self-dual pairs and this immediately leads to an alternative proof of the normal form theorem around Dirac-Jacobi transversals. 2) We prove the characteristic leaf correspondence theorem for weak dual pairs paralleling and extending analogous results for symplectic and contact dual pairs. Moreover, the same ideas of this proof apply to get a presymplectic leaf correspondence for weak dual pairs in Dirac geometry (not yet present in literature)., Comment: 38 pages, comments welcome!

Brain-Computer Interfaces (BCI) based on motor imagery translate mental motor images recognized from the electroencephalogram (EEG) to control commands. EEG patterns of different imagination tasks, e.g. hand and foot movements, are effectively classified with machine learning techniques using band power features. Recently, also Convolutional Neural Networks (CNNs) that learn both effective features and classifiers simultaneously from raw EEG data have been applied. However, CNNs have two major drawbacks: (i) they have a very large number of parameters, which thus requires a very large number of training examples; and (ii) they are not designed to explicitly learn features in the frequency domain. To overcome these limitations, in this work we introduce Sinc-EEGNet, a lightweight CNN architecture that combines learnable band-pass and depthwise convolutional filters. Experimental results obtained on the publicly available BCI Competition IV Dataset 2a show that our approach outperforms reference methods in terms of classification accuracy.

The theory of $G$-structures provides us with a unified framework for a large class of geometric structures, including symplectic, complex and Riemannian structures, as well as foliations and many others. Surprisingly, contact geometry - the "odd-dimensional counterpart" of symplectic geometry - does not fit naturally into this picture. In this paper, we introduce the notion of a homogeneous $G$-structure, which encompasses contact structures, as well as some other interesting examples that appear in the literature., Comment: 20 pages, to appear in Ann. Mat. Pura Appl., comments welcome

We introduce and study the notion of contact dual pair adopting a line bundle approach to contact and Jacobi geometry. A contact dual pair is a pair of Jacobi morphisms defined on the same contact manifold and satisfying a certain orthogonality condition. Contact groupoids and contact reduction are the main sources of examples. Among other properties, we prove the Characteristic Leaf Correspondence Theorem for contact dual pairs which parallels the analogous result of Weinstein for symplectic dual pairs., Comment: v2: 36 pages, minor revisions, final version to appear in IMRN, comments still welcome!

In this thesis, we study the deformation problem of coisotropic submanifolds in Jacobi manifolds. In particular we attach two algebraic invariants to any coisotropic submanifold $S$ in a Jacobi manifold, namely the $L_\infty[1]$-algebra and the BFV-complex of $S$. Our construction generalizes and unifies analogous constructions in symplectic, Poisson, and locally conformal symplectic geometry. As a new special case we also attach an $L_\infty[1]$-algebra and a BFV-complex to any coisotropic submanifold in a contact manifold. The $L_\infty[1]$-algebra of $S$ controls the formal coisotropic deformation problem of $S$, even under Hamiltonian equivalence. The BFV-complex of $S$ controls the non-formal coisotropic deformation problem of $S$, even under both Hamiltonian and Jacobi equivalence. In view of these results, we exhibit, in the contact setting, two examples of coisotropic submanifolds whose coisotropic deformation problem is obstructed., Comment: Ph.D. thesis, University of Florence, supervisors: Luca Vitagliano and Paolo de Bartolomeis, 174 pages

VB-groupoids and algebroids are vector bundle objects in the categories of Lie groupoids and Lie algebroids respectively, and they are related via the Lie functor. VB-groupoids and algebroids play a prominent role in Poisson and related geometries. Additionally, they can be seen as models for vector bundles over singular spaces. In this paper we study their infinitesimal automorphisms, i.e. vector fields on them generating a flow by diffeomorphisms preserving both the linear and the groupoid/algebroid structures. For a special class of VB-groupoids/algebroids coming from representations of Lie groupoids/algebroids, we prove that infinitesimal automorphisms are the same as multiplicative sections of a certain derivation groupoid/algebroid., Comment: v4: 44 pages, several revisions, final version to appear in Q. J. Math., comments very welcome

We demonstrate an electrolyte-gated hybrid nanoparticle/organic synapstor (synapse-transistor, termed EGOS) that exhibits short-term plasticity as biological synapses. The response of EGOS makes it suitable to be interfaced with neurons: short-term plasticity is observed at spike voltage as low as 50 mV (in a par with the amplitude of action potential in neurons) and with a typical response time in the range of tens milliseconds. Human neuroblastoma stem cells are adhered and differentiated into neurons on top of EGOS. We observe that the presence of the cells does not alter short-term plasticity of the device., Comment: Full paper, figures and supporting information

Unlike Legendrian submanifolds, the deformation problem of coisotropic submanifolds can be obstructed. Starting from this observation, we single out in the contact setting the special class of integral coisotropic submanifolds as the direct generalization of Legendrian submanifolds for what concerns deformation and moduli theory. Indeed, being integral coisotropic is proved to be a rigid condition, and moreover the integral coisotropic deformation problem is unobstructed with discrete moduli space., Comment: v3: 12 pages, published in Lett. Math. Phys., comments still welcome!

We extend the construction of the BFV-complex of a coisotropic submanifold from the Poisson setting to the Jacobi setting. In particular, our construction applies in the contact and l.c.s. settings. The BFV-complex of a coisotropic submanifold $S$ controls the coisotropic deformation problem of $S$ under both Hamiltonian and Jacobi equivalence., Comment: v2: 51 pages, added a new section on an obstructed example in the contact setting, final version to appear in J. Geom. Phys., comments still welcome!

We present the notion of higher Kirillov brackets on the sections of an even line bundle over a supermanifold. When the line bundle is trivial we shall speak of higher Jacobi brackets. These brackets are understood furnishing the module of sections with an $L_{\infty}$-algebra, which we refer to as a homotopy Kirillov algebra. We are then to higher Kirillov algebroids as higher generalisations of Jacobi algebroids. Furthermore, we show how to associate a higher Kirillov algebroid and a homotopy BV-algebra with every higher Kirillov manifold. In short, we construct homotopy versions of some of the well-known theorems related to Kirillov's local Lie algebras., Comment: Change of title, improved introduction section and examples. Minor typos etc corrected. No major revision of the key results. 15 pages. Further comments welcomed

In this paper, we attach an $L_\infty$-algebra to any coisotropic submanifold in a Jacobi manifold. Our construction generalizes and unifies analogous constructions by Oh-Park (symplectic case), Cattaneo-Felder (Poisson case), L\^e-Oh (locally conformal symplectic case). As a new special case, we attach an $L_\infty$-algebra to any coisotropic submanifold in a contact manifold. The $L_\infty$-algebra of a coisotropic submanifold $S$ governs the (formal) deformation problem of $S$., Comment: 41 pages, v2: several revisions, title and abstract slightly changed, mathematics unchanged; final version, to appear in J. Sympl. Geom. 16 (2018)

Focusing on the culture of students with disabilities allows teachers to add another real-world dimension to the multi-cultural atmosphere of their classrooms. While Public Law 94-142 (the Education for All Handicapped Children Act) legislates what opportunities must exist for the differently able, volunteer program such as "Kids on the Block" encourage acceptance of those with disabilities. Using the Banraku style of puppetry, "Kids on the Block" brings four-foot-high puppets into the classroom and creates a climate that allows children to be more open about disability. After the introductory lesson presented by this volunteer group, sixth-graders at School #74 in Buffalo, New York, read and then wrote about the campers with disabilities depicted in Ron Jones's book "The Acorn People." Students read, took notes, and composed their summary of the book as a group. Addressing the issue of understanding persons with disabilities is not part of the English Curriculum per se, but it provides productive opportunities for group writing and for appreciation of diversity. (SAM)