Quantum Cross Subspace Alignment Codes via the NN-sum Box Abstraction

Cross-subspace alignment (CSA) codes are used in various private information retrieval (PIR) schemes (e.g., with secure storage) and in secure distributed batch matrix multiplication (SDBMM). Using a recently developed $N$-sum box abstraction of a quantum multiple-access channel (QMAC), we translate CSA schemes over classical multiple-access channels into efficient quantum CSA schemes over a QMAC, achieving maximal superdense coding gain. Because of the $N$-sum box abstraction, the underlying problem of coding to exploit quantum entanglements for CSA schemes, becomes conceptually equivalent to that of designing a channel matrix for a MIMO MAC subject to given structural constraints imposed by the $N$-sum box abstraction, such that the resulting MIMO MAC is able to implement the functionality of a CSA scheme (encoding/decoding) over-the-air. Applications include Quantum PIR with secure and MDS-coded storage, as well as Quantum SDBMM.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0756

On the Capacity of Secure KK-user Product Computation over a Quantum MAC

Inspired by a recent study by Christensen and Popovski on secure $2$-user product computation for finite-fields of prime-order over a quantum multiple access channel (QMAC), the generalization to $K$ users and arbitrary finite fields is explored. Combining ideas of batch-processing, quantum $2$-sum protocol, a secure computation scheme of Feige, Killian and Naor (FKN), a field-group isomorphism and additive secret sharing, asymptotically optimal (capacity-achieving for large alphabet) schemes are proposed for secure $K$-user (any $K$) product computation over any finite field. The capacity of modulo-$d$ ($d\geq 2$) secure $K$-sum computation over the QMAC is found to be $2/K$ computations/qudit as a byproduct of the analysis

Double Blind TT-Private Information Retrieval

Double blind $T$-private information retrieval (DB-TPIR) enables two users, each of whom specifies an index ($\theta_1, \theta_2$, resp.), to efficiently retrieve a message $W(\theta_1,\theta_2)$ labeled by the two indices, from a set of $N$ servers that store all messages $W(k_1,k_2), k_1\in\{1,2,\cdots,K_1\}, k_2\in\{1,2,\cdots,K_2\}$, such that the two users' indices are kept private from any set of up to $T_1,T_2$ colluding servers, respectively, as well as from each other. A DB-TPIR scheme based on cross-subspace alignment is proposed in this paper, and shown to be capacity-achieving in the asymptotic setting of large number of messages and bounded latency. The scheme is then extended to $M$-way blind $X$-secure $T$-private information retrieval (MB-XS-TPIR) with multiple ($M$) indices, each belonging to a different user, arbitrary privacy levels for each index ($T_1, T_2,\cdots, T_M$), and arbitrary level of security ($X$) of data storage, so that the message $W(\theta_1,\theta_2,\cdots, \theta_M)$ can be efficiently retrieved while the stored data is held secure against collusion among up to $X$ colluding servers, the $m^{th}$ user's index is private against collusion among up to $T_m$ servers, and each user's index $\theta_m$ is private from all other users. The general scheme relies on a tensor-product based extension of cross-subspace alignment and retrieves $1-(X+T_1+\cdots+T_M)/N$ bits of desired message per bit of download.Comment: Accepted for publication in IEEE Journal on Selected Areas in Information Theory (JSAIT

Voxel-based extraction of individual pylons and wires from lidar point cloud data

Extraction of individual pylons and wires is important for modelling of 3D objects in a power line corridor (PLC) map. However, the existing methods mostly classify points into distinct classes like pylons and wires, but hardly into individual pylons or wires. The proposed method extracts standalone pylons, vegetation and wires from LiDAR data. The extraction of individual objects is needed for a detailed PLC mapping. The proposed approach starts off with the separation of ground and non ground points. The non-ground points are then classified into vertical (e.g., pylons and vegetation) and non-vertical (e.g., wires) object points using the vertical profile feature (VPF) through the binary support vector machine (SVM) classifier. Individual pylons and vegetation are then separated using their shape and area properties. The locations of pylons are further used to extract the span points between two successive pylons. Finally, span points are voxelised and alignment properties of wires in the voxel grid is used to extract individual wires points. The results are evaluated on dataset which has multiple spans with bundled wires in each span. The evaluation results show that the proposed method and features are very effective for extraction of individual wires, pylons and vegetation with 99% correctness and 98% completeness

Creating a capture zone in microfluidic flow greatly enhances the throughput and efficiency of cancer detection

Efficient capture of rare circulating tumor cells (CTCs) from blood samples is valuable for early cancer detection to improve the management of cancer. In this work, we developed a highly efficient microfluidics-based method for detecting CTCs in human blood. This is achieved by creating separate capture and flow zones in the microfluidic device (ZonesChip) and using patterned dielectrophoretic force to direct cells from the flow zone into the capture zone. This separation of the capture and flow zones minimizes the negative impact of high flow speed (and thus high throughput) and force in the flow zone on the capture efficiency, overcoming a major bottleneck of contemporary microfluidic approaches using overlapping flow and capture zones for CTC detection. When the flow speed is high (≥0.58 mm/s) in the flow zone, the separation of capture and flow zones in our ZonesChip could improve the capture efficiency from ∼0% (for conventional device without separating the two zones) to ∼100%. Our ZonesChip shows great promise as an effective platform for the detection of CTCs in blood from patients with early/localized-stage colorectal tumors

Carrier Phase Estimation Through the Rotation Algorithm for 64-QAM Optical Systems

A novel low-complexity two-stage digital feedforward carrier phase estimation algorithm based on the rotation of constellation points to remove phase modulation for a 64-ary quadrature amplitude modulation (QAM) system is proposed and analyzed both experimentally and through numerical simulations. The first stage is composed of a Viterbi and Viterbi (V&V) block, based on either the standard quadrature phase shift keying (QPSK) partitioning algorithm using only Class-1 symbols or a modified QPSK partitioning scheme utilizing both Class-1 and outer most triangle-edge (TE) symbols. The second stage applies the V&V algorithm after the removal of phase modulation through rotation of constellation points. Comparison of the proposed scheme with constellation transformation, blind phase search (BPS) and BPS+MLE (maximum likelihood estimation) algorithm is also shown. For an OSNR penalty of 1 dB at bit error rate of 1e−2 , the proposed scheme can tolerate a linewidth times symbol duration product (Δν · Ts) equal to 3.7 × 1e−5 , making it possible to operate 32-GBd optical 64-QAM systems with current commercial tunable laser

Experimental Validation of Microwave Tomographywith the DBIM-TwIST Algorithm for Brain StrokeDetection and Classification

We present an initial experimental validation of a microwave tomography (MWT) prototype for brain stroke detection and classification using the distorted Born iterative method, two-step iterative shrinkage thresholding (DBIM-TwIST) algorithm. The validation study consists of first preparing and characterizing gel phantoms which mimic the structure and the dielectric properties of a simplified brain model with a haemorrhagic or ischemic stroke target. Then, we measure the S-parameters of the phantoms in our experimental prototype and process the scattered signals from 0.5 to 2.5 GHz using the DBIM-TwIST algorithm to estimate the dielectric properties of the reconstruction domain. Ourresultsdemonstratethatweareabletodetectthestroketargetinscenarios where the initial guess of the inverse problem is only an approximation of the true experimental phantom. Moreover, the prototype can differentiate between haemorrhagic and ischemic strokes based on the estimation of their dielectric properties

The effect of training intensity on implicit learning rates in schizophrenia

Cognitive impairments in learning and memory are core symptoms of schizophrenia, associated with reduced self-reported quality of life. The most effective treatment of cognitive impairments is drill and practice cognitive training. Still, to date no study has investigated the effect of varying the frequency of training on cognitive outcomes. Here we utilized a verbal memory based language learning task, tapping into implicit cognitive processes, to investigate the role of training intensity on learning rates in individuals with schizophrenia. Data from 47 participants across two studies was utilized, one with a daily training regimen over 5 days and the other with a more intensive schedule of 5 sessions delivered over 2 days. The primary outcome measure was the change in implicit learning performance across five sessions, quantified with the Matthews Correlation Coefficient (MCC). Participants in the daily training group showed improved performance compared to the intensive group only at session 4. This is the first study to show that implicit learning rates are influenced by training intensity, with daily sessions outperforming a more intensive regimen; a period of consolidation overnight may be necessary to optimize cognitive training for individuals with schizophrenia

Efficient Scene Text Detection with Textual Attention Tower

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-attention mechanism is adopted to suppress false positive detections. Experiments on public benchmarks including ICDAR 2013, ICDAR 2015 and MSRA-TD500 show that our proposed approach can achieve better or comparable performances with fewer parameters and less computational cost.Comment: Accepted by ICASSP 202