Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hierarchical Encoder-Decoder Model for Statistical Parametric Speech Synthesis

Current approaches to statistical parametric speech synthesis using Neural Networks generally require input at the same temporal resolution as the output, typically a frame every 5ms, or in some cases at waveform sampling rate. It is therefore necessary to fabricate highly-redundant frame-level (or sample-level) linguistic features at the input. This paper proposes the use of a hierarchical encoder-decoder model to perform the sequence-to-sequence regression in a way that takes the input linguistic features at their original timescales, and preserves the relationships between words, syllables and phones. The proposed model is designed to make more effective use of supra-segmental features than conventional architectures, as well as being computationally efﬁcient. Experiments were conducted on prosodically-varied audiobook material because the use of supra-segmental features is thought to be particularly important in this case. Both objective measures and results from subjective listening tests, which asked listeners to focus on prosody, show that the proposed method performs signiﬁcantly better than a conventional architecture that requires the linguistic input to be at the acoustic frame rate. We provide code and a recipe to enable our system to be reproduced using the Merlin toolkit.
Knowledge formation and dialogue using the KRAKEN toolset

The KRAKEN toolset is a comprehensive interface for knowledge acquisition that operates in conjunction with the Cyc knowledge base. The KRAKEN system is designed to allow subject-matter experts to make meaningful additions to an existing knowledge base, without the benefit of training in the areas of artificial intelligence, ontology development, or logical representation. Users interact with KRAKEN via a natural-language interface, which translates back and forth between English and the KB's logical representation language. A variety of specialized tools are available to guide users through the process of creating new concepts, stating facts about those concepts, and querying the knowledge base. KRAKEN has undergone two independent performance evaluations. In this paper we describe the general structure and several of the features of KRAKEN, focussing on key aspects of its functionality in light of the specific knowledge-formation and acquisition challenges they are intended to address.
Predictive value of fundus autofluorescence for development of geographic atrophy in age-related macular degeneration.

PURPOSE It has been suggested that lipofuscin accumulation, as measured by increased fundus autofluorescence (FAF), precedes progression or development of junctional zone geographic atrophy (GA) in age-related macular degeneration (AMD). The tools of biomedical image analysis were used to measure the probabilistic relationship of GA progression to increased FAF. METHODS Serial AF images of eight eyes of six patients with AMD with GA were registered on computer. The images were leveled with a 12-zone quadratic polynomial mathematical model to minimize background variability. Semiautomated segmentation of GA was performed on the leveled images. Increased FAF was defined as a gray level greater than 2 standard deviations above the leveled image mean, identified on the initial image with automated segmentation, and measured as a fraction of the 250-microm border zone surrounding the initial GA lesion. Areas of GA lesions were identified on the final image. The positive predictive value (PPV) of increased FAF was determined as the probability that any pixel with increased FAF in the initial image would become part of new GA in the final image. Relative PPV was determined relative to the total quantity of new GA. The NPV (NPV) of increased FAF was calculated as the probability that any pixels without increased FAF would not become atrophic. The relative NPV was determined similarly. A similar analysis was also conducted with a 500-microm border zone to determine the predictive value of proximity to the original GA lesion ("proximity") for GA progression. RESULTS As a fraction of the geographic atrophy border zone, the mean new GA was 0.44+/-0.20, and the mean increased FAF was 0.06+/-0.06. The mean PPV of increased FAF for new GA formation was 0.50+/-0.26. Compared with the relative PPV of chance of 1.0, the mean relative PPV of increased FAF was 1.15+/-0.28. The mean NPV of increased FAF was 0.57+/-0.20. The mean relative NPV of increased FAF was 1.00+/-0.02. In the 500-microm border zone, the mean relative PPV of FAF and of proximity were essentially equal (1.56+/-.70 and 1.52+/-0.26, respectively), whereas the mean relative NPV of proximity was significantly greater than that of FAF (1.26+/-0.19 and 1.01+/-0.01, respectively, P=0.02) CONCLUSIONS The results of digital image analysis suggest that although increased FAF may have a modest PPV for new GA development, the relative PPV is generally no greater than chance. Similarly, the relative NPV demonstrates negligible difference from chance and is also lower than the relative NPV of proximity. This suggests that increased FAF, though a disease manifestation, is not a strong risk factor for development or extension of GA.
The FOXM1 Inhibitor RCM-1 Decreases Carcinogenesis and Nuclear β-Catenin

The oncogenic transcription factor FOXM1 has been previously shown to play a critical role in carcinogenesis by inducing cellular proliferation in multiple cancer types. A small-molecule compound, Robert Costa Memorial drug-1 (RCM-1), has been recently identified from high-throughput screen as an inhibitor of FOXM1 in vitro and in mouse model of allergen-mediated lung inflammation. In the present study, we examined antitumor activities of RCM-1 using tumor models. Treatment with RCM-1 inhibited tumor cell proliferation as evidenced by increased cell-cycle duration. Confocal imaging of RCM-1–treated tumor cells indicated that delay in cellular proliferation was concordant with inhibition of FOXM1 nuclear localization in these cells. RCM-1 reduced the formation and growth of tumor cell colonies in the colony formation assay. In animal models, RCM-1 treatment inhibited growth of mouse rhabdomyosarcoma Rd76-9, melanoma B16-F10, and human H2122 lung adenocarcinoma. RCM-1 decreased FOXM1 protein in the tumors, reduced tumor cell proliferation, and increased tumor cell apoptosis. RCM-1 decreased protein levels and nuclear localization of β-catenin, and inhibited protein–protein interaction between β-catenin and FOXM1 in cultured tumor cells and in vivo. Altogether, our study provides important evidence of antitumor potential of the small-molecule compound RCM-1, suggesting that RCM-1 can be a promising candidate for anticancer therapy.
Disasters and Recoveries

Twenty years ago, Thomas A. Rietz (1988) showed that infrequent, large drops in consumption make the theoretical equity premium large. Recent research has resurrected this disasterexplanation of the equity premium puzzle. Robert J. Barro (2006) measures disasters during the XXth century, and nds that they are frequent and large enough, and stock returns low enough relative to bond returns during disasters, to make this explanation quantitatively plausible. Xavier Gabaix (2007) extends the model to incorporate a time-varying incidence of disasters, and he argues that this simple feature can resolve many asset pricing puzzles. These papers make the simplifying assumption that disasters are permanent. Mathematically, they model log consumption per capita as following a unit root process plus a Poisson jump. However a casual look at the data suggests that disasters are often followed by recoveries. The rst contribution of this paper is to measure recoveries and introduce recoveries in the Barro-Rietz model. I nd that the e¤ect of recoveries hinges on the intertemporal elasticity of substitution (IES): when the IES is low, recoveries may increase the equity premium implied by the model; but when it is high, the opposite happens. A second contribution of the paper is to study additional implications of the disaster model. I thank Robert Barro, Xavier Gabaix, Ian Martin, Romain Ranciere, Adrien Verdelhan, and participants in a BU macro lunch for discussions or comments. Contact information: Boston University, Department of Economics, 270 Bay State Road, Boston MA 02215. Email: fgourio@bu.edu. Tel. : (617) 353 4534.
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We introduce a fixedpoint algorithm for verifying safety properties of hybrid systems with differential equations that have right-hand sides that are polynomials in the state variables. In order to verify non-trivial systems without solving their differential equations and without numerical errors, we use a continuous generalization of induction, for which our algorithm computes the required differential invariants. As a means for combining local differential invariants into global system invariants in a sound way, our fixedpoint algorithm works with a compositional verification logic for hybrid systems. To improve the verification power, we further introduce a saturation procedure that refines the system dynamics successively with differential invariants until safety becomes provable. By complementing our symbolic verification algorithm with a robust version of numerical falsification, we obtain a fast and sound verification procedure. We verify roundabout maneuvers in air traffic management and collision avoidance in train control.
The use of MBTI in Software Engineering

In this paper we evaluate the use of Carl Jung’s theories of Psychological Type assessed using the Myer-Briggs Type Indicator in the Software Engineering field. The current level of implementation and its quality is established and the results discussed to provide insight into what we currently know, and suggestions on what could be important to investigate for the future. Upon gathering MBTI data from a range of sources it is apparent that there is agreement on the types of personalities often discovered inside software engineering. Thinking and judging personality preferences are commonly found, while feeling and perceiving is far less common. This differs substantially from results representative of the American population, and supports the belief that software engineers are more commonly represented by specific types of people. However, there is discrepancy between four of the 16 types identified in the MBTI, suggesting that there is still some understanding to be gained about personality in software engineering, and we do not by any means know the exact breakdown of types present within the industry.
Multi-objective evolutionary design of fuzzy rule-based systems

This paper clearly demonstrates advantages of our evolutionary multiobjective optimization approach to the design of fuzzy rule-based classification systems over single-objective methods. The main advantage of our approach is that a large number of tradeoff (i.e., nondominated) fuzzy rule-based systems can be obtained by its single run with respect to conflicting objectives: accuracy maximization and complexity minimization. By analyzing the obtained fuzzy rule-based systems, the decision maker can understand the tradeoff between these two objectives. Such understanding is of great help when the decision maker chooses a final compromise fuzzy rule-based system. In the case of single-objective methods, only a single fuzzy rule-based system is obtained based on the pre-specified preference of the decision maker. We compare four formulations of genetic algorithm-based rule selection through computational experiments on well-known benchmark data sets. The four formulations have two objectives, their weighted sum, three objectives, and their weighted sum, respectively.
Minimizing User Vulnerability and Retaining Social Utility in Social Media

Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breach with dire consequences. In our recent work, we show that it is feasible to measure user vulnerability and reduce one’s vulnerability without changing the structure of a social networking site. The approach is to unfriend one’s most vulnerable friends. However, when such a vulnerable friend is also socially important, unfriending him would significantly reduce one’s own social status. In this work, we address the problem of vulnerability minimization with social utility constraints. We formally formulate the optimization problem, propose an approximation algorithm with a proven bound, and conduct empirical experiments with different forms of social utility on a large-scale Facebook dataset for performance evaluation and comparison.
A Simpler and More Generalizable Story Detector using Verb and Character Features

Story detection is the task of determining whether or not a unit of text contains a story. Prior approaches achieved a maximum performance of 0.66 F1, and did not generalize well across different corpora. We present a new state-of-the-art detector that achieves a maximum performance of 0.75 F1 (a 14% improvement), with significantly greater generalizability than previous work. In particular, our detector achieves performance above 0.70 F1 across a variety of combinations of lexically different corpora for training and testing, as well as dramatic improvements (up to 4,000%) in performance when trained on a small, disfluent data set. The new detector uses two basic types of features–ones related to events, and ones related to characters–totaling 283 specific features overall; previous detectors used tens of thousands of features, and so this detector represents a significant simplification along with increased performance.
Quantification of Software Changes through Probabilistic Symbolic Execution (N)

Characterizing software changes is fundamental for software maintenance. However existing techniques are imprecise leading to unnecessary maintenance efforts. We introduce a novel approach that computes a precise numeric characterization of program changes, which quantifies the likelihood of reaching target program events (e.g., assert violations or successful termination) and how that evolves with each program update, together with the percentage of inputs impacted by the change. This precise characterization leads to a natural ranking of different program changes based on their probability of execution and their impact on target events. The approach is based on model counting over the constraints collected with a symbolic execution of the program, and exploits the similarity between program versions to reduce cost and improve the quality of analysis results. We implemented our approach in the Symbolic PathFinder tool and illustrate it on several Java case studies, including the evaluation of different program repairs, mutants used in testing, or incremental analysis after a change.
Pharmacokinetics, optimal dosing, and safety of linezolid in children with multidrug-resistant tuberculosis: Combined data from two prospective observational studies

Background Linezolid is increasingly important for multidrug-resistant tuberculosis (MDR-TB) treatment. However, among children with MDR-TB, there are no linezolid pharmacokinetic data, and its adverse effects have not yet been prospectively described. We characterised the pharmacokinetics, safety, and optimal dose of linezolid in children treated for MDR-TB. Methods and findings Children routinely treated for MDR-TB in 2 observational studies (2011–2015, 2016–2018) conducted at a single site in Cape Town, South Africa, underwent intensive pharmacokinetic sampling after either a single dose or multiple doses of linezolid (at steady state). Linezolid pharmacokinetic parameters, and their relationships with covariates of interest, were described using nonlinear mixed-effects modelling. Children receiving long-term linezolid as a component of their routine treatment had regular clinical and laboratory monitoring. Adverse events were assessed for severity and attribution to linezolid. The final population pharmacokinetic model was used to derive optimal weight-banded doses resulting in exposures in children approximating those in adults receiving once-daily linezolid 600 mg. Forty-eight children were included (mean age 5.9 years; range 0.6 to 15.3); 31 received a single dose of linezolid, and 17 received multiple doses. The final pharmacokinetic model consisted of a one-compartment model characterised by clearance (CL) and volume (V) parameters that included allometric scaling to account for weight; no other evaluated covariates contributed to the model. Linezolid exposures in this population were higher compared to exposures in adults who had received a 600 mg once-daily dose. Consequently simulated, weight-banded once-daily optimal doses for children were lower than those currently used for most weight bands. Ten of 17 children who were followed long term had a linezolid-related adverse event, including 5 with a grade 3 or 4 event, all anaemia. Adverse events resulted in linezolid dose reductions in 4, temporary interruptions in 5, and permanent discontinuation in 4 children. Limitations of the study include the lack of very young children (none below 6 months of age), the limited number who were HIV infected, and the modest number of children contributing to long-term safety data. Conclusions Linezolid-related adverse effects were frequent and occasionally severe. Careful linezolid safety monitoring is required. Compared to doses currently used in children in many settings for MDR-TB treatment, lower doses may approximate current adult target exposures, might result in fewer adverse events, and should therefore be evaluated.
Using landscape measures for the online tuning of heterogeneous distributed gas

Tuning distributed genetic algorithms (dGAs) increases even more the task of finding an appropriate parameterization, since the migration operator adds, at least, five additional values that have to be set up. This work is a preliminary approach on using a landscape measure (the Fitness Distance Correlation) to dynamically adjust one of these five parameters, in particular, the migration period. The results have shown that, by using this information, the quality of the solutions is competitive with those obtained by the algorithms with the pre-tuned migration period, but with a saving of more than 100 hours of preliminary experimentation.
Imaging of Thalamocortical Dysrhythmia in Neuropsychiatry

Abnormal brain activity dynamics, in the sense of a thalamocortical dysrhythmia (TCD), has been proposed as the underlying mechanism for a subset of disorders that bridge the traditional delineations of neurology and neuropsychiatry. In order to test this proposal from a psychiatric perspective, a study using magnetoencephalography (MEG) was implemented in subjects with schizophrenic spectrum disorder (n = 14), obsessive–compulsive disorder (n = 10), or depressive disorder (n = 5) and in control individuals (n = 18). Detailed CNS electrophysiological analysis of these patients, using MEG, revealed the presence of abnormal theta range spectral power with typical TCD characteristics, in all cases. The use of independent component analysis and minimum-norm-based methods localized such TCD to ventromedial prefrontal and temporal cortices. The observed mode of oscillation was spectrally equivalent but spatially distinct from that of TCD observed in other related disorders, including Parkinson's disease, central tinnitus, neuropathic pain, and autism. The present results indicate that the functional basis for much of these pathologies may relate most fundamentally to the category of calcium channelopathies and serve as a model for the cellular substrate for low-frequency oscillations present in these psychiatric disorders, providing a basis for therapeutic strategies.
Associating IDS Alerts by an Improved Apriori Algorithm

Among a large number of association rule mining algorithms, Apriori algorithm is the most classic one, but the Apriori algorithm has three deficiencies, namely: the need for scanning databases many times, generating a large number of Candidate Anthology, as well as frequent itemsets iteratively. The paper presents a method that solves the maximal frequent itemsets through one intersection operation. The degree of support is obtained through the times of intersection without having to scan the transaction database, by numbering some of the properties to reduce memory space and search the candidate set list easily, thereby enhancing the efficiency of the algorithm. Finally, it can generate association rules for Intrusion Detection System. Experimental results show that the optimized algorithm can effectively improve the efficiency of mining association rules.
Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.
The Cultural Meaning of Names Among Basotho of South Africa: A Historical and Linguistic Analysis

‘Names’ are more than a ‘word’ or words by which a person, animal, place or thing is known, and does not fundamentally connote designation, reputation, or identification, separation of one individual from the other per se. Among Basotho in southern Africa ‘Names’ and the naming process is a socio-cultural interpretation of historical events. They embody individual or group social experiences, social norms and values, status roles and authority, as well as personality and individual attributes. The discussion focuses on the cultural meaning of personal names and their relationship with historical events. It is argued that the concepts of ‘person’ and ‘self’ among southern African societies have to be understood as historical social products.
RACHS‐ANZ: A Modified Risk Adjustment in Congenital Heart Surgery Model for Outcome Surveillance in Australia and New Zealand

Background Outcomes for pediatric cardiac surgery are commonly reported from international databases compiled from voluntary data submissions. Surgical outcomes for all children in a country or region are less commonly reported. We aimed to describe the bi‐national population‐based outcome for children undergoing cardiac surgery in Australia and New Zealand and determine whether the Risk Adjustment for Congenital Heart Surgery (RACHS) classification could be used to create a model that accurately predicts in‐hospital mortality in this population. Methods and Results The study was conducted in all children's hospitals performing cardiac surgery in Australia and New Zealand between January 2007 and December 2015. The performance of the original RACHS‐1 model was assessed and compared with an alternative RACHS‐ANZ (Australia and New Zealand) model, developed balancing discrimination with parsimonious variable selection. A total of 14 324 hospital admissions were analyzed. The overall hospital mortality was 2.3%, ranging from 0.5% for RACHS category 1 procedures, to 17.0% for RACHS category 5 or 6 procedures. The original RACHS‐1 model was poorly calibrated with death overpredicted (1161 deaths predicted, 289 deaths observed). The RACHS‐ANZ model had better performance in this population with excellent discrimination (Az‐ROC of 0.830) and acceptable Hosmer and Lemeshow goodness‐of‐fit (P=0.216). Conclusions The original RACHS‐1 model overpredicts mortality in children undergoing heart surgery in the current era. The RACHS‐ANZ model requires only 3 risk variables in addition to the RACHS procedure category, can be applied to a wider range of patients than RACHS‐1, and is suitable to use to monitor regional pediatric cardiac surgery outcomes.