VkD:V_kD: Improving Knowledge Distillation using Orthogonal Projections

Knowledge distillation is an effective method for training small and efficient deep learning models. However, the efficacy of a single method can degenerate when transferring to other tasks, modalities, or even other architectures. To address this limitation, we propose a novel constrained feature distillation method. This method is derived from a small set of core principles, which results in two emerging components: an orthogonal projection and a task-specific normalisation. Equipped with both of these components, our transformer models can outperform all previous methods on ImageNet and reach up to a 4.4% relative improvement over the previous state-of-the-art methods. To further demonstrate the generality of our method, we apply it to object detection and image generation, whereby we obtain consistent and substantial performance improvements over state-of-the-art. Code and models are publicly available: https://github.com/roymiles/vkdComment: CVPR 2024. Code available at https://github.com/roymiles/vk

Pedestrian Trajectory Prediction with Structured Memory Hierarchies

This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorporating a structured memory component in the human trajectory prediction pipeline to capture historical information to improve performance. We introduce structured LSTM cells for modelling the memory content hierarchically, preserving the spatiotemporal structure of the information and enabling us to capture both short-term and long-term context. We demonstrate how this architecture can be extended to integrate salient information from multiple modalities to automatically store and retrieve important information for decision making without any supervision. We evaluate the effectiveness of the proposed models on a novel multimodal dataset that we introduce, consisting of 40,000 pedestrian trajectories, acquired jointly from a radar system and a CCTV camera system installed in a public place. The performance is also evaluated on the publicly available New York Grand Central pedestrian database. In both settings, the proposed models demonstrate their capability to better anticipate future pedestrian motion compared to existing state of the art.Comment: To appear in ECML-PKDD 201

Novel Bacterial Diversity and Fragmented eDNA Identified in Hyperbiofilm-Forming Pseudomonas aeruginosa Rugose Small Colony Variant

Pseudomonas aeruginosa biofilms represent a major threat to health care. Rugose small colony variants (RSCV) of P. aeruginosa, isolated from chronic infections, display hyperbiofilm phenotype. RSCV biofilms are highly resistant to antibiotics and host defenses. This work shows that RSCV biofilm aggregates consist of two distinct bacterial subpopulations that are uniquely organized displaying contrasting physiological characteristics. Compared with that of PAO1, the extracellular polymeric substance of RSCV PAO1ΔwspF biofilms presented unique ultrastructural characteristics. Unlike PAO1, PAO1ΔwspF released fragmented extracellular DNA (eDNA) from live cells. Fragmented eDNA, thus released, was responsible for resistance of PAO1ΔwspF biofilm to disruption by DNaseI. When added to PAO1, such fragmented eDNA enhanced biofilm formation. Disruption of PAO1ΔwspF biofilm was achieved by aurine tricarboxylic acid, an inhibitor of DNA-protein interaction. This work provides critical novel insights into the contrasting structural and functional characteristics of a hyperbiofilm-forming clinical bacterial variant relative to its own wild-type strain

Probing the dynamics of an optically trapped particle by phase sensitive back focal plane interferometry

The dynamics of an optically trapped particle are often determined by measuring intensity shifts of the back-scattered light from the particle using position sensitive detectors. We present a technique which measures the phase of the back-scattered light using balanced detection in an external Mach-Zender interferometer scheme where we separate out and beat the scattered light from the bead and that from the top surface of our trapping chamber. The technique has improved axial motion resolution over intensity-based detection, and can also be used to measure lateral motion of the trapped particle. In addition, we are able to track the Brownian motion of trapped 1 and 3 $\mu$m diameter beads from the phase jitter and show that, similar to intensity-based measurements, phase measurements can also be used to simultaneously determine displacements of the trapped bead as well as the spring constant of the trap. For lateral displacements, we have matched our experimental results with a simulation of the overall phase contour of the back-scattered light for lateral displacements by using plane wave decomposition in conjunction with Mie scattering theory. The position resolution is limited by path drifts of the interferometer which we have presently reduced to obtain a displacement resolution of around 2 nm for 1.1 $\mu$m diameter probes by locking the interferometer to a frequency stabilized diode laser.Comment: 10 pages, 7 figure

Harvest Strategies for the Torres Strait Finfish fishery

The project has provided a foundation and framework for a Harvest Strategy for both Spanish mackerel and coral trout, with both fish species supported within the project by stock assessments. An update to the Spanish mackerel assessment was conducted with direct feedback between the outputs and diagnostics of the assessment informing the process of harvest strategy development. Similarly, for coral trout the initial harvest strategy resourced the first preliminary assessment of the coral trout, also funded as part of the project. Project staff worked closely with management agencies and stakeholders, using formal committee meetings inputs and advice, which fulfilled the requirements of the guidelines for developing harvest strategies. The versions of the harvest strategies presented herein are correct up the date of the submission of the report. The current versions of the harvest strategies are adaptive, as various components need checking based on updated assessments and any new information. The project team have made a series of recommendations for future updates required to progress to the full and complete harvest strategies

Efficient exploration of unknown indoor environments using a team of mobile robots

Whenever multiple robots have to solve a common task, they need to coordinate their actions to carry out the task efficiently and to avoid interferences between individual robots. This is especially the case when considering the problem of exploring an unknown environment with a team of mobile robots. To achieve efficient terrain coverage with the sensors of the robots, one first needs to identify unknown areas in the environment. Second, one has to assign target locations to the individual robots so that they gather new and relevant information about the environment with their sensors. This assignment should lead to a distribution of the robots over the environment in a way that they avoid redundant work and do not interfere with each other by, for example, blocking their paths. In this paper, we address the problem of efficiently coordinating a large team of mobile robots. To better distribute the robots over the environment and to avoid redundant work, we take into account the type of place a potential target is located in (e.g., a corridor or a room). This knowledge allows us to improve the distribution of robots over the environment compared to approaches lacking this capability. To autonomously determine the type of a place, we apply a classifier learned using the AdaBoost algorithm. The resulting classifier takes laser range data as input and is able to classify the current location with high accuracy. We additionally use a hidden Markov model to consider the spatial dependencies between nearby locations. Our approach to incorporate the information about the type of places in the assignment process has been implemented and tested in different environments. The experiments illustrate that our system effectively distributes the robots over the environment and allows them to accomplish their mission faster compared to approaches that ignore the place labels

Quantum diffusion of electromagnetic fields of ultrarelativistic spin-half particles

We compute electromagnetic fields created by a relativistic charged spin-half particle in empty space at distances comparable to the particle Compton wavelength. The particle is described as a wave packet evolving according to the Dirac equation. It produces the electromagnetic field that is essentially different from the Coulomb field due to the quantum diffusion effect.Comment: 10 pages, 10 figure

Altering river flow impacts estuarine species and catches: lessons from giant mud crabs

Anthropogenic alterations to river flow could have repercussions for flow-dependent species downstream but few studies account for these dynamic relationships or quantify impacts of altered river flow. Scylla serrata—a widely distributed portunid crab—was used as an example of a flow-dependent species to model impacts of altered flow on species abundance and catch. Crab population dynamics were modelled across a large semi-enclosed tropical sea in northern Australia. Environmental drivers, primarily river flow, but also temperature and the Southern Oscillation Index were linked to crab dynamics to explain variability in historical catches. Catch and abundance could then be predicted under altered flow scenarios. River flow significantly improved the ability to explain historical catches for some regions but not all, and the strength of this relationship varied across catchments. Altered flows had negligible effects for perennial rivers but for ephemeral and temporally variable rivers, predicted decreases in abundance and catch ranged from 36 to 46% on average. Our modelling approach showcases a way to dynamically and rigorously quantify impacts of altered river flow on a key species with potential to help inform natural resource management, including policy decisions on the timing, quantity, and method of water removed from rivers

Efficient Hardware Implementation for Maiorana-McFarland type Functions

Maiorana--McFarland type constructions are basically concatenating the truth tables of linear functions on a smaller number of variables to obtain highly nonlinear ones on larger inputs. Such functions and their different variants have significant applications in cryptology and coding theory. Straightforward hardware implementation of such functions may require exponential resources on the number of inputs. In this paper, we study such constructions in detail and provide implementation strategies for a selected subset of this class with polynomial many gates over the number of inputs. We demonstrate that such implementations cover the requirement of cryptographic primitives to a great extent. Several existing constructions are revisited in this direction and exact implementations are provided with specific depth and gate counts in the hardware implementation. Related combinatorial as well as circuit complexity-related results of theoretical nature are also analyzed in this regard. Finally we present a novel construction of a new class of balanced Boolean functions having very low absolute indicator and very high nonlinearity that can be implemented in polynomial circuit size over the number of inputs. In conclusion, we present that these constructions have immediate applications to resist the signature generation in Differential Fault Attack (DFA) and to implement functions on large number of variables in designing ciphers for the paradigm of Fully Homomorphic Encryption (FHE)