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Effective fall risk assessment is critical for post-stroke patients. The present study proposes a novel, data-informed fall risk assessment method based on the instrumented Timed Up and Go (ITUG) test data, bringing in many mobility measures that traditional clinical scales fail to capture. IFRA, which stands for Instrumented Fall Risk Assessment, has been developed using a two-step process: first, features with the highest predictive power among those collected in a ITUG test have been identified using machine learning techniques; then, a strategy is proposed to stratify patients into low, medium, or high-risk strata. The dataset used in our analysis consists of 142 participants, out of which 93 were used for training (15 synthetically generated), 17 for validation and 32 to test the resulting IFRA scale (22 non-fallers and 10 fallers). Features considered in the IFRA scale include gait speed, vertical acceleration during sit-to-walk transition, and turning angular velocity, which align well with established literature on the risk of fall in neurological patients. In a comparison with traditional clinical scales such as the traditional Timed Up & Go and the Mini-BESTest, IFRA demonstrates competitive performance, being the only scale to correctly assign more than half of the fallers to the high-risk stratum (Fischer's Exact test p = 0.004). Despite the dataset's limited size, this is the first proof-of-concept study to pave the way for future evidence regarding the use of IFRA tool for continuous patient monitoring and fall prevention both in clinical stroke rehabilitation and at home post-discharge., Comment: 26 pages, 2 figures, submitted for review dec 2024

A monolithic silicon pixel ASIC prototype, produced in 2024 as part of the Horizon 2020 MONOLITH ERC Advanced project, was tested with a 120 GeV/c pion beam. The ASIC features a matrix of hexagonal pixels with a 100 \mu m pitch, read by low-noise, high-speed front-end electronics built using 130 nm SiGe BiCMOS technology. It includes the PicoAD sensor, which employs a continuous, deep PN junction to generate avalanche gain. Data were taken across power densities from 0.05 to 2.6 W/cm2 and sensor bias voltages from 90 to 180 V. At the highest bias voltage, corresponding to an electron gain of 50, and maximum power density, an efficiency of (99.99 \pm 0.01)% was achieved. The time resolution at this working point was (24.3 \pm 0.2) ps before time-walk correction, improving to (12.1 \pm 0.3) ps after correction.

The ability to detect and engineer chirality plays a defining role in understanding nature, presenting itself in various phenomena ranging from the formation of DNA to enzymatic activities and digital encoding. Inducing a chiral response using nanophotonic practices typically entails lithographic efforts to create micro- and nano-structures that break mirror symmetry. Even with such configurations and despite the ubiquitousness of thermal radiation, generating chiral light at mid-infrared frequencies through thermal emission remains a challenge, since incandescence is by nature incoherent and unpolarized. In contrast to lithography-based approaches, we demonstrate that unpatterned twisted bilayers of in-plane anisotropic materials serve as a new material platform for intrinsic mid-infrared chirality engineering. Via absorption spectroscopy as well as direct thermal emission measurements, we report large circular dichroism and asymmetric circularly polarized thermal emission from exfoliated twisted bilayers of $\alpha$-molybdenum trioxide ($\alpha$-MoO$_3$). These findings highlight the potential of twisted layered van der Waals materials for polarization control and advanced thermal infrared technologies.

For machine learning components used as part of autonomous systems (AS) in carrying out critical tasks it is crucial that assurance of the models can be maintained in the face of post-deployment changes (such as changes in the operating environment of the system). A critical part of this is to be able to monitor when the performance of the model at runtime (as a result of changes) poses a safety risk to the system. This is a particularly difficult challenge when ground truth is unavailable at runtime. In this paper we introduce a process for creating safety monitors for ML components through the use of degraded datasets and machine learning. The safety monitor that is created is deployed to the AS in parallel to the ML component to provide a prediction of the safety risk associated with the model output. We demonstrate the viability of our approach through some initial experiments using publicly available speed sign datasets.

Approaching thermodynamic limits in light harvesting requires enabling nonreciprocal thermal emission. The majority of previously reported nonreciprocal thermal emitters operate in reflection mode, following original proposals by M. Green and others. In these proposals, cascaded nonreciprocal junctions that re-direct each junction's emission towards a subsequent one are employed for efficient light-harvesting. Recently, simplified concepts have been proposed in solar photovoltaics and thermophotovoltaics that leverage the concept of tandem junctions to approach thermodynamic limits. In these simplified scenarios, polarization-independent nonreciprocal response in transmission mode is required. We propose a pattern-free heterostructure that enables such functionality, using a magneto-optical material embedded between two dissimilar dielectric layers., Comment: 9 pages, 6 figures

Samples of the monolithic silicon pixel ASIC prototype produced in 2022 within the framework of the Horizon 2020 MONOLITH ERC Advanced project were irradiated with 70 MeV protons up to a fluence of 1 x 1016 neq/cm2, and then tested using a beam of 120 GeV/c pions. The ASIC contains a matrix of 100 \mu m pitch hexagonal pixels, readout out by low noise and very fast frontend electronics produced in a 130 nm SiGe BiCMOS technology process. The dependence on the proton fluence of the efficiency and the time resolution of this prototype was measured with the frontend electronics operated at a power density between 0.13 and 0.9 W/cm2. The testbeam data show that the detection efficiency of 99.96% measured at sensor bias voltage of 200 V before irradiation becomes 96.2% after a fluence of 1 x 1016 neq/cm2. An increase of the sensor bias voltage to 300 V provides an efficiency to 99.7% at that proton fluence. The timing resolution of 20 ps measured before irradiation rises for a proton fluence of 1 x 1016 neq/cm2 to 53 and 45 ps at HV = 200 and 300 V, respectively.

The time resolution of the second monolithic silicon pixel prototype produced for the MONOLITH H2020 ERC Advanced project was studied using a femtosecond laser. The ASIC contains a matrix of hexagonal pixels with 100 {\mu}m pitch, readout by low-noise and very fast SiGe HBT frontend electronics. Silicon wafers with 50 {\mu}m thick epilayer with a resistivity of 350 {\Omega}cm were used to produce a fully depleted sensor. At the highest frontend power density tested of 2.7 W/cm2, the time resolution with the femtosecond laser pulses was found to be 45 ps for signals generated by 1200 electrons, and 3 ps in the case of 11k electrons, which corresponds approximately to 0.4 and 3.5 times the most probable value of the charge generated by a minimum-ionizing particle. The results were compared with testbeam data taken with the same prototype to evaluate the time jitter produced by the fluctuations of the charge collection., Comment: Submitted to JINST

A monolithic silicon pixel prototype produced for the MONOLITH ERC Advanced project was irradiated with 70 MeV protons up to a fluence of 1 x 10^16 1 MeV n_eq/cm^2. The ASIC contains a matrix of hexagonal pixels with 100 {\mu}m pitch, readout by low-noise and very fast SiGe HBT frontend electronics. Wafers with 50 {\mu}m thick epilayer with a resistivity of 350 {\Omega}cm were used to produce a fully depleted sensor. Laboratory tests conducted with a 90Sr source show that the detector works satisfactorily after irradiation. The signal-to-noise ratio is not seen to change up to fluence of 6 x 10^14 n_eq /cm^2 . The signal time jitter was estimated as the ratio between the voltage noise and the signal slope at threshold. At -35 {^\circ}C, sensor bias voltage of 200 V and frontend power consumption of 0.9 W/cm^2, the time jitter of the most-probable signal amplitude was estimated to be 21 ps for proton fluence up to 6 x 10 n_eq/cm^2 and 57 ps at 1 x 10^16 n_eq/cm^2 . Increasing the sensor bias to 250 V and the analog voltage of the preamplifier from 1.8 to 2.0 V provides a time jitter of 40 ps at 1 x 10^16 n_eq/cm^2., Comment: Submitted to JINST

Heat engines cannot generally operate at maximum power and efficiency, imposing a trade-off between the two. Here, we highlight the exact nature of this trade-off for engines that exchange heat radiatively with a hot source. We derive simple analytical expressions for the performance bounds of reciprocal and nonreciprocal radiative heat engines. We also highlight that radiative engines can achieve a better power-efficiency trade-off than linear ones. These bounds are especially relevant for thermophotovoltaics, offering useful metrics against which to compare device performance., Comment: 7 pages, 6 figures

A second monolithic silicon pixel prototype was produced for the MONOLITH project. The ASIC contains a matrix of hexagonal pixels with 100 {\mu}m pitch, readout by a low-noise and very fast SiGe HBT frontend electronics. Wafers with 50 {\mu}m thick epilayer of 350 {\Omega}cm resistivity were used to produce a fully depleted sensor. Laboratory and testbeam measurements of the analog channels present in the pixel matrix show that the sensor has a 130 V wide bias-voltage operation plateau at which the efficiency is 99.8%. Although this prototype does not include an internal gain layer, the design optimised for timing of the sensor and the front-end electronics provides a time resolutions of 20 ps., Comment: 11 pages, 11 figures

Structured waves are ubiquitous for all areas of wave physics, both classical and quantum, where the wavefields are inhomogeneous and cannot be approximated by a single plane wave. Even the interference of two plane waves, or a single inhomogeneous (evanescent) wave, provides a number of nontrivial phenomena and additional functionalities as compared to a single plane wave. Complex wavefields with inhomogeneities in the amplitude, phase, and polarization, including topological structures and singularities, underpin modern nanooptics and photonics, yet they are equally important, e.g., for quantum matter waves, acoustics, water waves, etc. Structured waves are crucial in optical and electron microscopy, wave propagation and scattering, imaging, communications, quantum optics, topological and non-Hermitian wave systems, quantum condensed-matter systems, optomechanics, plasmonics and metamaterials, optical and acoustic manipulation, and so forth. This Roadmap is written collectively by prominent researchers and aims to survey the role of structured waves in various areas of wave physics. Providing background, current research, and anticipating future developments, it will be of interest to a wide cross-disciplinary audience., Comment: 110 pages, many figures

Wildfires are a common problem in many areas of the world with often catastrophic consequences. A number of systems have been created to provide early warnings of wildfires, including those that use satellite data to detect fires. The increased availability of small satellites, such as CubeSats, allows the wildfire detection response time to be reduced by deploying constellations of multiple satellites over regions of interest. By using machine learned components on-board the satellites, constraints which limit the amount of data that can be processed and sent back to ground stations can be overcome. There are hazards associated with wildfire alert systems, such as failing to detect the presence of a wildfire, or detecting a wildfire in the incorrect location. It is therefore necessary to be able to create a safety assurance case for the wildfire alert ML component that demonstrates it is sufficiently safe for use. This paper describes in detail how a safety assurance case for an ML wildfire alert system is created. This represents the first fully developed safety case for an ML component containing explicit argument and evidence as to the safety of the machine learning.

Thermal emission is typically associated with a blackbody at a temperature above absolute zero, which exchanges energy with its environment in the form of radiation. Blackbody thermal emission is largely incoherent both spatially and temporally. Using principles in nanophotonics, thermal emission with characteristics that differ considerably from those of a blackbody have been demonstrated. In particular, by leveraging intrinsic properties of emerging materials or via nanostructuring at the wavelength or sub-wavelength scale, one can gain control over the directionality, temporal coherence, and other more exotic properties of thermal radiation. Typically, however, these are fixed at the time of fabrication. Gaining dynamic control of thermal emission requires exploiting external mechanisms that actively modulate radiative properties. Numerous applications can benefit from such thermal emission control, for example in solar energy harvesting, thermo-photovoltaic energy conversion, radiative cooling, sensing, spectroscopy, imaging and thermal camouflage. In this tutorial, we introduce thermal emission in two domains: the far-field, and the near-field, and we outline experimental approaches for probing thermal radiation in both ranges. We discuss ways for tailoring the spatial and temporal coherence of thermal emission and present available mechanisms to actively tune these characteristics., Comment: 19 pages, 14 figures, tutorial

The proof-of-concept prototype of the Picosecond Avalanche Detector, a multi-PN junction monolithic silicon detector with continuous gain layer deep in the sensor depleted region, was tested with a beam of 180 GeV pions at the CERN SPS. The prototype features low noise and fast SiGe BiCMOS frontend electronics and hexagonal pixels with 100 {\mu}m pitch. At a sensor bias voltage of 125 V, the detector provides full efficiency and average time resolution of 30, 25 and 17 ps in the overall pixel area for a power consumption of 0.4, 0.9 and 2.7 W/cm^2, respectively. In this first prototype the time resolution depends significantly on the distance from the center of the pixel, varying at the highest power consumption measured between 13 ps at the center of the pixel and 25 ps in the inter-pixel region.

The Picosecond Avalanche Detector is a multi-junction silicon pixel detector based on a $\mathrm{(NP)_{drift}(NP)_{gain}}$ structure, devised to enable charged-particle tracking with high spatial resolution and picosecond time-stamp capability. It uses a continuous junction deep inside the sensor volume to amplify the primary charge produced by ionizing radiation in a thin absorption layer. The signal is then induced by the secondary charges moving inside a thicker drift region. A proof-of-concept monolithic prototype, consisting of a matrix of hexagonal pixels with 100 $\mu$m pitch, has been produced using the 130 nm SiGe BiCMOS process by IHP microelectronics. Measurements on probe station and with a $^{55}$Fe X-ray source show that the prototype is functional and displays avalanche gain up to a maximum electron gain of 23. A study of the avalanche characteristics, corroborated by TCAD simulations, indicates that space-charge effects due to the large primary charge produced by the conversion of X-rays from the $^{55}$Fe source limits the effective gain.

A monolithic silicon pixel detector prototype has been produced in the SiGe BiCMOS SG13G2 130 nm node technology by IHP. The ASIC contains a matrix of hexagonal pixels with pitch of approximately 100 $\mu$m. Three analog pixels were calibrated in laboratory with radioactive sources and tested in a 180 GeV/c pion beamline at the CERN SPS. A detection efficiency of $\left(99.9^{+0.1}_{-0.2}\right)$% was measured together with a time resolution of $(36.4 \pm 0.8)$ps at the highest preamplifier bias current working point of 150 $\mu$A and at a sensor bias voltage of 160 V. The ASIC was also characterized at lower bias voltage and preamplifier current.

Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains such as healthcare , automotive and manufacturing, exhibit high degrees of autonomy and are safety critical. Establishing justified confidence in ML forms a core part of the safety case for these systems. In this document we introduce a methodology for the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). AMLAS comprises a set of safety case patterns and a process for (1) systematically integrating safety assurance into the development of ML components and (2) for generating the evidence base for explicitly justifying the acceptable safety of these components when integrated into autonomous system applications.

The TOP-IMPLART project consists of the design and implementation of a linear proton accelerator, its control and monitoring systems for the treatment of superficial and semi-deep tumors. The energy of 150 MeV (corresponding to a penetration in tissue of about 15 cm) is a milestone in design being useful for the proton therapy treatment of almost 50% of tumors based on their position and depth (including ocular melanoma, head-neck tumors, pediatric tumors, and more superficial tumors). The capability to vary the intensity on a pulse-to-pulse basis combined with an electronic feedback system allows to get the required dose uniformity (2.5%) reducing the number of re-paintings. In this paper the state of the art and the objectives of the TOP-IMPLART project are described within the framework of the progress of Protontherapy.

Movement is fundamental to life, shaping population dynamics, biodiversity patterns, and ecosystem structure. Recent advances in tracking technology have enabled fundamental questions about movement to be tackled, leading to the development of the movement ecology framework (MEF), considered a milestone in the field [1]. The MEF introduced an integrative theory of organismal movement, linking internal state, motion capacity and navigation capacity to external factors. Here, a decade later, we investigated the current state of research in the field. Using a text mining approach on >8000 peer-reviewed papers in movement ecology, we explored the main research topics, evaluated the impact of the MEF, and assessed changes in the use of technological devices, software and statistical methods. The number of publications has increased considerably and there have been major technological changes in the past decade (i.e.~increased use of GPS devices, accelerometers and video cameras, and a convergence towards R), yet we found that research focuses on the same questions, specifically, on the effect of environmental factors on movement and behavior. In practice, it appears that movement ecology research does not reflect the MEF. We call on researchers to transform the field from technology-driven to embrace interdisciplinary collaboration, in order to reveal key processes underlying movement (e.g.~navigation), as well as evolutionary, physiological and life-history consequences of particular strategies., Comment: 17 pages including figures; 7 figures

Simulation models have been informing the COVID-19 policy-making process. These models, therefore, have significant influence on risk of societal harms. But how clearly are the underlying modelling assumptions and limitations communicated so that decision-makers can readily understand them? When making claims about risk in safety-critical systems, it is common practice to produce an assurance case, which is a structured argument supported by evidence with the aim to assess how confident we should be in our risk-based decisions. We argue that any COVID-19 simulation model that is used to guide critical policy decisions would benefit from being supported with such a case to explain how, and to what extent, the evidence from the simulation can be relied on to substantiate policy conclusions. This would enable a critical review of the implicit assumptions and inherent uncertainty in modelling, and would give the overall decision-making process greater transparency and accountability., Comment: 6 pages and 2 figures

Electrochemical exfoliation is one of the most promising methods for scalable production of graphene. However, limited understanding of its Raman spectrum as well as lack of measurement standards for graphene strongly limit its industrial applications. In this work we show a systematic study of the Raman spectrum of electrochemically exfoliated graphene, produced using different electrolytes and different types of solvents in varying amounts. We demonstrate that no information on the thickness can be extracted from the shape of the 2D peak as this type of graphene is defective. Furthermore, the number of defects and the uniformity of the samples strongly depend on the experimental conditions, including post-processing. Under specific conditions, formation of short conductive trans-polyacetylene chains has been observed. Our Raman analysis provides guidance for the community on how to get information on defects coming from electrolyte, temperature and other experimental conditions, by making Raman spectroscopy a powerful metrology tool., Comment: This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in Nano Letters, American Chemical Society after peer review. To access the final edited and published work, included the SI, see DOI: 10.1021/acs.nanolett.0c00332

Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems. When these regions belong to otherwise well-represented classes, their presence and negative impact are very hard to identify. We propose an approach for the detection and mitigation of such rare subclasses in deep neural network classifiers. The new approach is underpinned by an easy-to-compute commonality metric that supports the detection of rare subclasses, and comprises methods for reducing the impact of these subclasses during both model training and model exploitation. We demonstrate our approach using two well-known datasets, MNIST's handwritten digits and Kaggle's cats/dogs, identifying rare subclasses and producing models which compensate for subclass rarity. In addition we demonstrate how our run-time approach increases the ability of users to identify samples likely to be misclassified at run-time., Comment: 8 pages, 7 Figures, 2 Tables

Optically-induced magnetic resonances in non-magnetic media have unlocked magnetic light-matter interactions and led to new technologies in many research fields. Previous proposals for the levitation of nanoscale particles without structured illumination have worked on the basis of epsilon-near-zero surfaces or anisotropic materials but these carry with them significant fabrication difficulties. We report the optical levitation of a magnetic dipole over a wide range of realistic materials, including bulk metals, thereby relieving these difficulties. The repulsion is independent of surface losses and we propose an experiment to detect this force which consists of a core-shell nanoparticle, exhibiting a magnetic resonance, in close proximity to a gold substrate under plane wave illumination. We anticipate the use of this phenomenon in new nanomechanical devices., Comment: 5 pages, 2 figures

The design of far-field radiation diagrams from combined electric and magnetic dipolar sources has recently found applications in nanophotonic metasurfaces that realize tailored reflection and refraction. Such dipolar sources also exhibit important near-field evanescent coupling properties with applications in polarimetry and quantum optics. Here we introduce a rigorous theoretical framework for engineering the angular spectra encompassing both far- and near-fields of electric and magnetic sources and develop a unified description of both free space and guided mode directional radiation. The approach uses the full parametric space of six complex-valued components of magnetic and electric dipoles in order to engineer constructive or destructive near-field interference. Such dipolar sources can be realized with dielectric or plasmonic nanoparticles. We show how a single dipolar source can be designed to achieve the selective coupling to multiple waveguide modes and far-field simultaneously with a desired amplitude, phase, and direction.

The electromagnetic field scattered by nano-objects contains a broad range of wave vectors and can be efficiently coupled to waveguided modes. The dominant contribution to scattering from subwavelength dielectric and plasmonic nanoparticles is determined by electric and magnetic dipolar responses. Here, we experimentally demonstrate spectral and phase selective excitation of Janus dipoles, sources with electric and magnetic dipoles oscillating out of phase, in order to control near-field interference and directional coupling to waveguides. We show that by controlling the polarisation state of the dipolar excitations and the excitation wavelength to adjust their relative contributions, directionality and coupling strength can be fully tuned. Furthermore, we introduce a novel spinning Janus dipole featuring cylindrical symmetry in the near and far field, which results in either omnidirectional coupling or noncoupling. Controlling the propagation of guided light waves via fast and robust near-field interference between polarisation components of a source is required in many applications in nanophotonics and quantum optics.

Matter manipulation with optical forces has become commonplace in a wide range of research fields and is epitomized by the optical trap. Calculations of optical forces on small illuminated particles typically neglect multiple scattering on nearby structures. However, this scattering can result in large recoil forces, particularly when the scattering includes directional near-field excitations. Near-field recoil forces have been studied in the case of electric, magnetic and circularly polarized dipoles, but they exist for any type of directional near-field excitation. We use the force angular spectrum as a concise and intuitive analytical expression for the force on any dipole near planar surfaces, which allows us to clearly distinguish the effect due to the dipole, and due to the surface. We relate this directly to the coupling efficiency of surface or guided modes via Fermi's golden rule. To exemplify this, a near-field force transverse to the illumination is computationally calculated for a Huygens dipole near a metallic waveguide. We believe this formalism will prove insightful for various nanomanipulation systems within areas such as nanofluidics, sensing, biotechnology and nano-assembly of nanostructures., Comment: 9 pages, 3 figures

The aim of this paper is to obtain mixed weak-type inequalities for multilinear fractional operators, extending results by F. Berra, M. Carena and G. Pradolini \cite{BCP}. We prove that, under certain conditions on the weights, there exists a constant $C$ such that $$\Bigg\| \frac{\mathcal G_{\alpha}(\vec f \,)}{v}\Bigg\|_{L^{q, \infty}(\nu v^q)} \leq C \ \prod_{i=1}^m{\|f_i\|_{L^1(u_i)}},$$ where $\mathcal G_{\alpha}(\vec f \,)$ is the multilinear maximal function $\mathcal M_{\alpha}(\vec f\,)$ that was introduced by K. Moen in \cite{M} or the multilineal fractional integral $\mathcal I_{\alpha}(\vec f \,)$. As an application a vector-valued weighted mixed inequality for $\mathcal I_{\alpha}(\vec f \,)$ will be provided as well.

Spin and orbital angular momenta (AM) of light are well studied for free-space electromagnetic fields, even nonparaxial. One of the important applications of these concepts is the information transfer using AM modes, often via optical fibers and other guiding systems. However, the self-consistent description of the spin and orbital AM of light in optical media (including dispersive and metallic cases) was provided only recently [K.Y. Bliokh et al., Phys. Rev. Lett. 119, 073901 (2017)]. Here we present the first accurate calculations, both analytical and numerical, of the spin and orbital AM, as well as the helicity and other properties, for the full-vector eigenmodes of cylindrical dielectric and metallic (nanowire) waveguides. We find remarkable fundamental relations, such as the quantization of the canonical total AM of cylindrical guided modes in the general nonparaxial case. This quantization, as well as the noninteger values of the spin and orbital AM, are determined by the generalized geometric and dynamical phases in the mode fields. Moreover, we show that the spin AM of metallic-wire modes is determined, in the geometrical-optics approximation, by the transverse spin of surface plasmon-polaritons propagating along helical trajectories on the wire surface. Our work provides a solid platform for future studies and applications of the AM and helicity properties of guided optical and plasmonic waves., Comment: 12 pages, 4 figures, to appear in Optica

Study of photonic spin-orbital interactions, which involves control of the propagation and spatial distributions of light with the polarization of electromagnetic fields, is not only important at the fundamental level but also has significant implications for functional photonic applications that require active tuning of directional light propagation. Many of the experimental demonstrations have been attributed to the spin-momentum locking characteristic of evanescent waves. In this letter, we show another property of evanescent waves: the polarization dependent direction of the imaginary part of the Poynting vector, i.e. reactive power. Based on this property, we propose a simple and robust way to tune the directional far-field scattering from nanoparticles near a surface under evanescent wave illumination by controlling linear polarization and direction of the incident light.

Controlling directionality of emission, scattering and waveguiding is an important requirement in quantum optical technology, integrated photonics and new metasurface designs, as well as radio and microwave engineering. Recently, several approaches have been developed to achieve unidirectional scattering in the far-field relying on Huygens' dipolar sources, and in waveguided optics based on spin-Hall effects involving circularly polarised electric or magnetic dipoles, all of which can be realised with plasmonic or dielectric nanoparticles. Here we show that there exists a dipolar source complimentary to Huygens' dipole, termed Janus dipole, which is not directional in the far-field, but its coupling to waveguided modes is topologically protected so that it is allowed on one side of the dipole but not on the opposite side. The near field directionality of the Huygens' dipole is also revealed and a generalised Kerker's condition for far- and near-field directionality is introduced. Circular electric and magnetic dipoles, together with Huygens' and Janus dipolar sources, form a complete set of directional dipolar sources in far- and near-field, paving the way for promising applications.

Repulsion of nanoparticles, molecules and atoms from surfaces can have important applications in nanomechanical devices, microfluidics, optical manipulation and atom optics. Here, through the solution of a classical scattering problem, we show that a dipole source can experience a robust and strong repulsive force when its near-field interacts with a two-dimensional material that has a metallic character. As an example, the case of graphene is considered, showing that a broad bandwidth of repulsion can be obtained spanning the frequency range $0<\hbar\omega<(5/3)\mu_c$, where ${\mu}_c$ is the chemical potential of graphene, tuneable electrically or by chemical doping.

In this paper we present a theorem that generalizes Sawyer's classic result about mixed weighted inequalities to the multilinear context. Let $\vec{w}=(w_1,...,w_m)$ and $\nu = w_1^\frac{1}{m}...w_m^\frac{1}{m}$, the main result of the paper sentences that under different conditions on the weights we can obtain $$\Bigg\| \frac{T(\vec f\,)(x)}{v}\Bigg\|_{L^{\frac{1}{m}, \infty}(\nu v^\frac{1}{m})} \leq C \ \prod_{i=1}^m{\|f_i\|_{L^1(w_i)}}, $$ where $T$ is a multilinear Calder\'on-Zygmund operator. To obtain this result we first prove it for the $m$-fold product of the Hardy-Littlewood maximal operator $M$, and also for $\mathcal{M}(\vec{f})(x)$: the multi(sub)linear maximal function introduced in \cite{LOPTT}. As an application we also prove a vector-valued extension to the mixed weighted weak-type inequalities of multilinear Calder\'on-Zygmund operators., Comment: 10 pages