Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Cloud and Edge Mobile Computing in C-RAN Systems With Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.
An Efficient Dual Active-Set Solver for Embedded Model Predictive Control

—In this paper we present a reﬁned dual active-set solver for quadratic programming which has properties suitable for use in embedded model predictive control applications. In particular, the solver is efﬁcient, can easily be warm-started, and is simple to code. Moreover, the solver is reliable since its exact worst-case computational complexity can be determined ofﬂine and, by using outer proximal-point iterations, ill-conditioned problems can be handled in a robust manner.
Robust Audio-Visual Instance Discrimination

We present a self-supervised learning method to learn audio and video representations. Prior work uses the natural correspondence between audio and video to define a standard cross-modal instance discrimination task, where a model is trained to match representations from the two modalities. However, the standard approach introduces two sources of training noise. First, audio-visual correspondences often produce faulty positives since the audio and video signals can be uninformative of each other. To limit the detrimental impact of faulty positives, we optimize a weighted contrastive learning loss, which down-weighs their contribution to the overall loss. Second, since self-supervised contrastive learning relies on random sampling of negative instances, instances that are semantically similar to the base instance can be used as faulty negatives. To alleviate the impact of faulty negatives, we propose to optimize an instance discrimination loss with a soft target distribution that estimates relationships between instances. We validate our contributions through extensive experiments on action recognition tasks and show that they address the problems of audio-visual instance discrimination and improve transfer learning performance.
Postprocessing of MCMC

Markov chain Monte Carlo is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of how the output from a Markov chain is postprocessed and reported is often overlooked. Convergence diagnostics can be used to control bias via burn-in removal, but these do not account for (common) situations where a limited computational budget engenders a bias-variance trade-off. The aim of this article is to review state-of-the-art techniques for postprocessing Markov chain output. Our review covers methods based on discrepancy minimization, which directly address the bias-variance trade-off, as well as general-purpose control variate methods for approximating expected quantities of interest. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint

Wav2vec 2.0 is a state-of-the-art speech recognition model which maps speech audio waveforms into latent representations. The largest version of wav2vec 2.0 contains 317 million parameters. Hence, the inference latency of wav2vec 2.0 will be a bottleneck in production, leading to high costs and a significant environmental footprint. To improve wav2vec’s applicability to a production setting, we explore multiple model compression methods borrowed from the domain of large language models. Using a teacher-student approach, we distilled the knowledge from the original wav2vec 2.0 model into a student model, which is 2 times faster, 4.8 times smaller than the original model. More importantly, the student model is 2 times more energy efficient than the original model in terms of CO2 emission. This increase in performance is accomplished with only a 7% degradation in word error rate (WER). Our quantized model is 3.6 times smaller than the original model, with only a 0.1% degradation in WER. To the best of our knowledge, this is the first work that compresses wav2vec 2.0.
Revisiting Deep Local Descriptor for Improved Few-Shot Classification

—Few-shot classiﬁcation studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts have been aimed at designing more and more complex classiﬁers that measure similarities between query and support images, but left the importance of feature embeddings seldom explored. We show that the reliance on sophisticated classiﬁer is not necessary and a simple classiﬁer applied directly to improved feature embeddings can outperform state-of-the-art methods. To this end, we present a new method named DCAP in which we investigate how one can improve the quality of embeddings by leveraging Dense Classiﬁcation and Attentive Pooling. Speciﬁ-cally, we propose to pre-train a learner on base classes with abundant samples to solve dense classiﬁcation problem ﬁrst and then ﬁne-tune the learner on a bunch of randomly sampled few-shot tasks to adapt it to few-shot scenerio or the test time scenerio. We suggest to pool feature maps by applying attentive pooling instead of the widely used global average pooling (GAP) to prepare embeddings for few-shot classiﬁcation during meta-ﬁnetuning. Attentive pooling learns to reweight local descriptors, explaining what the learner is looking for as evidence for decision making. Experiments on two benchmark datasets show the proposed method to be superior in multiple few-shot settings while being simpler and more explainable. Code is available at: https://github.com/Ukeyboard/dcap/.
Multimodal data integration via mediation analysis with high‐dimensional exposures and mediators

Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high‐dimensional exposures and high‐dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein–structure–memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.
Electrically switchable entanglement channel in van der Waals magnets

Two dimensional layered van der Waals (vdW) magnets have demonstrated their potential to study both fundamental and applied physics due to their remarkable electronic properties. However, the connection of vdW magnets to spintronics as well as quantum information science is not clear. In particular, it remains elusive whether there are novel magnetic phenomena only belonging to vdW magnets, but absent in the widely studied crystalline magnets. Here we consider the quantum correlations of magnons in a layered vdW magnet and identify an entanglement channel of magnons across the magnetic layers, which can be effectively tuned and even deterministically switched on and off by both magnetic and electric means. This is a unique feature of vdW magnets in which the underlying physics is well understood in terms of the competing roles of exchange and anisotropy fields that contribute to the magnon excitation. Furthermore, we show that such a tunable entanglement channel can mediate the electrically controllable entanglement of two distant qubits, which also provides a protocol to indirectly measure the entanglement of magnons. Our findings provide a novel avenue to electrically manipulate the qubits and further open up new opportunities to utilize vdW magnets for quantum information science.
The Development and Validation of the Technology-Supported Reflection Inventory

Reflection is an often addressed design goal in Human-Computer Interaction (HCI) research. An increasing number of artefacts for reflection have been developed in recent years. However, evaluating if and how an interactive technology helps a user reflect is still complex. This makes it difficult to compare artefacts (or prototypes) for reflection, impeding future design efforts. To address this issue, we developed the Technology-Supported Reflection Inventory (TSRI), which is a scale that evaluates how effectively a system supports reflection. We first created a list of possible scale items based on past work in defining reflection. The items were then reviewed by experts. Next, we performed exploratory factor analysis to reduce the scale to its final length of nine items. Subsequently, we confirmed test-retest validity of our instrument, as well as its construct validity. The TSRI enables researchers and practitioners to compare prototypes designed to support reflection.
Assessing the Role of Random Forests in Medical Image Segmentation

Neural networks represent a field of research that can quickly achieve very good results in the field of medical image segmentation using a GPU. A possible way to achieve good results without GPUs are random forests. For this purpose, two random forest approaches were compared with a state-of-the-art deep convolutional neural network. To make the comparison the PhC-C2DH-U373 and the retinal imaging datasets were used. The evaluation showed that the deep convolutional neutral network achieved the best results. However, one of the random forest approaches also achieved a similar high performance. Our results indicate that random forest approaches are a good alternative to deep convolutional neural networks and, thus, allow the usage of medical image segmentation without a GPU.
V-static spaces with positive isotropic curvature

In this paper, we give a complete classification of critical metrics of the volume functional on a compact manifold M with boundary ∂M having positive isotropic curvature. We prove that for a pair (f,κ) of a nontrivial smooth function f ∶ M → R and a nonnegative real number κ, if (M,g) having positive isotropic curvature satisfies Ddf − (∆f)g − fRic = κg, then (M,g) is isometric to a geodesic ball in S when κ > 0, and either M isometric to Sn+ , or the product I × S, up to finite cover when κ = 0.
Levobupivacaine Consumption in Automated Intermittent Bolus in Ultrasound Guided Subparaneural Sciatic Nerve Catheters: A Prospective Double-Blind Randomized Trial

Purpose Continuous sciatic nerve blocks have proven benefits for postoperative analgesia after foot surgery. However, the optimal mode of administration remains a point of debate. Ultrasound guided subparaneural injection accelerates onset time and increases duration after a single shot sciatic nerve block. This double blind prospective randomized trial compares the 48-hour local anesthetic (LA) dose consumption of an automated intermittent bolus technique to a continuous infusion regimen in a subparaneural sciatic nerve catheter after hallux valgus surgery. Patients and Methods Patients scheduled for hallux valgus surgery were randomized to receive either a continuous infusion of levobupivacaine 0.125% at 5mL/h (group A) or an intermittent automated bolus of 9.8 mL every 2 hours with a background of 0.1 mL/h (group B), both with a PCA bolus of 6 mL and lockout of 30 minutes. The 48 hour LA consumption, PCA boluses, Numeric Rating Scale (NRS), satisfaction and return of normal sensation were recorded. Results Sixteen patients were excluded because of protocol violation or technical problems and 42 patients remained for analysis. The 48 hour ropivacaine consumption was higher in group A (293 ±60 mL) than group B (257±33 mL). The median and highest NRS scores and patient satisfaction were not statistically different between groups. Normal sensation returned after 75 ± 22 hours (group A) and 70 ± 17 hours (group B). Conclusion Programmed bolus administration in subparaneural sciatic nerve catheters reduces LA consumption 48 hours after surgery with equal analgesia and patient satisfaction. Return of sensation is variable and can last more than 75 hours.
Budgetary Impact of Bendamustine Ready-to-Dilute Products in Chronic Lymphocytic Leukemia and Non-Hodgkin Lymphoma to a United States Infusion Facility

Background Bendamustine hydrochloride (BND HCl) is indicated for first-line treatment of chronic lymphocytic leukemia (CLL) and rituximab-refractory indolent non-Hodgkin lymphoma (iNHL). There are two ready-to-dilute (RTD) formulations of BND HCl on the US market: a large-volume, long-duration infusion (BND-L) and a small-volume, short-duration infusion (BND-S). It is estimated that the shorter duration infusion could result in cost savings to infusion facilities. Objective Estimate the one-year budget impact between BND-S and BND-L for use in the treatment of CLL and iNHL when all current BND-L utilization is replaced with BND-S, from the US infusion facility perspective. Methods An illustrative budget impact model estimated the change in costs associated with a projected increase from 50% to 100% market share for BND-S. The model included CLL and iNHL patient populations. Budgetary costs reflected facility expenditures on drug acquisition and administration based on recommended dosing for BND-S and BND-L. The base-case model assumptions and inputs were derived from scientific literature and publicly available resources. The total budget impact was calculated annually, along with the differences in per patient cost; one-way sensitivity analyses were conducted. Results Per-patient savings with BND-S use after the utilization shift were estimated at $2812.24 for CLL and $4769.01 for iNHL. Across both indications, the total annual incremental savings after the utilization shift were estimated at $452,209 for 250 CLL and iNHL patients in a 10,000-patient infusion facility, resulting in cost savings of $150.74 per BND HCI patient per month and $1808.84 per BND HCI patient per year. The model was sensitive to changes in proportion of patients receiving BND HCI infusions for CLL and iNHL, patient body surface area, and BND-S wholesale acquisition cost. Conclusion This analysis estimated over $450,000 in annual savings for a 10,000-patient chemotherapy infusion facility following a utilization shift from 50% use of each RTD product to 100% use of BND-S in CLL and iNHL patients, driven by lower acquisition costs for BND-S and lower administration labor costs associated with rapid infusion.
Mechanical and Degradation Properties of Hybrid Scaffolds for Tissue Engineered Heart Valve (TEHV)

In addition to biocompatibility, an ideal scaffold for the regeneration of valvular tissue should also replicate the natural heart valve extracellular matrix (ECM) in terms of biomechanical properties and structural stability. In our previous paper, we demonstrated the development of collagen type I and hyaluronic acid (HA)-based scaffolds with interlaced microstructure. Such hybrid scaffolds were found to be compatible with cardiosphere-derived cells (CDCs) to potentially regenerate the diseased aortic heart valve. This paper focused on the quantification of the effect of crosslinking density on the mechanical properties under dry and wet conditions as well as degradation resistance. Elastic moduli increased with increasing crosslinking densities, in the dry and wet state, for parent networks, whereas those of interlaced scaffolds were higher than either network alone. Compressive and storage moduli ranged from 35 ± 5 to 95 ± 5 kPa and 16 ± 2 kPa to 113 ± 6 kPa, respectively, in the dry state. Storage moduli, in the dry state, matched and exceeded those of human aortic valve leaflets (HAVL). Similarly, degradation resistance increased with increasing the crosslinking densities for collagen-only and HA-only scaffolds. Interlaced scaffolds showed partial degradation in the presence of either collagenase or hyaluronidase as compared to when exposed to both enzymes together. These results agree with our previous findings that interlaced scaffolds were composed of independent collagen and HA networks without crosslinking between them. Thus, collagen/HA interlaced scaffolds have the potential to fill in the niche for designing an ideal tissue engineered heart valve (TEHV).
MicroRNAs as crucial mediators in the pharmacological activities of triptolide (Review)

Triptolide is the main bioactive constituent isolated from the Chinese herb Tripterygium wilfordii Hook F. , which possesses a variety of pharmacological properties. MicroRNAs (miRNAs/miRs) are short non-coding RNAs that regulate gene expression post-transcriptionally. miRNAs are implicated in several intracellular processes, whereby their dysregulation contributes to pathogenesis of various diseases. Thus, miRNAs have great potential as biomarkers and therapeutic targets for diseases, and are implicated in drug treatment. Previous studies have reported that specific miRNAs are targeted, and their expression levels can be altered following exposure to triptolide. Thus, miRNAs are emerging as crucial mediators in the pharmacological activities of triptolide. The present review summarizes current literature on miRNAs as target molecules in the pharmacological activities of triptolide, including antitumor, anti-inﬂammatory, immunosuppressive, renal protective, cardioprotective, antiangiogenesis activities and multiorgan toxicity effects. In addition, the diverse signaling pathways involved are discussed to provide a comprehensive understanding of the underlying molecular mechanisms of triptolide in the regulation of target miRNAs.
Complement Profiles in Patients with Amyotrophic Lateral Sclerosis: A Prospective Observational Cohort Study

Background The complement system has been suggested to be involved in the pathophysiology of amyotrophic lateral sclerosis (ALS), a progressive motor neuron disease. In the present study, we compared levels of selected complement markers to clinical outcome in ALS patients. Methods This observational, explorative cohort study included 92 ALS patients, 61 neurological controls (NCs) admitted for suspected aneurysmal subarachnoid haemorrhage, and 96 neurologically healthy controls (NHCs). Peripheral blood and cerebrospinal fluid (CSF) were obtained for the measurement of ficolin-1, −2, and −3; collectin-11, MBL, MASP-3, MAP-1, C4, C3, PTX-3, and complement activation products C4c, C3bc, and sC5b-9. We recorded clinical outcomes of ALS patients for 24 to 48 months after inclusion in order to analyse the effects of the complement markers on survival time. Results Compared with both control groups, ALS patients exhibited increased collectin-11, C4 and sC5b-9 in plasma, as well as increased ficolin-3 in CSF. Ficolin-2 was significantly decreased in plasma of the ALS patients compared with NHCs, but not with NCs. The concentration of collectin-11, C3 and C3bc correlated negatively with the revised ALS functional rating scale (ALSFRS-R). No association was found between levels of complement markers and survival as estimated by hazard ratios. Conclusion ALS patients exhibit aberrant expression of selected mediators of the lectin complement pathway as well as increased activation of the terminal complement pathway, corroborating the notion that the complement system might be involved in the pathophysiology of ALS.
Outcome of Single Dexamethasone Implant Injection in the Treatment of Persistent Diabetic Macular Edema After Anti-VEGF Treatment: Real-Life Data from a Tertiary Hospital in Jordan

Purpose To analyze the real-life clinical outcome of a single dexamethasone implant (DEX) injection in the treatment of persistent diabetic macular edema (DME) after anti-vascular endothelial growth factor (anti-VEGF) agents in a sample of the Jordanian population. Methods An observational case study design that involved a retrospective chart review analysis in a tertiary hospital in Amman, Jordan. Patients who showed persistent DME after receiving at least six doses of anti-VEGF agents for DME treatment were included. Results The study population consisted of 72 participants (29 females, 43 males) having an average age of 66 years. All patients had best-corrected visual acuity (BCVA) less than 0.7 (6/9) and SD-OCT documented center-involved DME. The study results showed that the average baseline BCVA improved from 0.205±0.1 before DEX injection to 0.358±0.1 at 3 months post-injection (p<0.0001). The central mean thickness (CMT) showed significant improvement also (539.347±132.402 to 379.041±99.430, p<0.0001). There was a mean of 3 mmHg increase in intraocular pressure at 3 months post-injection (p<0.0001), however, only 4% of patients required medical treatment. Other inflammatory biomarkers in OCT, such as intraretinal hyper-reflective dots (HRD), showed significant improvement also (23.67±16 to 14.83±13, p<0.0001). No other significant safety concerns were noticed. Conclusion A single DEX injection showed significant clinical and anatomical improvement in DME cases that are persistent after anti-VEGF treatment in our sample, with an excellent safety profile. In case of supply shortage of intravitreal injections, which occurs frequently at our center, a single DEX injection may be utilized as an effective DME therapy. Further research is mandated to identify clinical response in a larger sample and more frequent injections.