Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimized Sparse Array Design Based on the Sum Coarray

The number of elements of a uniform linear array (ULA) is the main bottleneck in many applications in array processing in terms of cost and power consumption. This motivates the use of sparse arrays where some of the elements are removed. However, designing a sparse array configuration with the smallest number of elements that preserves the full array beam pattern is generally NP hard. In this paper we adopt work in multiple-input-multiple-output (MIMO) radar to study a sparse array composed of two sub apertures. We derive the minimal number of elements required using this design, showing that in general there are two optimal solutions. Next, we present an extension of this approach beyond two sub apertures. By optimizing the number of sub apertures, we prove that the optimal array configuration is related to the notion of prime factorization. This allows to achieve a significant reduction in the number of elements.
An Entropy-based Method for Detection of Covert Channels over LTE

With the rapid development of mobile technologies, LTE is turning to be a wonderful carrier for covert channels. Existing detection for covert storage channel (CSC) are almost packet analysis based methods. In this paper, we present an entropy-based method for detecting CSC in Sequence Number (SN) fields of PDCP and RLC layer, which is seen as the most difficult to be detected. We simulate the LTE network in NS3 platform, and propose a Protocol Data Unit (PDU) based blind method to calculate the distance between the SN of PDU and its first left neighbor, instead of analyzing the packets or extracting the value of SN from the PDU. Our experimental results have demonstrated that the proposed detection method is sensitive to the hidden information in the SN fields of PDCP and RLC layer. It can detect them in an accurate manner, and can be conducted in both real-time online and offline storage detection.
Doppler Shift Measurement Using Complex-Valued CSI of WiFi in Corridors

This paper presents a novel approach to measure indoor Doppler shift (which is generally neglected) based on Channel State Information (CSI) using WiFi. Our approach is motivated by the fact that the CSI provided by a commercially available measurement tool is complex-valued, and therefore the Fast Fourier Transform (FFT) of CSI is not conjugate-symmetric. After establishing a CSI acquisition system, we process CSI data guided by theoretical derivation under different corridor scenarios and finally obtain the Doppler shift of different relative motions. Our results demonstrate that this method of measuring Doppler shift could be applied to further research on crowd counting or human activity recognition based on WiFi.
A Multilingual System Based on Semantic Knowledge for Complex Environments

This paper presents a system for the management of audio information based on semantic knowledge in complex environments. These environments are defined by: the limited resources (audio corpora); the poor quality of the signal (internet radio); the trilingual context (Basque, French and Spanish); the cross-lingual elements between the languages; and the critical under-resourced situation of the Basque minority language. Evolutionary methods have been applied to fulfill the specifications based on: folksonomies, automatic configuration selection, and collaborative networks. The resulting system includes a multilingual ASR engine that is able to tackle under-resourced languages with a very small audio corpus.
Generating Photorealistic Facial Expressions in Dyadic Interactions

We propose an approach for generating photorealistic facial expressions for multiple virtual identities in dyadic interactions. To this end, we study human-human interactions to model one individual’s facial expressions in the reaction of the other. We introduce a two level optimization of generative adversarial networks, wherein the ﬁrst stage generates one’s face shapes conditioned on facial action features derived from their dyadic interaction partner and the second stage synthesizes high quality face images from sketches. A ‘layer features’ L 1 regularization is employed to enhance the generation quality and an identity-constraint is utilized to ensure appearance distinction between different identities. We demonstrate that our model is effective at generating visually compelling facial expressions. Moreover, we quantitatively showed that generated agent facial expressions reﬂect valid emotional reactions to behavior of the human partner.
An improved LC-MS/MS method for simultaneous evaluation of CYP2C9, CYP2C19, CYP2D6 and CYP3A4 activity.

AIM To develop an LC-MS/MS assay to quantitate well-tolerated substrates; midazolam (CYP3A), omeprazole (CYP2C19), dextromethorphan (CYP2D6), losartan (CYP2C9) and their respective metabolites' concentrations in plasma samples. PATIENTS & METHODS A solid-phase extraction method was optimized to extract analytes of interest simultaneously from human plasma samples. The assay analyzed plasma samples collected from patients who received equal or lower than therapeutic doses of CYP substrates. RESULTS This assay was validated based on the European Medicines Agency guideline for bioanalytical method validation and was sensitive, linear, accurate and precise with acceptable recovery and matrix effects. CONCLUSION Small sample volume and dose of cytochrome P450 substrates, short-run time, using stable isotope internal standards and being cost effective are the major advantages of the assay.
Robust, Compliant Assembly via Optimal Belief Space Planning

In automated manufacturing, robots must reliably assemble parts of various geometries and low tolerances. Ideally, they plan the required motions autonomously. This poses a substantial challenge due to high-dimensional state spaces and non-linear contact-dynamics. Furthermore, object poses and model parameters, such as friction, are not exactly known and a source of uncertainty. The method proposed in this paper models the task of parts assembly as a belief space planning problem over an underlying impedance-controlled, compliant system. To solve this planning problem we introduce an asymptotically optimal belief space planner by extending an optimal, randomized, kinodynamic motion planner to nondeterministic domains. Under an expansiveness assumption we establish probabilistic completeness and asymptotic optimality. We validate our approach in thorough, simulated and realworld experiments of multiple assembly tasks. The experiments demonstrate our planner's ability to reliably assemble objects, solely based on CAD models as input.
Power Systems Topology and State Estimation by Graph Blind Source Separation

In this paper, we consider the problem of blind estimation of states and topology (BEST) in power systems. We use the linearized dc model of real power measurements with unknown voltage phases (i.e., states) and an unknown admittance matrix (i.e., topology) and show that the BEST problem can be formulated as a blind source separation (BSS) problem with a weighted Laplacian mixing matrix. We develop the constrained maximum likelihood (ML) estimator of the Laplacian matrix for this graph BSS problem with Gaussian-distributed states. The ML-BEST is shown to be only a function of the states’ second-order statistics. Since the topology recovery stage of the ML-BEST approach results in a high-complexity optimization problem, we propose two low-complexity methods to implement it: First, two-phase topology recovery, which is based on solving the relaxed convex optimization and then finding the closest Laplacian matrix, and second, augmented Lagrangian topology recovery. We derive a closed-form expression for the associated Cram$\acute{\text{e}}$r–Rao bound (CRB) on the topology matrix estimation. The performance of the proposed methods is evaluated for three case studies: the IEEE-14 bus system, the IEEE 118-bus system, and a random network, and compared with the oracle minimum mean-squared-error state estimator and with the proposed CRB.
Defining reference intervals for a serum growth differentiation factor-15 (GDF-15) assay in a Caucasian population and its potential utility in diabetic kidney disease (DKD)

Abstract Background: Growth differentiation factor-15 (GDF-15), a stress responsive cytokine, is a promising biomarker of renal functional decline in diabetic kidney disease (DKD). This study aimed primarily to establish normative data and secondarily to evaluate the potential utility of GDF-15 in DKD using Roche Diagnostics electrochemiluminescence immunoassay (ECLIA) in an Irish Caucasian population. Methods: Following informed consent, 188 healthy volunteers and 128 participants with diabetes (72 with and 56 without DKD) were recruited to a cross-sectional study. Baseline demographics, anthropometric measurements and laboratory measurements were recorded. Blood for GDF-15 measurement was collected into plain specimen tubes kept at room temperature and processed (centrifugation, separation of serum, freezing at −80 °C) within 1 h of phlebotomy pending batch analyses. Reference intervals were determined using the 2.5th and 97.5th percentiles for serum GDF-15 concentration. Results: Of 188 healthy participants, 63 failed to meet study inclusion criteria. The reference interval for serum GDF-15 was 399 ng/L (90% confidence interval [CI]: 399–399) – 1335 ng/L (90% CI: 1152–1445). Receiver operator characteristics (ROC) curve analysis for DKD determined the area under the ROC curve to be 0.931 (95% CI: 0.893–0.959; p<0.001). The optimum GDF-15 cutoff for predicting DKD was >1136 ng/L providing a diagnostic sensitivity and specificity of 94.4% and 79%, respectively, and positive likelihood ratio of 4.5:1 (95% CI: 3.4–6.0). Conclusions: The reference interval for serum GDF-15 in a healthy Irish Caucasian population using Roche Diagnostics ECLIA was established and a preliminary determination of the potential of GDF-15 as a screening test for DKD was made. Further prospective validation with a larger DKD cohort will be required before the cutoff presented here is recommended for clinical use.
Soft Miniaturized Linear Actuators Wirelessly Powered by Rotating Permanent Magnets

Wireless actuation by magnetic fields allows for the operation of untethered miniaturized devices, e.g. in biomedical applications. Nevertheless, generating large controlled forces over relatively large distances is challenging. Magnetic torques are easier to generate and control, but they are not always suitable for the tasks at hand. Moreover, strong magnetic fields are required to generate a sufficient torque, which are difficult to achieve with electromagnets. Here, we demonstrate a soft miniaturized actuator that transforms an externally applied magnetic torque into a controlled linear force. We report the design, fabrication and characterization of both the actuator and the magnetic field generator. We show that the magnet assembly, which is based on a set of rotating permanent magnets, can generate strong controlled oscillating fields over a relatively large workspace. The actuator, which is 3D-printed, can lift a load of more than 40 times its weight. Finally, we show that the actuator can be further miniaturized, paving the way towards strong, wirelessly powered microactuators.
Active Learning for Regression Tasks with Expected Model Output Changes

Annotated training data is the enabler for supervised learning. While recording data at large scale is possible in some application domains, collecting reliable annotations is time-consuming, costly, and often a project’s bottleneck. Active learning aims at reducing the annotation effort. While this ﬁeld has been studied extensively for classiﬁcation tasks, it has received less attention for regression problems although the annotation cost is often even higher. We aim at closing this gap and propose an active learning approach to enable regression applications. To address continuous outputs, we build on Gaussian process models – an established tool to tackle even non-linear regression problems. For active learning, we extend the expected model output change (EMOC) framework to continuous label spaces and show that the involved marginalizations can be solved in closed-form. This mitigates one of the major drawbacks of the EMOC principle. We empirically analyze our approach in a variety of application scenarios. In summary, we observe that our approach can ef-ﬁciently guide the annotation process and leads to better models in shorter time and at lower costs.
A strongly polynomial algorithm for linear exchange markets

We present a strongly polynomial algorithm for computing an equilibrium in Arrow-Debreu exchange markets with linear utilities. Our algorithm is based on a variant of the weakly-polynomial Duan-Mehlhorn (DM) algorithm. We use the DM algorithm as a subroutine to identify revealed edges, i.e., pairs of agents and goods that must correspond to best bang-per-buck transactions in every equilibrium solution. Every time a new revealed edge is found, we use another subroutine that decides if there is an optimal solution using the current set of revealed edges, or if none exists, finds the solution that approximately minimizes the violation of the demand and supply constraints. This task can be reduced to solving a linear program (LP). Even though we are unable to solve this LP in strongly polynomial time, we show that it can be approximated by a simpler LP with two variables per inequality that is solvable in strongly polynomial time.
Lipoprotein Sub-Fractions by Ion-Mobility Analysis and Its Association with Subclinical Coronary Atherosclerosis in High-Risk Individuals

Aims: There is limited knowledge about the association of lipoprotein particles and markers of coronary atherosclerosis such as coronary artery calcification (CAC) in relatively young high-risk persons. This study examines the association of lipoprotein subfractions and CAC in high cardiometabolic risk individuals. Methods: The study presents analysis from baseline data of a randomized trial targeted at high-risk workers. Employees of Baptist Health South Florida with metabolic syndrome or diabetes were recruited. At baseline, all 182 participants had lipoprotein subfraction analysis using the ion mobility technique and participants above 35 years (N = 170) had CAC test done. Principal components (PC) were computed for the combination of lipoprotein subclasses. Multiple bootstrapped regression analyses (BSA) were conducted to assess the relationship between lipoprotein subfractions and CAC. Results: The study population (N = 170) was largely female (84%) with a mean age of 58 years. Three PCs accounted for 88% variation in the sample. PC2, with main contributions from VLDL particles in the positive direction and large LDL particles in the negative direction was associated with a 22% increase in CAC odds (P value < 0.05 in 100% of BSA). PC3, with main contributions from HDL lipoprotein particles in the positive direction and small/medium LDL and large IDL particles in the negative direction, was associated with a 9% reduction in CAC odds (P < 0.05 in 88% of BSA). PC1, which had approximately even contributions from HDL, LDL, IDL and VLDL lipoprotein subfractions in the positive direction, was not associated with CAC. Conclusion: In a relatively young but high-risk population, a lipoprotein profile predominated by triglyceriderich lipoproteins was associated with increased risk of CAC, while one predominated by HDL lipoproteins offered modest protection. Lipoprotein sub-fraction analysis may help to further discriminate patients who require more intensive cardiovascular work-up and treatment.
Secrecy Capacity Under List Decoding For A Channel with A Passive Eavesdropper and an Active Jammer

We investigate secure communication over a channel that undergoes two different classes of attacks at the same time: passive eavesdropping and active jamming. This scenario is perfectly modeled by the concept of arbitrarily varying wiretap channels (AVWCs). We derive a full characterization of the secrecy capacity of AVWCs under list decoding. We show that the list secrecy capacity is either equivalent to the correlated random secrecy capacity or zero depending on the order of symmetrizability of the legitimate AVC. Differently from correlated random codes, our coding scheme does not assume any restrictions on the communication between the eavesdropper and the jammer.
A Spatial Domain Feature Based Approach for No Reference Image Quality Assessment of JPEG Compressed Images

This is the world of technological advancement; where emphasis is more on data transfer and that too with higher speeds. This leads to higher bandwidth requirement. To resolve the bandwidth requirement problem various compression techniques are employed. But with this compression and data transfer new problem arises that is nothing but distortions. And at the same time it is also important to assess the quality of these images being processed and transferred. This paper presents a novel approach for image quality assessment without any reference. This paper focuses on JPEG compressed images which mainly suffers blocking and blurring artifacts. A spatial domain approach is employed along with histogram to quantify the distortion. Results are found to be correlating well with the subjective image quality assessment and various standard quality assessment parameters. Results are also found to be comparable to that of full reference quality assessment technique.
An Insurance-based Incentive Mechanism for Mobile Crowdsourcing to Improve System Security

In a crowdsourcing system, security is a critical issue which affects the participation willingness of users. To motivate users' participation, most of existing work provide additional reward to compensate their loss due to security issues. However, more efficient way is to motivate the users to arm with higher security capability, to reduce the infection probability from the attackers and malicious software. In this paper, we propose an insurance-based incentive framework to motivate the users to upgrade to a higher security level. The framework can be formed as a Stackelberg game, where crowdsourcing platform is the leader and the users are followers. Through backward induction, we found that a Nash Equilibrium exists in the Stackelberg game. Simulation result shows that the proposed mechanism can enhance both social welfare, platform utility and users' utility in the crowdsourcing system.
Decreasing pediatric pain and agitation during botulinum toxin injections for spasticity with virtual reality: Lessons learned from clinical use.

PURPOSE Recurrent botulinum toxin (BoNT) injections are used in the pediatric population for management of spasticity, but the procedure is often associated with significant pain and anxiety for patients. Non-pharmacologic treatment alternatives for procedural discomfort are limited. Immersive virtual reality (VR) has shown efficacy in treatment of procedural discomfort in pediatric patients. A retrospective chart review was performed to assess feasibility and use of treating BoNT procedure-related discomfort in pediatric patients with VR. METHODS Fourteen patients undergoing elective BoNT injections viewed VR videos using a mobile VR headset during the procedure. Face, Legs, Activity, Cry, Consolability scale (FLACC) scores were obtained and feedback was elicited following injections. RESULTS The VR intervention was well tolerated, and patients' guardians requested that it be used again in 9/14 cases. The remainder either had high FLACC scores despite VR and neutral or negative guardian impressions of intervention, or were unable to utilize the device. CONCLUSION VR was helpful in reducing BoNT procedure-related discomfort in a majority of patients. Challenges with VR setup, patient tolerance, and selecting viewing experiences were identified to guide further research and use of VR in a clinical environment.
A Network Coding Approach to In-Band Control Traffic Sharing in Software Defined Networks

In-band southbound connectivity provides a cost-efficient way to share control traffic between the software-defined network controller and underlying forwarding devices; without having to worry about establishing and maintaining a separate transport network. However, the adoption of such a design also exposes the control plane traffic to non-negligible delay and losses; incurred from burdening a single transport network with an increased number of transmissions. These drawbacks can severely degrade network performance. For instance, when the requests in the form of control packets from switch-to-controller (or vice-versa) are dropped, the information may not get updated promptly and thus, the system would not behave as desired or in the worst case scenario can collapse altogether. To mitigate these constraints, in this paper, we propose the use of network coding which encourages information mixing as packets traverse the network. We show that network coding when applied to control plane traffic in an in-band design can help overcome losses due to congestion and link impairments. Evaluation results suggest that network coding aware in-band design can mitigate number of transmissions as well as total delay incurred by control packet losses.
Sequence Distillation for Purely Sequence Trained Acoustic Models

This paper presents our exploration into teacher-student (TS) training for acoustic models (AMs) based on the lattice-free maximum mutual information technique. Whereas most previous studies of TS training used a frame-level distance between teacher and student models' distributions, we propose using the sequence-level temper-atured Kullback-Leibler divergence as a metric for TS training. In our experiment on the AMI meeting corpus, we prepared a strong teacher model consisting of a convolutional neural network, time delay neural network, and long short-term memory, which had 47.7M parameters and achieved a state-of-the-art word error rate (WER) of 18.05%. Whereas the small student AM (10.8M params. and 19.72% WER) trained by a frame-level TS training was able to fill only 43% of the WER gap between teacher and student AMs, the student AM trained by the proposed method achieved a 18.23% WER, filling 89% of the WER gap from the teacher AM. We also show that the frame-level TS training sometimes even degrades the performance of the student model whereas the proposed method consistently improved the accuracy.