Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Magnitude Estimation and Phase Recovery Using Cycle-In-Cycle GAN for Non-Parallel Speech Enhancement

For the lack of adequate paired noisy-clean speech corpus in many real scenarios, non-parallel training is a promising task for DNN-based speech enhancement methods. However, because of the severe mismatch between input and target speeches, many previous studies only focus on the magnitude spectrum estimation and remain the phase unaltered, resulting in the degraded speech quality under low signal-to-noise ratio conditions. To tackle this problem, we decouple the difficult target w.r. t. original spectrum optimization into spectral magnitude and phase, and a novel Cycle-in-Cycle generative adversarial network (dubbed CinCGAN) is proposed to jointly estimate the spectral magnitude and phase information stage by stage under unpaired data. In the first stage, we pretrain a magnitude Cycle-GAN to coarsely estimate the spectral magnitude of clean speech. In the second stage, we incorporate the pretrained CycleGAN with a complex-valued CycleGAN as a cycle-in-cycle structure to simultaneously recover phase information and refine the overall spectrum. Experimental results demonstrate that the proposed approach significantly outperforms previous baselines under non-parallel training. The evaluation on training the models with standard paired data also shows that CinCGAN achieves remarkable performance especially in reducing background noise and speech distortion.
Jointly Learning to Repair Code and Generate Commit Message

We propose a novel task of jointly repairing program codes and generating commit messages. Code repair and commit message generation are two essential and related tasks for software development. However, existing work usually performs the two tasks independently. We construct a multilingual triple dataset including buggy code, fixed code, and commit messages for this novel task. We first introduce a cascaded method with two models, one is to generate the fixed code first, and the other generates the commit message based on the fixed and original codes. We enhance the cascaded method with different training approaches, including the teacher-student method, the multi-task method, and the back-translation method. To deal with the error propagation problem of the cascaded method, we also propose a joint model that can both repair the program code and generate the commit message in a unified framework. Massive experiments on our constructed buggy-fixed-commit dataset reflect the challenge of this task and that the enhanced cascaded model and the proposed joint model significantly outperform baselines in both quality of code and commit messages.
DRL-based Slice Placement under Realistic Network Load Conditions

We propose to demonstrate a network slice placement optimization solution based on Deep Reinforcement Learning (DRL), referred to as Heuristically-controlled DRL, which uses a heuristic to control the DRL algorithm convergence. The solution is adapted to realistic networks with large scale and under non-stationary traffic conditions (namely, the network load). We demonstrate the applicability of the proposed solution and its higher and stable performance over a non-controlled DRL-based solution. Demonstration scenarios include full online learning with multiple volatile network slice placement request arrivals.
HARP: Practically and Effectively Identifying Uncorrectable Errors in Memory Chips That Use On-Die Error-Correcting Codes

Aggressive storage density scaling in modern main memories causes increasing error rates that are addressed using error-mitigation techniques. State-of-the-art techniques for addressing high error rates identify and repair bits that are at risk of error from within the memory controller. Unfortunately, modern main memory chips internally use on-die error correcting codes (on-die ECC) that obfuscate the memory controller’s view of errors, complicating the process of identifying at-risk bits (i.e., error profiling). To understand the problems that on-die ECC causes for error profiling, we analytically study how on-die ECC changes the way that memory errors appear outside of the memory chip (e.g., to the memory controller). We show that on-die ECC introduces statistical dependence between errors in different bit positions, raising three key challenges for practical and effective error profiling: on-die ECC (1) exponentially increases the number of at-risk bits the profiler must identify; (2) makes individual at-risk bits more difficult to identify; and (3) interferes with commonly-used memory data patterns that are designed to make at-risk bits easier to identify. To address the three challenges, we introduce Hybrid Active-Reactive Profiling (HARP), a new error profiling algorithm that rapidly achieves full coverage of at-risk bits based on two key insights. First, errors that on-die ECC fails to correct have two sources: (1) direct errors from raw bit errors in the data portion of the ECC word and (2) indirect errors that on-die ECC introduces when facing uncorrectable errors. Second, the maximum number of indirect errors that can occur concurrently is limited to the correction capability of on-die ECC. HARP’s key idea is to first identify all bits at risk of direct errors using existing profiling techniques with the help of small modifications to the on-die ECC mechanism. Then, a secondary ECC within the memory controller with correction capability equal to or greater than that of on-die ECC can safely identify bits at-risk of indirect errors, if and when they fail. We evaluate HARP in simulation relative to two state-of-the-art baseline error profiling algorithms. We show that HARP achieves full coverage of all at-risk bits faster (e.g., 99th-percentile coverage 20.6%/36.4%/52.9%/62.1% faster, on average, given 2/3/4/5 raw bit errors per ECC word) than the baseline algorithms, which sometimes fail to achieve full coverage. We perform a case study of how each profiler impacts the system’s overall bit error rate (BER) when using a repair mechanism to tolerate DRAM data-retention errors. We show that HARP identifies all errors faster than the best-performing baseline algorithm (e.g., by 3.7 × for a raw per-bit error probability of 0.75). We conclude that HARP effectively overcomes the three error profiling challenges introduced by on-die ECC.
Coreference Resolution for the Biomedical Domain: A Survey

Issues with coreference resolution are one of the most frequently mentioned challenges for information extraction from the biomedical literature. Thus, the biomedical genre has long been the second most researched genre for coreference resolution after the news domain, and the subject of a great deal of research for NLP in general. In recent years this interest has grown enormously leading to the development of a number of substantial datasets, of domain-specific contextual language models, and of several architectures. In this paper we review the state of-the-art of coreference in the biomedical domain with a particular attention on these most recent developments.
The JDDC 2.0 Corpus: A Large-Scale Multimodal Multi-Turn Chinese Dialogue Dataset for E-commerce Customer Service

With the development of the Internet, more and more people get accustomed to online shopping. When communicating with customer service, users may express their requirements by means of text, images, and videos, which precipitates the need for understanding these multimodal information for automatic customer service systems. Images usually act as discriminators for product models, or indicators of product failures, which play important roles in the E-commerce scenario. On the other hand, detailed information provided by the images is limited, and typically, customer service systems cannot understand the intents of users without the input text. Thus, bridging the gap of the image and text is crucial for the multimodal dialogue task. To handle this problem, we construct JDDC 2.0, a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform (JD.com), containing about 246 thousand dialogue sessions, 3 million utterances, and 507 thousand images, along with product knowledge bases and image category annotations. We present the solutions of top-5 teams participating in the JDDC multimodal dialogue challenge based on this dataset, which provides valuable insights for further researches on the multimodal dialogue task.
Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian

Developing intelligent neuromorphic solutions remains a challenging endeavor. It requires a solid conceptual understanding of the hardware's fundamental building blocks. Beyond this, accessible and user-friendly prototyping is crucial to speed up the design pipeline. We developed an open source Loihi emulator based on the neural network simulator Brian that can easily be incorporated into existing simulation workflows. We demonstrate errorless Loihi emulation in software for a single neuron and for a recurrently connected spiking neural network. On-chip learning is also reviewed and implemented, with reasonable discrepancy due to stochastic rounding. This work provides a coherent presentation of Loihi's computational unit and introduces a new, easy-to-use Loihi prototyping package with the aim to help streamline conceptualization and deployment of new algorithms.
Efficient Non-linear Calculators

A novel algorithm for producing smooth nonlinearities on digital hardware is presented. The non-linearities are inherently quadratic and have both symmetrical and asymmetrical variants. The integer (and fixed point) implementation is highly amenable for use with digital gates on an ASIC or FPGA. The implementations are multiplier-less. Scaling of the non-linear output, as required in an LSTM cell, is integrated into the implementation. This too does not require a multiplier. The non-linearities are useful as activation functions in a variety of ANN architectures. The floating point mappings have been compared with other non-linearities and have been benchmarked. Results show that these functions should be considered in the ANN design phase. The hardware resource usage of the implementations have been thoroughly investigated. Our results make a strong case for implementations in edge applications. This document summarizes the findings and serves to give a quick overview of the outcomes of our research.
Casting exploit analysis as a Weird Machine reconstruction problem

Exploits constitute malware in the form of application inputs. They take advantage of security vulnerabilities inside programs in order to yield execution control to attackers. The root cause of successful exploitation lies in emergent functionality introduced when programs are compiled and loaded in memory for execution, called `Weird Machines' (WMs). Essentially WMs are unexpected virtual machines that execute attackers' bytecode, complicating malware analysis whenever the bytecode set is unknown. We take the direction that WM bytecode is best understood at the level of the process memory layout attained by exploit execution. Each step building towards this memory layout comprises an exploit primitive, an exploit's basic building block. This work presents a WM reconstruction algorithm that works by identifying pre-defined exploit primitive-related behaviour during the dynamic analysis of target binaries, associating it with the responsible exploit segment - the WM bytecode. In this manner any analyst familiar with exploit programming will immediately recognise the reconstructed WM bytecode's semantics. This work is a first attempt at studying the feasibility of this method and focuses on web browsers when targeted by JavaScript exploits.
Query-based Adversarial Attacks on Graph with Fake Nodes

While deep neural networks have achieved great success on the graph analysis, recent works have shown that they are also vulnerable to adversarial attacks where fraudulent users can fool the model with a limited number of queries. Compared with adversarial attacks on image classiﬁcation, performing adversarial attack on graphs is challenging because of the discrete and non-differential nature of a graph. To address these issues, we proposed Cluster Attack, a novel adversarial attack by introducing a set of fake nodes to the original graph which can mislead the classiﬁcation on certain victim nodes. Speciﬁcally, we query the victim model for each victim node to acquire their most adversarial feature, which is related to how the fake node’s feature will affect the victim nodes. We further cluster the victim nodes into several subgroups according to their most adversarial features such that we can reduce the searching space. Moreover, our attack is performed in a practical and unnoticeable manner: (1) We protect the predicted labels of nodes which we are not aimed for from being changed during attack. (2) We attack by introducing fake nodes into the original graph without changing existing links and features. (3) We attack with only partial information about the attacked graph, i.e., by leveraging the information of victim nodes along with their neighbors within k -hop instead of the whole graph. (4) We perform attack with a limited number of queries about the predicted scores of the model in a black-box manner, i.e., without model architecture and parameters. Extensive experiments demonstrate the effectiveness of our method in terms of the success rate of attack.
Capping agents in nanoparticle synthesis: Surfactant and solvent system

The preparation of nanomaterials by organometallic precursors require a capping agent, which primarily acts as stabilizing agent and provide colloidal stability along with preventing agglomeration and stopping uncontrolled growth. Final morphology of nanocrystal largely depends on the type of capping agent which is adsorbed on the surface of nanocrystal. Thus capping agents are the keys to obtain the small-sized nanoparticles and are very frequently used in colloidal synthesis of nanoparticles to avoid its overgrowth.
Hospital Cultural Competency and Attributes of Patient Safety Culture: A Study of U.S. Hospitals

Objective Given the increasing racial and ethnic diversity in the United States, hospitals face challenges in providing safe and high-quality care to minority patients. Cultural competency fostered through engagement in diversity programs can be used as a resourceful strategy to provide safe care and improve the patient safety culture. This article examined the association of cultural competency and employee’s perceived attributes of safety culture. Methods A longitudinal study design was used with 283 unique hospital observations from 2014 to 2016. The dependent variables were percent composite scores for 4 attributes of perceived safety culture: (1) management support for patient safety, (2) teamwork across units, (3) communication openness, and (4) nonpunitive response to an error. The independent variable was an engagement in diversity programs, considered in 3 categories: (1) high, (2) medium, and (3) low. Controls included hospital characteristics, market characteristics, and percent. Ordinal logistic regression was used for imputation, whereas multiple linear regression was used for analyses. Results Results indicate that hospitals with high engagement have 4.64% higher perceptions of management support for safety, 3.17% higher perceptions of teamwork across units, and 3.97% higher perceptions of nonpunitive response, as compared with hospitals that have a low engagement in diversity programs (P < 0.05). Conclusions Culturally competent hospitals have better safety culture than their counterparts. Cultural competency is an important resource to build a safety culture so that safe care for patients from minority and diverse backgrounds can be delivered.
Unattended automated office blood pressure measurement in children

Abstract Purpose We studied the performance of unattended automated office blood pressure (uAOBP) measurement in children, in relation to oscillometric office BP (OBP) and ambulatory blood pressure monitoring (ABPM). Materials and Methods One hundred and eleven stable treated and untreated outpatients investigated for hypertension underwent uAOBP measurements (seated unattended in a quiet room separate from the renal clinic room, six times after a 5 min rest with the BpTRU device), and immediately before using the oscillometric device. Ambulatory 24 h blood pressure monitoring (ABPM) was performed on the same day in a subgroup of 42 children. Results UAOBP measurements were successful in 106 children (95%), 5 pre-school children did not tolerate to be alone in the room. The mean ± SD systolic/diastolic uAOBP, OBP and daytime ABP were 109.1 ± 14.0/70.8 ± 10.7 mmHg, 121.6 ± 16.5/77.6 ± 10.5 mmHg and 123.5 ± 11.3/73.7 ± 6.8 mmHg, respectively. Systolic/diastolic uAOBP was significantly lower than OBP by 13.6/7.6 mmHg (p < 0.0001) and lower than daytime ABP by 14.4 ± 0.5/2.9 ± 0.3 mmHg (p < 0.0001). The heart rate was not significantly different during uAOBP than during OBP measurements. On Bland Altman analysis the uAOBP underestimated OBP by a mean of 15.6 mmHg for systolic BP and by 8.6 mmHg for diastolic BP. In all 9 children with white-coat systolic hypertension uAOBP was within the normal range (<95th pc for OBP), in six of nine children with white-coat diastolic hypertension uAOBP was within the normal range however, in three of them it was elevated despite normal ABP. Conclusion uAOBP measurement is feasible in school-aged children, its values are considerably lower than OBP as well as daytime ABP and it could help with detection of white-coat systolic hypertension. The clinical applicability of uAOBP in children should be confirmed in further studies.
Overexpression approaches to advance understanding of Candida albicans

Candida albicans is an opportunistic fungal pathogen that is responsible for infections linked to high mortality. Loss‐of‐function approaches, taking advantage of gene knockouts or inducible down‐regulation, have been successfully used in this species in order to understand gene function. However, overexpression of a gene provides an alternative, powerful tool to elucidate gene function and identify novel phenotypes. Notably, overexpression can identify pathway components that might remain undetected using loss‐of‐function approaches. Several repressible or inducible promoters have been developed which allow to shut off or turn on the expression of a gene in C. albicans upon growth in the presence of a repressor or inducer. In this review, we summarize recent overexpression approaches used to study different aspects of C. albicans biology, including morphogenesis, biofilm formation, drug tolerance, and commensalism.
Stretching for Recovery from Groin Pain or Injury in Athletes: A Critical and Systematic Review

Stretching is usually used as part of rehabilitation protocols for groin pain or injury, but its specific contribution to and within multimodal recovery protocols is unclear. Our goal was to systematically review the effects of stretching for the recovery from groin pain or injury. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, with eligibility criteria defined according to PICOS: (Participants) athletes with groin pain or injuries; (Interventions) interventions with stretching as the differentiating factor; (Comparators) comparators not applying stretching; (Outcomes) symptom remission or improvement and/or time to return to sport and/or return to play; (Study design) randomized controlled trials. Searches were performed on 26 March 2021, in CINAHL, Cochrane Library, EBSCO, EMBASE, PEDro, PubMed, Scielo, Scopus, SPORTDiscus, and Web of Science, with no limitations regarding language or date, and no filters. Of 117 retrieved results, 65 were duplicates and 49 were excluded at the screening stage. The three articles eligible for full-text analysis failed to comply with one or more inclusion criteria (participants, intervention and/or comparators). We then went beyond the protocol and searched for non-randomized trials and case series, but no intervention was found where stretching was the differentiating factor. We found no trials specifically assessing the effects of stretching on recovery or improvement of groin pain or injury in athletes. Currently, the efficacy of these interventions is unknown, and more research is warranted.
Non‐Chromatographic Purification of Synthetic RNA Using Bio‐Orthogonal Chemistry

Solid‐phase synthesis of RNA oligonucleotides over 100 nt in length remains challenging due to the complexity of purification of the target strands from the failure sequences. This article describes a non‐chromatographic procedure that will enable routine solid‐phase synthesis and purification of long RNA strands. The optimized five‐step process is based on bio‐orthogonal inverse electron demand Diels‐Alder chemistry between trans‐cyclooctene (TCO) and tetrazine (Tz), and entails solid‐phase synthesis of RNA on a photo‐labile support. The target oligonucleotide strands are selectively tagged with Tz while on‐support. After photocleavage from the solid support, the target oligonucleotide strands can be captured and purified from the failure sequences using immobilized TCO. The approach can be applied for purification of 76‐nt long tRNA and 101‐nt long sgRNA for CRISPR experiments. Purity of the isolated oligonucleotides should be evaluated using gel electrophoresis, while functional fidelity of the sgRNA should be confirmed using CRISPR‐Cas9 experiments. © 2021 Wiley Periodicals LLC.
Impact of a Graphene Oxide Reducing Agent on a Semi-Permeable Graphene/Reduced Graphene Oxide Forward Osmosis Membrane Filtration Efficiency

Graphene has been considered as a material that may overcome the limitations of polymer semi-permeable membranes in water treatment technology. However, monolayer graphene still suffers from defects that cause leakage. Here, we report a method of sealing defects in graphene transferred onto porous polymer substrate via reduced graphene oxide (rGO). The influence of various reducing agents (e.g., vitamin C, hydrazine) on the properties of rGO was investigated by SEM, Raman, FTIR, and XRD. Subsequently, membranes based on graphene/reduced graphene oxide were tested in a forward osmosis system using sodium chloride (NaCl). The effect of the effectiveness of the reduction of graphene oxide, the type and number of attached groups, the change in the distance between the rGO flakes, and the structure of this material were examined in terms of filtration efficiency. As a result, semi-permeable centimetre-scale membranes with ion blocking efficiency of up to 90% and water flux of 20 mL h−1 m−2 bar−1 were proposed.
Visible and Thermal Image-Based Trunk Detection with Deep Learning for Forestry Mobile Robotics

Mobile robotics in forests is currently a hugely important topic due to the recurring appearance of forest wildfires. Thus, in-site management of forest inventory and biomass is required. To tackle this issue, this work presents a study on detection at the ground level of forest tree trunks in visible and thermal images using deep learning-based object detection methods. For this purpose, a forestry dataset composed of 2895 images was built and made publicly available. Using this dataset, five models were trained and benchmarked to detect the tree trunks. The selected models were SSD MobileNetV2, SSD Inception-v2, SSD ResNet50, SSDLite MobileDet and YOLOv4 Tiny. Promising results were obtained; for instance, YOLOv4 Tiny was the best model that achieved the highest AP (90%) and F1 score (89%). The inference time was also evaluated, for these models, on CPU and GPU. The results showed that YOLOv4 Tiny was the fastest detector running on GPU (8 ms). This work will enhance the development of vision perception systems for smarter forestry robots.