Miracle-dz/UniFAR-FTUs huggingface dataset:
Witness validation and stepwise testification across software verifiers

It is commonly understood that a verification tool should provide a counterexample to witness a specification violation. Until recently, software verifiers dumped error witnesses in proprietary formats, which are often neither human- nor machine-readable, and an exchange of witnesses between different verifiers was impossible. To close this gap in software-verification technology, we have defined an exchange format for error witnesses that is easy to write and read by verification tools (for further processing, e.g., witness validation) and that is easy to convert into visualizations that conveniently let developers inspect an error path. To eliminate manual inspection of false alarms, we develop the notion of stepwise testification: in a first step, a verifier finds a problematic program path and, in addition to the verification result FALSE, constructs a witness for this path; in the next step, another verifier re-verifies that the witness indeed violates the specification. This process can have more than two steps, each reducing the state space around the error path, making it easier to validate the witness in a later step. An obvious application for testification is the setting where we have two verifiers: one that is efficient but imprecise and another one that is precise but expensive. We have implemented the technique of error-witness-driven program analysis in two state-of-the-art verification tools, CPAchecker and Ultimate Automizer, and show by experimental evaluation that the approach is applicable to a large set of verification tasks.
Beating Monte Carlo Integration: a Nonasymptotic Study of Kernel Smoothing Methods

Evaluating integrals is an ubiquitous issue and Monte Carlo methods, exploiting advances in random number generation over the last decades, offer a popular and powerful alternative to integration deterministic techniques, unsuited in particular when the domain of integration is complex. This paper is devoted to the study of a kernel smoothing based competitor built from a sequence of n ≥ 1 i.i. d random vectors with arbitrary continuous probability distribution f(x)dx, originally proposed in [7], from a nonasymptotic perspective. We establish a probability bound showing that the method under study, though biased, produces an estimate approximating the target integral ∫ x∈Rd φ(x)dx with an error bound of order o(1/ √ n) uniformly over a class Φ of functions φ, under weak complexity/smoothness assumptions related to the class Φ, outperforming Monte-Carlo procedures. This striking result is shown to derive from an appropriate decomposition of the maximal deviation between the target integrals and their estimates, highlighting the remarkable benefit to averaging strongly dependent terms regarding statistical accuracy in this situation. The theoretical analysis then rests on sharp probability inequalities for degenerate U -statistics. It is illustrated by numerical results in the context of covariate shift regression, providing empirical evidence of the relevance of the approach. Proceedings of the 21 International Conference on Artificial Intelligence and Statistics (AISTATS) 2018, Lanzarote, Spain. PMLR: Volume 84. Copyright 2018 by the author(s).
Chlorophyll-a Estimation in Turbid Waters Using Combined SAR Data With Hyperspectral Reflectance Data: A Case Study in Lake Taihu, China

The estimation of chlorophyll-a (chl-a) concentration remains a great challenge in turbid waters due to their complex optical conditions. To improve chl-a estimation, this study aims to determine whether combined use of polarimetric synthetic-aperture radar (SAR) data has potential for improving the chl-a estimation from hyperspectral sensing reflectance for turbid waters such as those found in Lake Taihu, China. In situ measurements of hyperspectral reflectance data and water samples were collected over the lake corresponding to ENVISAT ASAR data. Semiempirical (two-band and three-band models) and empirical [multiple linear regression (MLR) and multilayer perceptron network (MLP)] models are compared to estimate the chl-a concentration from in situ hyperspectral reflectance and SAR data. The results show that there is a general underestimation of chl-a for concentrations higher than 26 ug/L, which is probably caused by the large spatial variation of chl-a in the study area. The results also demonstrate that the MLR model performs in a more stable manner than the MLP network does, while MLP underestimates low and high areas of chl-a concentrations in the lake. On the other hand, due to the availability of one scenic SAR data on the same day, our results show that the additional use of SAR data improved chl-a estimation very slightly in this case study, although the performance of vertical/vertical polarization SAR data was better than that of horizontal/horizontal polarization data in chl-a estimation. Potential future work in this subject could explore other measures of mutual information between SAR and hyperspectral optical data beyond the correlation and regression techniques described. Therefore, it is still necessary to apply more SAR data in varied turbid waters in the near future to determine how SAR data can be useful in the improvement of chl-a estimation.
Cognitive Function Does Not Impact Self-reported Health-Related Quality of Life in Heart Failure Patients

Background:Adults with heart failure (HF) often demonstrate impairment across multiple domains of cognitive functioning and report poor health-related quality of life (HRQoL). Previous researchers have found that cognitive deficits were generally not associated with HRQoL in a carefully evaluated sample. The exception was memory, which was only weakly associated with HRQoL. However, cognitive deficits interfere with self-care and disease self-management, which could be expected to affect HRQoL. Objective:We sought to verify this counterintuitive finding in a large well-characterized sample of HF patients using a well-validated neuropsychological battery. Methods:Participants were 302 adults (63% male) predominately white (72.5%) HF patients (68.7 ± 9.6 years) recruited from 2 medical centers. Self-reported HRQoL was assessed using the Kansas City Cardiomyopathy Questionnaire. Participants completed a neuropsychological battery examining attention, executive function, memory, and visuospatial functioning. Hierarchical multiple linear regression was used for analyses. Results:Mild global cognitive impairment was observed in 29.5% of the sample (Modified Mini-Mental State [3MS] Examination score <90). Controlling for gender, depression, HF severity, premorbid IQ, comorbidities, and education, only executive function predicted HRQoL, &bgr; = . 17, P < . 05. However, executive function accounted for only 0.6% of the variance in HRQoL. Conclusion:Cognitive function generally did not predict HRQoL in HF patients. The correlates of HRQoL in HF do not appear to include mild cognitive impairment. Other factors may play a bigger role such as disease severity, age, and depressive symptoms. Future studies should investigate modifiable determinants of HRQoL in HF patients, toward the goal of finding interventions that preserve HRQoL during this chronic illness.
Neoadjuvant Bevacizumab plus Chemotherapy versus Chemotherapy Alone to Treat Non-Metastatic Breast Cancer: A Meta-Analysis of Randomised Controlled Trials

Purpose Results from previous randomised controlled trials (RCTs) investigating whether the addition of bevacizumab to neoadjuvant chemotherapy (NAC) could statistically significantly increase the pathological complete response (pCR) and to identify which subgroup would benefit most from such regimens have produced conflicting results. This meta-analysis was designed to assess the efficacy and safety of bevacizumab plus chemotherapy compared with chemotherapy alone in the neoadjuvant setting. Methods A literature search of MEDLINE, EMBASE, Web of Science, and the Cochrane library was performed to identify eligible studies. The primary endpoint of interest was pCR. The secondary endpoints were clinical complete rate (cCR), surgery rate, breast-conserving surgery (BCS) rate, and toxicity. The meta-analysis was performed using Review Manager software version 5.3. Results Nine RCTs matched the selection criteria, yielding a total of 4967 patients (bevacizumab plus chemotherapy: 50.1%, chemotherapy alone: 49.9%). The results of this meta-analysis demonstrated that the addition of bevacizumab to NAC significantly increased the pCR rate (odds ratio [OR] = 1.34 [1.18–1.54]; P < 0.0001) compared with chemotherapy alone. Subgroup analysis showed that the effect of bevacizumab was more pronounced in patients with HER2-negative cancer (OR = 1.34 [1.17–1.54]; P < 0.0001) compared with HER2-positive cancer (OR = 1.69 [0.90–3.20]; P = 0.11). Similarly, in patients with HER2-negative cancer, the effect of bevacizumab was also more pronounced in patients with HR-negative cancer (OR = 1.38 [1.09–1.74]; P = 0.007) compared with HR-positive cancer (OR = 1.36 [0.78–2.35]; P = 0.27). No significant differences were observed between the groups with respect to cCR, surgery rate, or BCS rate. Additionally bevacizumab was associated with a higher incidence of neutropenia, febrile neutropenia, and hand–foot syndrome. Conclusions Higher proportions of patients achieved pCR when bevacizumab was added to NAC compared with when they received chemotherapy alone; acceptable toxicities were also found. Subgroup analysis demonstrated that patients with histologically confirmed HER2-negative and HR-negative breast cancer benefited the most.
Fast generalization error bound of deep learning from a kernel perspective

We develop a new theoretical framework to analyze the generalization error of deep learning, and derive a new fast learning rate for two representative algorithms: empirical risk minimization and Bayesian deep learning. The series of theoretical analyses of deep learning has revealed its high expressive power and universal approximation capability. Our point of view is to deal with the ordinary finite dimensional deep neural network as a finite approximation of the infinite dimensional one. Our formulation of the infinite dimensional model naturally defines a reproducing kernel Hilbert space corresponding to each layer. The approximation error is evaluated by the degree of freedom of the reproducing kernel Hilbert space in each layer. We derive the generalization error bound of both of empirical risk minimization and Bayesian deep learning and it is shown that there appears bias-variance trade-off in terms of the number of parameters of the finite dimensional approximation. We show that the optimal width of the internal layers can be determined through the degree of freedom and derive the optimal convergence rate that is faster than O(1/ √ n) rate which has been shown in the existing studies.
Space Charge Measurement by Electroacoustic Method: Impact of Acoustic Properties of Materials on The Response for Different Geometries

The pulsed electro-acoustic method (PEA) allows dynamic measurements of space charge density distribution with good temporal resolution and is suitable for both flat samples (a few hundred micrometers thickness) and coaxial cable samples. The response obtained by the technique is tributary to the electro-mechanical properties of the materials through which acoustic waves propagate. Hence, when dealing with multilayer dielectrics, the acoustic properties of individual layers are to be taken into account. Also, the acoustic attenuation and dispersion phenomena, especially when dealing with relatively thick samples, affect the space charge resolution at positions far away from the sensor. Therefore, application of a calibration procedure on experimental signals is necessary to recover space charge density profile from PEA raw signals and to provide real results on charge density both on its temporal and spatial dependence. We illustrate here in different samples configurations, spanning from flat samples, multi-layer and cable geometry, how the calibration is achieved and how the acoustic properties of materials impart the response.
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently

We study the fundamental problem of learning the parameters of a high-dimensional Gaussian in the presence of noise -- where an $\varepsilon$-fraction of our samples were chosen by an adversary. We give robust estimators that achieve estimation error $O(\varepsilon)$ in the total variation distance, which is optimal up to a universal constant that is independent of the dimension. In the case where just the mean is unknown, our robustness guarantee is optimal up to a factor of $\sqrt{2}$ and the running time is polynomial in $d$ and $1/\epsilon$. When both the mean and covariance are unknown, the running time is polynomial in $d$ and quasipolynomial in $1/\varepsilon$. Moreover all of our algorithms require only a polynomial number of samples. Our work shows that the same sorts of error guarantees that were established over fifty years ago in the one-dimensional setting can also be achieved by efficient algorithms in high-dimensional settings.
Distinct Roles for the Anterior Cingulate and Dorsolateral Prefrontal Cortices During Conflict Between Abstract Rules

Abstract Distinct patterns of activity within the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (dlPFC) reported in neuroimaging studies during tasks involving conflict between competing responses have often been cited as evidence for their key contributions to conflict‐monitoring and behavioral adaptation, respectively. However, supporting evidence from neuropsychological patients has been scarce and contradictory. We administered a well‐studied analog of the Wisconsin Card Sorting Test, designed to elicit conflict between 2 abstract rules, to a cohort of 6 patients with damage to ACC or dlPFC. Patients who had sustained more significant damage to the ACC were not impaired either on a measure of “conflict cost” nor on measures of “conflict‐induced behavioral adaptation. ” In contrast, damage to dlPFC did not affect the conflict cost measure but abolished the patients’ ability to adapt their behavior following exposure to conflict, compared with controls. This pattern of results complements the findings from nonhuman primates with more circumscribed lesions to ACC or dlPFC on the same task and provides converging evidence that ACC is not necessary for performance when conflict is elicited between 2 abstract rules, whereas dlPFC plays a fundamental role in behavioral adaptation.
Recent Developments in the Medicinal Applications of Silver-NHC Complexes and Imidazolium Salts

Because of their great structural diversity and multitude of chemical properties, N-heterocyclic carbenes (NHCs) have been utilized in a variety of capacities. Most recently, NHCs have been utilized as carrier molecules for many transition metals in medicinal chemistry. Specifically, Ag(I)-NHCs have been investigated as potent antibacterial agents and chemotherapeutics and have shown great efficacy in both in vitro and in vivo studies. Ag(I)-NHC compounds have been shown to be effective against a wide range of both Gram-positive and Gram-negative bacterial strains. Many compounds have also shown great efficacy as antitumor agents demonstrating comparable or better antitumor activity than standard chemotherapeutics such as cisplatin and 5-fluorouracil. While these compounds have shown great promise, clinical use has remained an unattained goal. Current research has been focused upon synthesis of novel Ag(I)-NHC compounds and further investigations of their antibacterial and antitumor activity. This review will focus on recent advances of Ag(I)-NHCs in medicinal applications.
Quality Sorting and Trade: Firm-Level Evidence for French Wine

Investigations of the effect of quality differences on heterogeneous performance in exporting have been limited by lack of direct measures of quality. We examine exports of French wine, matching the exporting firms to producer ratings from two wine guides. We show that high quality producers export to more markets, charge higher prices, and sell more in each market. More attractive markets are served by exporters that, on average, make lower rated Champagne. Market attractiveness has a weakly negative effect on prices and a strongly positive effect on quantities, confirming the sign predictions of a simple quality sorting model. Methodologically, we make several contributions to the literature. First, we propose an estimation method for regressions of firm-level exports on ability measures and use Monte Carlo simulations to show that it corrects a severe selection bias present in OLS estimates. Second, we show how the means of quality, price, and quantity for exporters to a given market can be used to recover estimates of core parameters (which we compare with firm-level estimates) and discriminate between productivity and quality-sorting versions of the Melitz model. Our new method regresses country means on an index of each country's attractiveness and the fixed costs of entering it. We compare our method, which utilizes explanatory variables estimated in the firm-level regressions, to the conventional approach that relies on a reduced-form relationship with proxies for attractiveness and fixed costs.
Resource Limitation Drives Patterns of Habitat Occupancy during the Nonbreeding Season for an Omnivorous Songbird

Abstract. The role of food in limiting migratory birds during the nonbreeding period is poorly understood, in part because of the complexities of quantifying food availability and diet. We tracked overwinter changes in the availability of arthropods and fruits, the primary winter foods of the Hermit Thrush (Catharus guttatus), a short-distance migrant. Fruit availability declined over the winter, and arthropod availability fluctuated with changing temperature. Concurrently, using fecal samples and stable isotopes, we tracked relative food consumption. In fecal samples fruit declined from early to mid season and &dgr;13C and &dgr;15N isotope signatures in blood became more enriched, consistent with a decline in fruit consumption and an increase in arthropod consumption. Larger-bodied birds, predominantly males, maintained territories in which the abundance of arthropods was higher, had a greater proportion of arthropods in their diet and less variation in &dgr;13C (indicator of a stable diet) and fat loads over the winter. In contrast, smaller-bodied birds, primarily females, gained fat midwinter in response to unpredictable and lower-quality resources. These results are consistent with both a size-mediated form of dominance and sexual habitat segregation, such that smaller bodied birds, mainly females, may be behaviorally excluded from optimal territories. Future research should focus on the long-term consequences of food limitation in the nonbreeding season and size and sex-mediated dominance behavior on both the condition of birds within a season and on subsequent breeding success and survival.
Cholera in Cameroon, 2000-2012: Spatial and Temporal Analysis at the Operational (Health District) and Sub Climate Levels

Introduction Recurrent cholera outbreaks have been reported in Cameroon since 1971. However, case fatality ratios remain high, and we do not have an optimal understanding of the epidemiology of the disease, due in part to the diversity of Cameroon’s climate subzones and a lack of comprehensive data at the health district level. Methods/Findings A unique health district level dataset of reported cholera case numbers and related deaths from 2000–2012, obtained from the Ministry of Public Health of Cameroon and World Health Organization (WHO) country office, served as the basis for the analysis. During this time period, 43,474 cholera cases were reported: 1748 were fatal (mean annual case fatality ratio of 7.9%), with an attack rate of 17.9 reported cases per 100,000 inhabitants per year. Outbreaks occurred in three waves during the 13-year time period, with the highest case fatality ratios at the beginning of each wave. Seasonal patterns of illness differed strikingly between climate subzones (Sudano-Sahelian, Tropical Humid, Guinea Equatorial, and Equatorial Monsoon). In the northern Sudano-Sahelian subzone, highest number of cases tended to occur during the rainy season (July-September). The southern Equatorial Monsoon subzone reported cases year-round, with the lowest numbers during peak rainfall (July-September). A spatial clustering analysis identified multiple clusters of high incidence health districts during 2010 and 2011, which were the 2 years with the highest annual attack rates. A spatiotemporal autoregressive Poisson regression model fit to the 2010–2011 data identified significant associations between the risk of transmission and several factors, including the presence of major waterbody or highway, as well as the average daily maximum temperature and the precipitation levels over the preceding two weeks. The direction and/or magnitude of these associations differed between climate subzones, which, in turn, differed from national estimates that ignored subzones differences in climate variables. Conclusions/Significance The epidemiology of cholera in Cameroon differs substantially between climate subzones. Development of an optimal comprehensive country-wide control strategy for cholera requires an understanding of the impact of the natural and built environment on transmission patterns at the local level, particularly in the setting of ongoing climate change.
Successful treatment of advanced malignant fibrous histiocytoma of the right forearm with apatinib: a case report

Malignant fibrous histiocytoma (MFH) is the most common soft-tissue sarcoma in late adult life. Unfortunately, advanced MFH has a poor prognosis due to a lack of effective drugs. We present here a case of advanced MFH with partial response to apatinib, a new potent oral small-molecule tyrosine kinase inhibitor targeting the intracellular domain of vascular endothelial growth factor receptor 2 (VEGFR-2). To the best of our knowledge, this is the first case report using apatinib for MFH. Quantitative polymerase chain reaction analysis revealed high expression of VEGFR-2 mRNA, suggesting that apatinib leads to clinical response by inhibiting VEGFR-2 tyrosine kinase activity and the crucial role of VEGFR-2 for MFH. Apatinib could be a new option for the treatment of MFH. Further studies are needed to optimize the treatment.
Folic acid treatment reduces elevated plasma levels of asymmetric dimethylarginine in hyperhomocysteinaemic subjects

Asymmetric dimethylarginine (ADMA), an endogenous inhibitor of NO synthase, has been suggested to be a novel risk factor for endothelial dysfunction. It has previously been reported that hyperhomocysteinaemia may be associated with impaired endothelium-dependent vasodilation and reduced plasma level of NO-derived endproducts (NOx). In the present study, plasma levels of arginine and ADMA were measured in twenty-one healthy control subjects, and in twenty-one hyperhomocysteinaemic subjects before and after 6 weeks and 12 months of folic acid supplementation, and compared with previously measured plasma NOx values in the hyperhomocysteinaemic subjects. Compared with control subjects, hyperhomocysteinaemic subjects had higher plasma levels of arginine and ADMA. More importantly, folic acid therapy significantly reduced plasma levels of arginine and ADMA. Furthermore, plasma levels of arginine and ADMA were positively correlated with plasma homocysteine levels and negatively correlated with plasma folate, as well as negatively correlated with plasma NOx. Our results suggest that ADMA may be a mediator of the atherogenic effects of homocysteine.