Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting MMSE Combining for Massive MIMO over Heterogeneous Propagation Channels

We consider a massive multiple-input multiple- output system with minimum-mean-squared-error processing on the uplink. A novel analytical framework is proposed to approximate the instantaneous signal-to-interference-plus-noise- ratio (SINR) of an arbitrary user terminal, as well as, the system sum spectral efficiency. Unlike previous studies, our methodology considers spatially correlated Ricean fading, with unequal Ricean K-factors, spatial correlation matrices and link gains across all terminals. Under this fully heterogeneous setting, we demonstrate that the SINR of a terminal can be tightly approximated by a linear combination of non-central chi-squared random variables, where the scaling depends on the individual link gains, K-factors, and eigenvalues of the terminal specific correlation matrices. Our approximations remain tight across the considered spatial correlation models, K-factor models, average uplink signal-to-noise-ratios and number of receive antennas. Leveraging the general form of the SINR and sum spectral efficiency, an analytical method to approximate their statistical moments is presented utilizing the moment generating function. The generality of the aforementioned analytical results is demonstrated via several special cases of practical relevance.
Open Category Detection with PAC Guarantees

Open category detection is the problem of detecting "alien" test instances that belong to categories or classes that were not present in the training data. In many applications, reliably detecting such aliens is central to ensuring the safety and accuracy of test set predictions. Unfortunately, there are no algorithms that provide theoretical guarantees on their ability to detect aliens under general assumptions. Further, while there are algorithms for open category detection, there are few empirical results that directly report alien detection rates. Thus, there are significant theoretical and empirical gaps in our understanding of open category detection. In this paper, we take a step toward addressing this gap by studying a simple, but practically-relevant variant of open category detection. In our setting, we are provided with a "clean" training set that contains only the target categories of interest and an unlabeled "contaminated" training set that contains a fraction $\alpha$ of alien examples. Under the assumption that we know an upper bound on $\alpha$, we develop an algorithm with PAC-style guarantees on the alien detection rate, while aiming to minimize false alarms. Empirical results on synthetic and standard benchmark datasets demonstrate the regimes in which the algorithm can be effective and provide a baseline for further advancements.
A Study on the Transient of Secondary Arc Current of UHV Transmission Lines

Secondary arcs are a type of alternating-current electric arc burning in open space, the arcing times of which are closely related to the transient of their currents. To elucidate the formation mechanism of the transient, the secondary current was first decomposed into the natural component and the forced component. Then, the corresponding circuit models were established and the formulas calculating these two components were derived. By means of the electromagnetic transients program simulation, the typical secondary arc current waveform is gotten, and its evolution is divided into three stages based on its characteristics. The influences of key factors (e.g., secondary arc resistance, shunt compensation degree, and neutral reactor) on the transient were investigated, and the most critical factor was obtained. It indicates that the transient of secondary arc current is mainly characterized by its natural component. Furthermore, using the Laplace transform method, and along with the simplification of the system circuit, the characteristic equation for the UHV transmission line was developed, and the damping factors and resonant frequencies of the natural component were solved. Compared with the previous steady-state analysis, the relationship between the decaying and oscillating processes with the system parameters is quantitatively explained. Rather than the line length, the neutral reactor, the decay of secondary arc current is directly proportional to the arc resistance, but inversely proportional to shunt reactor. The proposed methodology can be also applied to recovery voltage study. The results are essential for the transient analysis, the choice of neutral reactor, as well as the implementation of single-phase auto-reclosing strategy.
Moon IME: Neural-based Chinese Pinyin Aided Input Method with Customizable Association

Chinese pinyin input method engine (IME) lets user conveniently input Chinese into a computer by typing pinyin through the common keyboard. In addition to offering high conversion quality, modern pinyin IME is supposed to aid user input with extended association function. However, existing solutions for such functions are roughly based on oversimplified matching algorithms at word-level, whose resulting products provide limited extension associated with user inputs. This work presents the Moon IME, a pinyin IME that integrates the attention-based neural machine translation (NMT) model and Information Retrieval (IR) to offer amusive and customizable association ability. The released IME is implemented on Windows via text services framework.
Document Embedding Enhanced Event Detection with Hierarchical and Supervised Attention

Document-level information is very important for event detection even at sentence level. In this paper, we propose a novel Document Embedding Enhanced Bi-RNN model, called DEEB-RNN, to detect events in sentences. This model first learns event detection oriented embeddings of documents through a hierarchical and supervised attention based RNN, which pays word-level attention to event triggers and sentence-level attention to those sentences containing events. It then uses the learned document embedding to enhance another bidirectional RNN model to identify event triggers and their types in sentences. Through experiments on the ACE-2005 dataset, we demonstrate the effectiveness and merits of the proposed DEEB-RNN model via comparison with state-of-the-art methods.
Improvement of Linearity and Mitigation of Dispersion-Induced Power Fading in Multi-Channel Phase-Modulated Analog Photonic Link Based on a Polarization Modulator

We propose a multi-channel phase-modulated analog photonic link (APL) with linearization and mitigation of dispersion-induced power fading based on a polarization modulator (PolM). When the modulated signal from the PolM goes through a polarization controller and a polarization beam splitter (PBS), the odd- and even-sidebands of the phase-modulated signal can be separated into orthogonal polarization states and travel out from the two outputs of the PBS. By introducing a proper phase shift between two orthogonal polarization states, the third-order intermodulation distortion can be suppressed while the dispersion-induced power fading can be mitigated with different working frequency of the APL and different transmission length of the subchannel. Both theoretical analysis and experimental verification are given. Experimental results show the spurious free dynamic range of above 96 dB·Hz2/3 at the noise floor of –143 dBm/Hz, which is improved by 10.2 dB as compared with conventional phase-modulated APL.
Digital Information Asset Evaluation

The article discusses a model for information value assessment based on the concepts of information capacity, information utility, and information management costs. Notwithstanding that both state-of-the-art researchers and practitioners consider information as a fundamental asset, there is actually no consensus on what are the determinants of information value, particularly with regard to the increasing number of data available through digitalized processes and services. The model is applied to a case study comparing two information management projects for the improvement of core business processes of a global manufacturing company based in Italy.
The Effects of Institution-Based Trust on Community Commitments

This study investigates how institution-based trust affects community members' commitments toward their online communities. Drawing on trust and regulatory focus theory, our research model explains that institution-based trust (situational normality and structural assurance) influences members' community commitments and that members' regulatory foci (promotion- and prevention-focus) moderate the impacts of institution-based trust on community commitments. To test our research model, we surveyed 303 members of online communities. We find that structural assurance (not situational normality) positively affects members' community commitments. We also find that members' promotion-focused motivations moderate the relationship between situational normality and community commitments. This study suggests a theoretical framework to augment existing relationships of trust, motivation, and commitment research, emphasizing the role of institution-based trust provided by the community itself. This study also explains that online communities can sustain their competitive advantages with the community membership base by facilitating impersonal structures and functionalities of the community.
An Active Updating Strategy for Caching Periodic Data in the Internet of Things

Named Data Network (NDN) can cache data in the router to reduce the data transmission cost and hence can be used in the resource limited Internet of Things systems. However, there are still some problems. Particularly for periodic data, if the cached data can't be updated in real time, the data content will be outdated. However, if each cache node updates the data frequently, it will cost a lot of unnecessary network traffic. In this paper, we improve the traditional NDN routing procedure and propose an active updating strategy for periodic data to further reduce the network traffic. First, we create an Interest Record Table and modify the Content Store to store the periodic information. Then, we modify the router work-flow to allow the router node to actively update the cache data. Finally, we propose an algorithm based on user's access regularity to calculate the update time point. Experimental results indicate that the proposed approach can reduce the network traffic by about 33.8\% on average compared to the existing competing methods.
Reducing NVM Writes with Optimized Shadow Paging

Byte-addressable non-volatile memory (BNVM) technologies are closing the performance gap between traditional storage and memory. However, the integrity of persistent data structures after an unclean shutdown remains a major concern. Logging and shadow paging are commonly used to ensure consistency of BNVM systems. But both approaches can impose significant performance and energy overhead by writing extra data into BNVM. Our approach leverages the indirection of virtual memory to avoid the need for logging actual data and uses a novel cache line-level mapping mechanism to eliminate the need to write unnecessary data. Thus, our approach is able to significantly reduce the overhead of committing data to BNVM. Our preliminary evaluation results show that using OSP for transactions reduces the overhead necessary to persist data by up to 1.96× as compared to undo-log. Moreover, our approach can be used to provide fast, low-overhead persistence for hardware transactional memory, further facilitating the acceptance of BNVM into computing systems.
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global

We consider deep linear networks with arbitrary convex differentiable loss. We provide a short and elementary proof of the fact that all local minima are global minima if the hidden layers are either 1) at least as wide as the input layer, or 2) at least as wide as the output layer. This result is the strongest possible in the following sense: If the loss is convex and Lipschitz but not differentiable then deep linear networks can have sub-optimal local minima.
Three-year outcomes of small incision lenticule extraction (SMILE) and femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK) for myopia and myopic astigmatism

Aims To compare long-term clinical outcomes following small incision lenticule extraction (SMILE) and femtosecond laser-assisted laser in situ keratomileusis (FS-LASIK) for myopia and myopic astigmatism correction. Methods In this retrospective study, we enrolled a total of 101 patients (101 eyes) who underwent SMILE or FS-LASIK 3 years prior. Measured parameters included uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), manifest refraction and corneal wavefront aberrations. Results No significant differences in patient characteristics were found between the two groups. At the 3-year follow-up, UDVA was better than or equal to 20/20 in 90% and 85% (p=0.540) of the eyes; the efficacy indexes were 1.05±0.19 and 1.01±0.21 in the SMILE and FS-LASIK groups, respectively (p=0.352). Safety indexes were 1.19±0.17 and 1.15±0.20 in the SMILE and FS-LASIK groups, respectively (p=0.307). Eighty per cent and 65% of eyes were within ±0.50 D of the attempted spherical equivalent correction after SMILE and FS-LASIK, respectively (p=0.164). Vector analysis revealed no significant differences in astigmatic correction between the two groups (p>0.05). Surgically induced spherical aberration was higher in the FS-LASIK group than in the SMILE group (p<0.001). Conclusion Long-term follow-up analysis suggested that both SMILE and FS-LASIK were safe and equally effective for myopic and astigmatic correction.
Profiles of recovery from alcohol use disorder at three years following treatment: can the definition of recovery be extended to include high functioning heavy drinkers?

BACKGROUND AND AIMS Recovery from alcohol use disorder (AUD) is often narrowly defined by abstinence from alcohol and improvements in functioning (e.g. mental health, social functioning, employment). This study used latent profile analysis to examine variability in recovery outcomes, defined by alcohol use, alcohol-related problems and psychosocial functioning at 3 years following treatment. Secondary analysis investigated pre-treatment, post-treatment and 1- and 3-year post-treatment covariate predictors of the latent profiles. DESIGN Secondary analysis of data from a randomized clinical trial. SETTING United States. PARTICIPANTS We used data from the out-patient arm of Project MATCH (n = 806; 29.7% female, 22.2% non-white). MEASUREMENTS Recovery was defined by latent profile analyses including measures of psychosocial functioning and life satisfaction (Psychosocial Functioning Inventory), unemployment and mental health (Addiction Severity Index), alcohol and other drug use (Form 90) and alcohol-related consequences (Drinker Inventory of Consequences) 3 years following treatment. Mixture modeling was used to examine correlates of profiles. FINDINGS We identified four profiles at 3 years following treatment: (1) poor functioning frequent heavy drinkers, (2) poor functioning infrequent heavy drinkers, (3) high functioning occasional heavy drinkers and (4) high-functioning infrequent non-heavy drinkers. There were relatively few differences on indicators of functioning and treatment-related variables between the high functioning infrequent non-heavy drinkers and the high-functioning occasional heavy drinkers, other than high-functioning occasional heavy drinkers having lower alcohol dependence severity [odds ratio (OR) = 0.94, 95% confidence interval (CI) = 0.90, 0.98], fewer post-treatment coping skills (OR = 0.54, 95% CI = 0.32, 0.90) and lower 3-year post-treatment abstinence self-efficacy (OR = 0.37, 95% CI = 0.28, 0.49) and Alcoholics Anonymous (AA) involvement (OR = 0.87, 95% CI = 0.85, 0.99). The two high-functioning profiles showed the greatest improvements in functioning from baseline through the 3-year follow-up, whereas the low-functioning profiles showed the least amount of improvement. High-functioning occasional heavy drinkers had higher purpose in life than the poor-functioning profiles. CONCLUSIONS Some individuals who engage in heavy drinking following treatment for alcohol use disorder may function as well as those who are mostly abstinent with respect to psychosocial functioning, employment, life satisfaction and mental health.
A Wikia census: motives, tools and insights

Understanding the Wikisphere phenomenon is undoubtedly of great interest. Most of the studies focus in Wikipedia and its generalization to other wikis requires an enormous amount of work in terms of selecting and retrieving the data. To facilitate the analysis of other wikis we developed a set of tools to collect and create a census of Wikia, one of the largest and most diverse repository of wikis, which hosts more than 300,000 wikis. In this work, we carry out a preliminary quantitative analysis of the census, emphasizing on the differences between active and inactive wikis. Additionally, we provide the wiki research community with the census and the scripts employed to retrieve the data, facilitating others to reproduce or reuse it.
Truth without standard models: some conceptual problems reloaded

Abstract A theory of truth is usually demanded to be consistent, but -consistency is less frequently requested. Recently, Yatabe has argued in favour of -inconsistent first-order theories of truth, minimising their odd consequences. In view of this fact, in this paper, we present five arguments against -inconsistent theories of truth. In order to bring out this point, we will focus on two very well-known -inconsistent theories of truth: the classical theory of symmetric truth FS and the non-classical theory of naïve truth based on ᴌukasiewicz infinitely valued logic: PAᴌT.
Development and Evaluation of Talazoparib Nanoemulsion for Systemic Therapy of BRCA1-mutant Cancer

Aim: This is a debut study report on talazoparib (BMN-673)-loaded nanoemulsion (TZNE) for parenteral administration. Materials and Methods: TZNE (0.05% drug, 151.4±0.7 nm droplet size, polydispersity index of 0.120±0.010 and zeta potential of −33.30±1.22 mV) was designed, developed and characterized using in vitro studies. A cumulative in vitro release study was performed in physiological phosphate buffer solution at different pH (5.3, 6.5 and 7.4) using a dialysis method. Cytotoxicity and apoptosis assays were performed on MDA-MB-231, NCI/ADR-RES, 2008 C13, CP-70 and SKOV-3 cell lines using CellTiter® Blue. Quantitative and qualitative cell uptake was studied using fluorescent probe, coumarin-6 (C-6). Results: The drug release form TZNE nanoemulsion was slow and sustained for 24 h. Cytotoxicity and apoptosis were found to be concentration-dependent. The half-maximal inhibitory concentration of TZNE was 0.4852 and 1.35, 11.757 and 0.4696, and 1.169 and 0.7235 μM in MDA-MB-231, SKOV-3 and NCI/ADR-RES cells with 48 and 72 h incubation, respectively. Cellular uptake studies using fluorescent probe, coumarin-6 C-6, showed higher cellular uptake of TNZE compared with free C6. Results suggest that nanoemulsion could provide a new platform for systemic delivery of talazoparib.
Deep Leaf Segmentation Using Synthetic Data

Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images. In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation. We train a state-of-the-art deep learning segmentation architecture (Mask-RCNN) with a combination of real and synthetic images of Arabidopsis plants. Our proposed approach achieves 90% leaf segmentation score on the A1 test set outperforming the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge (LSC). Our approach also achieves 81% mean performance over all five test datasets.
Changes in Caregivers Lifestyle after Severe Acquired Brain Injury: A Preliminary Investigation

Introduction Severe acquired brain injury (sABI) is considered the most common cause of death and disability worldwide. sABI patients are supported by their caregivers who often exhibit high rates of psychological distress, mood disorders, and changes in relationship dynamics and family roles. Objectives To explore lifestyle changes of caregivers of sABI patients during the postacute rehabilitation, by investigating possible differences between primary and secondary caregivers. Primary caregivers spend most of the time with the patient, providing daily care and taking most responsibility for the day-to-day decisions, while secondary caregivers are those who provide additional support. Methods Three hundred forty-seven caregivers of sABI patients were asked to fill in an unpublished self-report questionnaire to explore their possible lifestyles changes. Results A statistically significant difference was found between primary and secondary caregivers in time spent in informal caregiving (p<0.001). The primary caregivers reduced all leisure activities compared to secondary carers (p<0.05). Conclusions By comparing the percentage of leisure activities performed by caregivers before and after the patient's sABI onset, all caregivers showed high percentages of changes in lifestyle and habits, even though primary caregivers reported more negative lifestyle changes than secondary caregivers. Further studies are needed to investigate needs and burden experienced by caregivers of sABI patients during the postacute rehabilitation phase, also in relation to the patients' outcome, to address support interventions for them and improve their quality of life.
Long-Time Stability and Accuracy of the Ensemble Kalman-Bucy Filter for Fully Observed Processes and Small Measurement Noise

The ensemble Kalman filter has become a popular data assimilation technique in the geosciences. However, little is known theoretically about its long term stability and accuracy. In this paper, we investigate the behavior of an ensemble Kalman-Bucy filter applied to continuous-time filtering problems. We derive mean field limiting equations as the ensemble size goes to infinity as well as uniform-in-time accuracy and stability results for finite ensemble sizes. The later results require that the process is fully observed and that the measurement noise is small. We also demonstrate that our ensemble Kalman-Bucy filter is consistent with the classic Kalman-Bucy filter for linear systems and Gaussian processes. We finally verify our theoretical findings for the Lorenz-63 system.
Accurate SHRG-Based Semantic Parsing

We demonstrate that an SHRG-based parser can produce semantic graphs much more accurately than previously shown, by relating synchronous production rules to the syntacto-semantic composition process. Our parser achieves an accuracy of 90.35 for EDS (89.51 for DMRS) in terms of elementary dependency match, which is a 4.87 (5.45) point improvement over the best existing data-driven model, indicating, in our view, the importance of linguistically-informed derivation for data-driven semantic parsing. This accuracy is equivalent to that of English Resource Grammar guided models, suggesting that (recurrent) neural network models are able to effectively learn deep linguistic knowledge from annotations.