A 14-year single-center experience evaluating sclerotherapy efficacy in lymphatic malformations.

OBJECTIVE: The aim of this study is to share our experience in treating patients with lymphatic malformations (LMs) over a span of 14 years, evaluating the efficacy and safety of sclerotherapy, particularly with the use of ethanol as sclerosant of choice. METHODS: A retrospective review of pediatric patients diagnosed and later treated for LMs between 2008 and 2022 was conducted. We collected patient demographics, LM characteristics, treatment strategies, and outcomes, including response to treatment and complications. RESULTS: The cohort included 36 patients (24 male patients), first presenting clinically at a median age of 5 months (range: 0-12 years). LMs were macrocystic (17), microcystic (3), and mixed types (16). In most patients (22), the malformation involved the cervicofacial area. Twenty-five patients underwent 54 procedures, averaging 2 procedures per patient (range: 1-13). Sclerotherapy resulted in 90% of patients exhibiting some response of the LM (P = .005). Ethanol was used in most procedures (31) and proved most efficacious, facilitating partial or complete response of the malformations in all cases compared with 72% with other sclerosants (P = .06). Sclerotherapy exhibited low complication rates among all sclerosants used (7%, P = .74). CONCLUSIONS: Sclerotherapy is a safe and effective intervention for pediatric LMs. Ethanol demonstrated comparable efficacy and safety to other sclerosants, highlighting its potential as a preferred treatment option. This study supports the tailored use of sclerotherapy, guided by a thorough understanding of the risks and benefits, to provide optimized care for patients with LMs.

Feedback Beamforming in the Time Domain.

Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands. The main objective of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture refines localization precision, a unique aspect of this approach. We conducted an in-depth analysis to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods, highlighting their benefits and limitations. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.

Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee.

BACKGROUND: Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies. OBJECTIVE: To develop and evaluate an AI algorithm for detecting effusion of any kind in knee X-rays and selected CT images and distinguishing between simple effusion and lipo-hemarthrosis indicative of intra-articular fractures. METHODS: This retrospective study analyzed post traumatic knee imaging from January 2016 to February 2023, categorizing images into lipo-hemarthrosis, simple effusion, or normal. It utilized the FishNet-150 algorithm for image classification, with class activation maps highlighting decision-influential regions. The AI's diagnostic accuracy was validated against a gold standard, based on the evaluations made by a radiologist with at least four years of experience. RESULTS: Analysis included CT images from 515 patients and X-rays from 637 post traumatic patients, identifying lipo-hemarthrosis, simple effusion, and normal findings. The AI showed an AUC of 0.81 for detecting any effusion, 0.78 for simple effusion, and 0.83 for lipo-hemarthrosis in X-rays; and 0.89, 0.89, and 0.91, respectively, in CTs. CONCLUSION: The AI algorithm effectively detects knee effusion and differentiates between simple effusion and lipo-hemarthrosis in post-traumatic patients for both X-rays and selected CT images further studies are needed to validate these results.

Artificial Intelligence for Identification of Images with Active Bleeding in Mesenteric and Celiac Arteries Angiography.

PURPOSE: The purpose of this study is to evaluate the efficacy of an artificial intelligence (AI) model designed to identify active bleeding in digital subtraction angiography images for upper gastrointestinal bleeding. METHODS: Angiographic images were retrospectively collected from mesenteric and celiac artery embolization procedures performed between 2018 and 2022. This dataset included images showing both active bleeding and non-bleeding phases from the same patients. The images were labeled as normal versus images that contain active bleeding. A convolutional neural network was trained and validated to automatically classify the images. Algorithm performance was tested in terms of area under the curve, accuracy, sensitivity, specificity, F1 score, positive and negative predictive value. RESULTS: The dataset included 587 pre-labeled images from 142 patients. Of these, 302 were labeled as normal angiogram and 285 as containing active bleeding. The model's performance on the validation cohort was area under the curve 85.0 ± 10.9% (standard deviation) and average classification accuracy 77.43 ± 4.9%. For Youden's index cutoff, sensitivity and specificity were 85.4 ± 9.4% and 81.2 ± 8.6%, respectively. CONCLUSION: In this study, we explored the application of AI in mesenteric and celiac artery angiography for detecting active bleeding. The results of this study show the potential of an AI-based algorithm to accurately classify images with active bleeding. Further studies using a larger dataset are needed to improve accuracy and allow segmentation of the bleeding.

A unified beamforming and source separation model for static and dynamic human-robot interaction.

This paper presents a unified model for combining beamforming and blind source separation (BSS). The validity of the model's assumptions is confirmed by recovering target speech information in noise accurately using Oracle information. Using real static human-robot interaction (HRI) data, the proposed combination of BSS with the minimum-variance distortionless response beamformer provides a greater signal-to-noise ratio (SNR) than previous parallel and cascade systems that combine BSS and beamforming. In the difficult-to-model HRI dynamic environment, the system provides a SNR gain that was 2.8 dB greater than the results obtained with the cascade combination, where the parallel combination is infeasible.

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning.

This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier's performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks' end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.

Assessment of glymphatic dysfunction in pediatric idiopathic intracranial hypertension: insights from quantitative diffusivity and perivascular spaces analysis-a case-control study.

Background: The impaired drainage of cerebrospinal fluid through the glymphatic system is thought to play a role in the idiopathic intracranial hypertension (IIH) pathophysiology. Limited data exist regarding the glymphatic system's involvement in pediatric patients with IIH. Therefore, the study's objective was to quantitatively evaluate alterations in parenchymal diffusivity and magnetic resonance imaging (MRI)-visible dilated perivascular spaces (PVS) as imaging indicators of glymphatic dysfunction in pediatric patients with IIH. Methods: Patients diagnosed with IIH in 2017-2022 in a single tertiary center (Sheba Medical Center, Israel) were retrospectively reviewed. Twenty-four pediatric patients were enrolled. All patients underwent clinical 3-T brain MRI. The control group included 24 age- and gender-matched healthy subjects with a normal-appearing brain on imaging. We used automatic atlas-based diffusion-weighted imaging analysis to determine regional diffusivity of the thalamus, caudate, putamen, globus pallidus, hippocampus, amygdala, and brain stem. PVS were evaluated using a semi-quantitative rating scale on T2-weighted images. Variables were compared using the Mann-Whitney test. Multivariate analysis of covariance was used to test for differences between controls and IIH patients. Results: No significant differences in regional brain diffusivity were observed between individuals with IIH and healthy controls (P=0.14-0.91 for various brain regions). The number of visible PVS was comparable between patients with IIH and the control group across all evaluated sites (P=0.12-0.74 for various brain regions). Conclusions: Pediatric IIH patients exhibited similar patterns of parenchymal diffusivity and PVS compared to age-matched controls. These findings do not support the hypothesis that the glymphatic system may play a role in the pathophysiology of pediatric IIH, although previously postulated. However, employing more sophisticated magnetic resonance (MR) techniques could enhance the sensitivity in uncovering underlying glymphatic dysfunction. Further research is warranted to validate and explore this association in larger cohorts and investigate the underlying mechanisms involved in IIH.

Performance of BeBack Crossing Catheter in Chronic Total Occlusion in Patients With Chronic Limb-Threatening Ischemia: A Single-Center Experience.

PURPOSE: To describe a single-center experience in the treatment of chronic limb-threatening ischemia (CLTI) with the application of BeBack catheter (Bentley InnoMed, Germany) in patients with arterial chronic total occlusion (CTO). MATERIALS AND METHODS: A retrospective review of patients who underwent limb revascularizations using the BeBack catheter between 2015 and 2022. All patients had an initial failed attempt using a traditional guidewire and catheter technique. Technical success was considered whenever a successful re-entry or lesion crossing using the study device was achieved. Procedural success was defined as recanalization of the occluded artery with residual stenosis of less than 30%, and improvement in ankle-brachial index (ABI) after 24 hours. A Rutherford score was assigned to each limb and affected anatomical segments and lesion length were documented. Procedural access sites and complications were noted. RESULTS: The study included 72 patients who underwent 78 procedures using the BeBack crossing catheter. Procedural success was achieved in 91% of cases, with a technical success rate of 92.3%. The most frequently involved occluded segments were the femoral and popliteal arteries. The average ABI improved from 0.59 to 0.95 after the procedure. The most used access site was the contralateral femoral, and the BeBack catheter was employed on 85 occasions. Only 1 patient suffered a severe immediate adverse effect, and during the 30-day follow-up period, 2 patients needed reintervention. Unfortunately, 3 patients died during the follow-up period. CONCLUSION: The BeBack catheter offers a viable option for the treatment of patients with chronic total occlusion, with high procedural success and a low complication rate. CLINICAL IMPACT: The BeBack catheter presents a notable advancement for clinicians managing chronic limb-threatening ischemia (CLTI) and arterial chronic total occlusion (CTO), showcasing over 90% procedural and technical success rates in this study. Its adept ability to navigate and recanalize occluded segments provides a robust alternative, especially when traditional techniques falter. This innovation may chane clinical strategies in vascular interventions, offering an efficient and reliable option, thereby potentially enhancing patient outcomes in limb revascularizations.

Deep-Learning-Based Classification of Cyclic-Alternating-Pattern Sleep Phases.

Determining the cyclic-alternating-pattern (CAP) phases in sleep using electroencephalography (EEG) signals is crucial for assessing sleep quality. However, most current methods for CAP classification primarily rely on classical machine learning techniques, with limited implementation of deep-learning-based tools. Furthermore, these methods often require manual feature extraction. Herein, we propose a fully automatic deep-learning-based algorithm that leverages convolutional neural network architectures to classify the EEG signals via their time-frequency representations. Through our investigation, we explored using time-frequency analysis techniques and found that Wigner-based representations outperform the commonly used short-time Fourier transform for CAP classification. Additionally, our algorithm incorporates contextual information of the EEG signals and employs data augmentation techniques specifically designed to preserve the time-frequency structure. The model is developed using EEG signals of healthy subjects from the publicly available CAP sleep database (CAPSLPDB) on Physionet. An experimental study demonstrates that our algorithm surpasses existing machine-learning-based methods, achieving an accuracy of 77.5% on a balanced test set and 81.8% when evaluated on an unbalanced test set. Notably, the proposed algorithm exhibits efficiency and scalability, making it suitable for on-device implementation to enhance CAP identification procedures.

Large language model (ChatGPT) as a support tool for breast tumor board.

Large language models (LLM) such as ChatGPT have gained public and scientific attention. The aim of this study is to evaluate ChatGPT as a support tool for breast tumor board decisions making. We inserted into ChatGPT-3.5 clinical information of ten consecutive patients presented in a breast tumor board in our institution. We asked the chatbot to recommend management. The results generated by ChatGPT were compared to the final recommendations of the tumor board. They were also graded independently by two senior radiologists. Grading scores were between 1-5 (1 = completely disagree, 5 = completely agree), and in three different categories: summarization, recommendation, and explanation. The mean age was 49.4, 8/10 (80%) of patients had invasive ductal carcinoma, one patient (1/10, 10%) had a ductal carcinoma in-situ and one patient (1/10, 10%) had a phyllodes tumor with atypia. In seven out of ten cases (70%), ChatGPT's recommendations were similar to the tumor board's decisions. Mean scores while grading the chatbot's summarization, recommendation and explanation by the first reviewer were 3.7, 4.3, and 4.6 respectively. Mean values for the second reviewer were 4.3, 4.0, and 4.3, respectively. In this proof-of-concept study, we present initial results on the use of an LLM as a decision support tool in a breast tumor board. Given the significant advancements, it is warranted for clinicians to be familiar with the potential benefits and harms of the technology.

Effect of General Anesthesia on MR Optic Nerve Sheath Diameter in the Pediatric Population.

BACKGROUND: Papilledema is thought to be the hallmark sign of increased intracranial pressure (ICP). Distension of the subarachnoid space within the optic nerve sheath is also commonly reported in MR studies as an indirect sign of increased ICP. HYPOTHESIS: General anesthesia and positive pressure ventilation might result in changes in optic sheath diameter (OSD) observed on clinical brain MRI. STUDY TYPE: Retrospective. POPULATION: One hundred forty-five  patients (154 MRI scans, 7.3 years ± 5.1); 97 studies in the anesthesia group (4.4 years ± 3.4) of which 22 had papilledema, and 57 in the non-anesthesia group (12.3 years ± 3.2), of which 28 had papilledema. FIELD STRENGTH/SEQUENCE: 1.5T or 3.0T volumetric T2 images. T2 images were obtained from different vendors. ASSESSMENT: OSD, optic nerve diameter (OND), and peri-optic cerebrospinal fluid (CSF) were measured manually on T2-weighted MR images for various population subgroups (with and without anesthesia; with or without papilledema). The correlation between these measurements and the clinical diagnosis of papilledema was evaluated via receiver operating characteristic (ROC) analysis. STATISTICAL TESTS: Chi-square test; Mann-Whitney Test; Spearman's test and ROCs; Interclass correlation coefficient, P = 0.05. RESULTS: General anesthesia resulted in significantly larger mean OSD in patients with or without papilledema (7.3 ± 1.0 mm vs. 6.1 ± 1.1 mm and 6.7 ± 1.0 mm vs. 5.4 ± 0.9 mm, respectively). In the non-anesthesia group, the average OSD values (6.1 ± 1.1 mm) were significantly higher in papilledema patients compared to non-papilledema patients (5.4 ± 0.9 mm), with larger peri-optic CSF rim (1.6 ± 0.4 mm vs. 1.3 ± 0.3 mm). In the anesthesia group, OND was significantly larger in papilledema patients (3.4 ± 0.4 mm vs. 3.1 ± 0.5 mm), though the average peri-optic CSF rim did not reach a significance in papilledema compared with non-papilledema patients (2.0 ± 0.3 mm vs. 1.8 ± 0.4 mm, P = 0.06). In patients with general anesthesia, peri-optic CSF rim had a limited correlation with increased ICP. DATA CONCLUSION: In the pediatric population, imaging findings of increased OSD on brain MRI might be related to general anesthesia rather than increased ICP. The interpretation of optic nerve sheath distention should be reported cautiously in conjunction with anesthesia status, especially in the pediatric population. EVIDENCE LEVEL: 4 Technical Efficacy: 5.

Fully Cross-Attention Transformer for Guided Depth Super-Resolution.

Modern depth sensors are often characterized by low spatial resolution, which hinders their use in real-world applications. However, the depth map in many scenarios is accompanied by a corresponding high-resolution color image. In light of this, learning-based methods have been extensively used for guided super-resolution of depth maps. A guided super-resolution scheme uses a corresponding high-resolution color image to infer high-resolution depth maps from low-resolution ones. Unfortunately, these methods still have texture copying problems due to improper guidance from color images. Specifically, in most existing methods, guidance from the color image is achieved by a naive concatenation of color and depth features. In this paper, we propose a fully transformer-based network for depth map super-resolution. A cascaded transformer module extracts deep features from a low-resolution depth. It incorporates a novel cross-attention mechanism to seamlessly and continuously guide the color image into the depth upsampling process. Using a window partitioning scheme, linear complexity in image resolution can be achieved, so it can be applied to high-resolution images. The proposed method of guided depth super-resolution outperforms other state-of-the-art methods through extensive experiments.

CT signs of hypovolemic shock complex in patients with non-traumatic shock.

PURPOSE: To assess the frequency of hypovolemic shock complex (HSC) signs on CT in patients who presented to the emergency department (ED) with undifferentiated non-traumatic shock. Secondary aim was to assess the correlation between HSC signs and all-cause mortality. METHODS: This retrospective, single-center study included 100 patients who underwent contrast-enhanced thoraco-abdominal CT in the ED to evaluate the etiology for non-traumatic undifferentiated shock. All patients were retrospectively assigned a shock subtype (i.e., distributive, cardiogenic, hypovolemic, obstructive, multifactorial, and unknown) based on medical records. Patients' demographics and time to all-cause mortality up to 90 days were collected. All CT studies were re-assessed for the presence of HSC signs. Correlation between HSC signs, mortality and shock subtype was assessed. RESULTS: Overall, 58% (58/100) of all patients had at least one HSC sign. Flattened inferior vena cava and adrenal hyper-enhancement were the most common HSC signs (27.3%, 27/99; in both). Overall mortality was 59% (59/100). When evaluated separately, shock liver was the only HSC sign to significantly correlate with increased mortality (84.6% vs. 55.2%, p = .04). However, patients with at least two HSC signs had a significantly higher mortality rate compared to patients without any HSC signs (73.5% vs. 45.2%, p = .017). CONCLUSION: Most patients with non-traumatic shock had at least one HSC sign. Mortality rates were significantly higher in patients with two or more HSC signs compared to patients without any signs. Patients with shock liver sign had significantly higher mortality rates.

The use of contrast enhanced thoraco-abdominal CT in patients with non-traumatic undifferentiated hemodynamic shock.

OBJECTIVE: To assess the role of thoraco-abdominal computed tomography (CT) in patients with undifferentiated shock in the emergency department (ED). Secondary aim was to assess common etiologies for undifferentiated shock. METHODS: This was a single-center, retrospective study evaluating patients with undifferentiated shock who underwent a dedicated "shock protocol" CT in the ED. CT included a non-contrast thoraco-abdominal scan followed by arterial thoraco-abdominal and abdominal portal phases. Patients' clinical records, laboratory, imaging data and all-cause 90-days mortality were collected. Patients' shock category (i.e., distributive, cardiogenic, hypovolemic, obstructive, multifactorial, and unknown) and etiology for shock were retrospectively determined based on patients' medical records. Effect of CT on patients' diagnosis and management was assessed based on physicians' reports before and after CT. RESULTS: Overall, 102 patients were scanned between December 2018 and December 2020. Distributive shock was the most prevalent type with 47.1% of all patients (48/102), followed by hypovolemic shock (17/102, 16.7%). Based on clinical data and CT reports, shock etiology was diagnosed for 89.2% (91/102) patients. Sepsis was the most common etiology in 50% of patients, followed by non-traumatic hemorrhage (15/102, 14.7%). Overall, 90-day mortality was 58.8%. Shock protocol CT led to change in differential diagnosis in 49% of patients and to change in management in 42.2%. CONCLUSION: Contrast-enhanced shock protocol CT can help in shock differentiation, assessment of etiology and in management of patients presenting to the ED with undifferentiated shock.

Constant-Beamwidth Beamforming with Concentric Ring Arrays.

Designing beampatterns with constant beamwidth over a wide range of frequencies is useful in many applications in speech, radar, sonar and communication. In this paper, we design constant-beamwidth beamformers for concentric ring arrays. The proposed beamformers utilize the circular geometry to provide improved beamwidth consistency compared to beamformers which are designed for linear sensor arrays of the same order. In the proposed configuration, all sensors on each ring share the same weight value. This constraint significantly simplifies the beamformers and reduces the hardware and computational resources required in a physical setup. Furthermore, a theoretical justification of the beamforming method is provided. We demonstrate the advantages of the proposed beamformers compared to the one-dimensional configuration in terms of directivity index, white noise gain and sidelobe attenuation.

Indoors audio classification with structure image method for simulating multi-room acoustics.

In this paper, we introduce an extension of the image method for generating room impulse responses in a structure with more than a single confined space, namely, the structure image method (StIM). The proposed method, StIM, can efficiently generate a large number of environmental examples for a structure impulse response, which is required by current deep-learning methods for many tasks, while maintaining low computational complexity. We address the integration of the environment representation, produced by StIM, into the training process, and present a framework for training deep models. We demonstrate the usage of StIM when training an audio classification model and testing with real recordings acquired by accessible day-to-day devices. StIM shows promising results for indoors audio classification, where the target sound source is not located in the same room as the microphones. StIM enables large scale simulations of multi-room acoustics with low computational complexity which is mostly beneficial for training of deep learning networks.

Continuously steerable differential beamformers with null constraints for circular microphone arrays.

Differential beamforming combined with microphone arrays can be used in a wide range of applications related to acoustic and speech signal acquisition and recovery. A practical and useful method for designing differential beamformers is the so-called null-constrained method, which was developed based on linear arrays and requires only the nulls' information from the target directivity pattern. While it is effective and easy to use, this method is found not suitable for designing steerable differential beamformers with circular arrays. This paper reexamines this technique in the context of circular differential microphone arrays. By analyzing the properties of the circular array topology, the null-constrained method is extended to include symmetric constraints, which is inherent in the design of circular arrays. This extension yields a design method for fully steerable differential beamformers that require only minimum information from the target beampattern. Simulations justify the theoretical analysis and demonstrate the good properties of the developed method.

[THE SURGERY EPIDEMIC AND TIBIAL SHAFT FRACTURES: CONSERVATIVE FUNCTIONAL TREATMENT ALGORITHM FOR TIBIAL SHAFT FRACTURE].

INTRODUCTION: With the emergence in recent years of advanced surgical methods for treatment of diaphyseal fractures of the tibia bone, there appears to be a decline in the familiarity and use of the conservative treatment based on weight bearing casts and early weight bearing. This phenomenon, dubbed "the surgery epidemic" by Dr. Sarmiento, one of the forefathers of tibial fractures treatment, refers to orthopedics surgeons' tendency to treat surgically, even in patients viable for conservative treatment. OBJECTIVES: In this study, we examined all the patients with diaphyseal tibial fracture who were treated at the Orthopedic ward at "Rambam" Hospital in the study period (2012-2016), in order to evaluate the results of the conservative functional treatment, to identify the different stages of said treatment, and to create a clear and accessible protocol for treating physicians. In addition, we sought to examine whether there is a preference for surgical treatment among physicians, even in cases where fracture characteristics, according to accepted criteria, would have allowed for conservative treatment. METHODS: Clinical and radiological evaluation of all patients who arrived with tibia bone fractures to "Rambam" hospital in the study period (2012-2106); identifying patients who fit the criteria for conservative functional treatment and were treated either conservatively or surgically. In those who were treated conservatively we documented the course of their treatment until full recovery. RESULTS: A total of 153 patients with tibial bone fracture were admitted in the study period. Of those patients, 15 were treated according to the conservative functional treatment, 33 were treated surgically despite their adherence to the conservative treatment guidelines. Of all the patients adhering to the conservative treatment criteria (48 patients), only 31.2% were treated conservatively, while 68.8% were treated surgically, unnecessarily, some would say. In other words, 25% of all the patients treated surgically for tibial bone fracture, could have been treated conservatively but instead were treated surgically with internal fixation in accordance to their surgeon's preference. DISCUSSION: In this study we observed a clear preference for surgical treatment in tibial bone fractures, even in cases where the fracture position met the accepted criteria for conservative treatment. We found that the conservative functional treatment, as practiced in our hospital, adheres to the highest standard of care. Taking into account surgery and anesthesia complications, and its added cost to the health care system, we believe it is appropriate to increase awareness among physicians to the possibility and benefits of conservative functional treatment that allows for early weight bearing and patient activity.

Window-Based Constant Beamwidth Beamformer.

Beamformers have been widely used to enhance signals from a desired direction and suppress noise and interfering signals from other directions. Constant beamwidth beamformers enable a fixed beamwidth over a wide range of frequencies. Most of the existing approaches to design constant beamwidth beamformers are based on optimization algorithms with high computational complexity and are often sensitive to microphone mismatches. Other existing methods are based on adjusting the number of sensors according to the frequency, which simplify the design, but cannot control the sidelobe level. Here, we propose a window-based technique to attain the beamwidth constancy, in which different shapes of standard window functions are applied for different frequency bins as the real weighting coefficients of microphones. Thereby, not only do we keep the beamwidth constant, but we also control the sidelobe level. Simulation results show the advantages of our method compared with existing methods, including lower sidelobe level, higher directivity factor, and higher white noise gain.

Data-Driven Tree Transforms and Metrics.

We consider the analysis of high dimensional data given in the form of a matrix with columns consisting of observations and rows consisting of features. Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality. Therefore, traditional transforms and metrics cannot be used for data organization and analysis. In this paper, our goal is to organize the data by defining an appropriate representation and metric such that they respect the smoothness and structure underlying the data. We also aim to generalize the joint clustering of observations and features in the case the data does not fall into clear disjoint groups. For this purpose, we propose multiscale data-driven transforms and metrics based on trees. Their construction is implemented in an iterative refinement procedure that exploits the co-dependencies between features and observations. Beyond the organization of a single dataset, our approach enables us to transfer the organization learned from one dataset to another and to integrate several datasets together. We present an application to breast cancer gene expression analysis: learning metrics on the genes to cluster the tumor samples into cancer sub-types and validating the joint organization of both the genes and the samples. We demonstrate that using our approach to combine information from multiple gene expression cohorts, acquired by different profiling technologies, improves the clustering of tumor samples.