Miracle-dz/UniFAR-FTUs huggingface dataset:
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks

Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.
Lewis Acidity and Basicity of Mixed Chlorometallate Ionic Liquids: Investigations from Surface Analysis and Fukui Function

Mixed chlorometallate ionic liquids (ILs) have been regarded as potential solvents, catalysts, and reagents for many organic processes. The acidity and basicity of these ILs were correlated with theoretically estimated parameters such as electrostatic surface potential maxima and minima, average local surface ionization energy, and Fukui and dual descriptor functions. The introduction of metal chloride into the anions would influence the acidity/basicity of ILs by withdrawing the electron density from the cationic counterpart. For the [C4mim]-based ILs with the mixed-metal anions, the acidity tends to attenuate while the basicity becomes stronger, as compared to the corresponding chloroaluminate ILs. However, the acidity of [(C2H5)3NH]-based ILs with the mixed-metal anions are greater than that of the net chloroaluminate ILs. The Fukui function values showed that most of the mixed chlorometallate ILs belong to bifunctional distribution. The mixed chlorometallate ILs both have electrophilic and nucleophilic sites, which would be beneficial for their applications.
Initial Investigation for the Fabrication of Hyperbolic Metamaterials Based on Ultra-Thin Au Layers

In this paper, we will present the fabrication possibilities developed within our group for obtaining multilayer hyperbolic metamaterials (HMMs). The minimum metallic layer thickness reproducibly obtainable with our current technology is down to 6 nm, while the dielectric layer can be as low as 4 nm. During the talk we will present our approach for obtaining Au layers with better optical properties than the standard techniques. This is achievable by using an adhesion layer whose influence of the metallic properties is lower than the one of the classical Cr or Ti adhesion layers. These organic adhesion layers behave as dielectrics and thus do not contribute more to the metallic response. Since the imaginary part of permittivity of these layers is negligible and the real part is very close to the silica one, their contribution to the behaviour of the HMMs is minimal.The optical properties of Au with organic adhesion layers showing a closer to theory response than Au with metallic adhesion layer as well as a possible explanation for this behaviour will be presented. This technique can be further used to obtain metal-dielectric multi-layers that lead to HMMs behaviour. Both fabrication possibilities and optical characterisation will be shown and discussed during the talk.
Speech and language profiles in 4- to 6-year-old children with early diagnosis of autism spectrum disorder without intellectual disability

Purpose This study aimed to present speech and language data from a community-representative group of 4- to 6-year-old children with early-diagnosed autism spectrum disorder (ASD) without intellectual disability (ID). Methods The study group comprised 83 children 4–6 years of age with ASD without ID. They had been diagnosed with ASD before age 4.5 years and had received intervention at a specialized habilitation center. At 2-year follow-up, their language abilities were evaluated comprehensively by two speech–language pathologists using a battery of assessments. Receptive and expressive language and phonology were examined. The phonology evaluation included measures of phonological speech production and of phonological processing. Results Results revealed that almost 60% had moderate–severe language problems. Nearly half exhibited combined expressive and receptive language problems, of which a majority also had phonology problems. Phonological speech problems were found in 21% of the total group. Conclusion The findings underscore the importance of considering speech/language disorders in children with ASD without ID, since they usually attend mainstream classes but need specific educational adaptations.
Zero-Delay Rate Distortion via Filtering for Vector-Valued Gaussian Sources

We deal with zero-delay source coding of a vector-valued Gauss–Markov source subject to a mean-squared error (MSE) fidelity criterion characterized by the operational zero-delay vector-valued Gaussian rate distortion function (RDF). We address this problem by considering the nonanticipative RDF (NRDF), which is a lower bound to the causal optimal performance theoretically attainable function (or simply causal RDF) and operational zero-delay RDF. We recall the realization that corresponds to the optimal “test-channel” of the Gaussian NRDF, when considering a vector Gauss–Markov source subject to a MSE distortion in the finite time horizon. Then, we introduce sufficient conditions to show existence of solution for this problem in the infinite time horizon (or asymptotic regime). For the asymptotic regime, we use the asymptotic characterization of the Gaussian NRDF to provide a new equivalent realization scheme with feedback, which is characterized by a resource allocation (reverse-waterfilling) problem across the dimension of the vector source. We leverage the new realization to derive a predictive coding scheme via lattice quantization with subtractive dither and joint memoryless entropy coding. This coding scheme offers an upper bound to the operational zero-delay vector-valued Gaussian RDF. When we use scalar quantization, then for $r$ active dimensions of the vector Gauss–Markov source the gap between the obtained lower and theoretical upper bounds is less than or equal to $0.254r + 1$ bits/vector. However, we further show that it is possible when we use vector quantization, and assume infinite dimensional Gauss–Markov sources to make the previous gap to be negligible, i.e., Gaussian NRDF approximates the operational zero-delay Gaussian RDF. We also extend our results to vector-valued Gaussian sources of any finite memory under mild conditions. Our theoretical framework is demonstrated with illustrative numerical experiments.
Deep Learning Based Latent Feature Extraction for Intrusion Detection

Despite the attraction of considerable interest from researchers and industries, the community still faces the problem of building reliable and efficient IDSs, capable of detecting intrusion with high accuracy and low time consuming. In this paper, we are investigating a hybrid scheme that combines advantages of deep learning methods and support vector machine to improve the accuracy and efficiency. Initially, a method of deep learning, such as stacked Auto-encoder (SAE) network, is utilized to reduce the dimensionality of the feature sets and gain the latent features. This is followed by a support vector machine (SVM) for binary classification of the events into normal or attacks. Our method is implemented and evaluated using ISCX IDS UNB dataset. Experimental result indicated that our combined method outperforms SVM alone in terms of both accuracy and run-time efficiency.
Predicting Bank Financial Failures using Random Forest

In 1994, there was an economic crisis in Turkey. Many banks were declared failed because of the negative impact from the crisis. The failure of individual banks has a huge impact on the real sector, households and can even cause knock-on effects for other banks. Therefore, it is important to predict bank failure. The 2009, Boyacioglu, Kara, and Baykan had predicted bank failures in the period 1994- 2004 using CAMELS as a predictor variable. In their research, they used Artificial Neural Network (ANN), Support Vector Machine (SVM) and multivariate statistical methods. However, in this research we will make novelty by using random forest as classifier method to predict bank failures in Turkey. Based on our results, random forest has 100\% accuracy in training performance, if we compare to other models that Boyaciaglu, Kara and Baykan (2009) paper, random forest is not inferior to other methods, if they must have to preparation data with normalization, in random forest we don’t need to do that. In testing set, random forest has 94\% accuracy with full ratio and 96\% accuracy with 6 ratios in its performance. Even random forest does not have accuracy more than other methods that have been used, but its accuracy is not far away with others. Then, random forest has property which is can measure important variable as predictor bank fimancial failure, in this research we get $\mathrm{C}\mathrm{A}_{2}, \mathrm{E}_{1}, \mathrm{C}\mathrm{A}_{3}, \mathrm{S}\mathrm{M}\mathrm{R}_{\mathrm{I}}$, SMR2, $\mathrm{E}_{2}, \mathrm{C}\mathrm{A}_{1}, \mathrm{L}_{1}, \mathrm{E}_{3}. \mathrm{A}\mathrm{Q}_{2}$ are six most important variables.
The Elasticity and Plasticity in Semi-Containerized Co-locating Cloud Workload: a View from Alibaba Trace

Cloud computing with large-scale datacenters provides great convenience and cost-efficiency for end users. However, the resource utilization of cloud datacenters is very low, which wastes a huge amount of infrastructure investment and energy to operate. To improve resource utilization, cloud providers usually co-locate workloads of different types on shared resources. However, resource sharing makes the quality of service (QoS) unguaranteed. In fact, improving resource utilization (IRU) and guaranteeing QoS at the same time in cloud has been a dilemma which we name an IRU-QoS curse. To tackle this issue, characterizing the workloads from real production cloud computing platforms is extremely important. In this work, we analyze a recently released 24-hour trace dataset from a production cluster in Alibaba. We reveal three key findings which are significantly different from those from the Google trace. First, each online service runs in a container while batch jobs run on physical servers. Further, they are concurrently managed by two different schedulers and co-located on same servers, which we call semi-containerized co-location. Second, batch instances largely use the spare resources that containers reserved but not used, which shows the elasticity feature of resource allocation of the Alibaba cluster. Moreover, through resource overprovisioning, overbooking, and overcommitment, the resource allocation of the Alibaba cluster achieves high elasticity. Third, as the high elasticity may hurt the performance of co-located online services, the Alibaba cluster sets bounds of resources used by batch tasks to guarantee the steady performance of both online services and batch tasks, which we call plasticity of resource allocation.
Model-Based Six-Component Scattering Matrix Power Decomposition

Fully polarimetric model-based decompositions are developed by accounting for the physical scattering model and experimental polarimetric SAR data acquisition processes. These decompositions offer the promising straightforward interpretation and highly improved inversion models for visualizing images of scattering scenarios optimally. However, the attempts in existing decompositions to implement the split real and imaginary components of the $T_{13}$ element of the coherency matrix have been hampered by the absence of physical models to fit the coherency matrix. In this paper, two additional physical scattering submodels are derived. The real and imaginary parts of $T_{13}$ are accounted for by implementing two newly developed physical scattering models. (One is for oriented dipole scattering and the other is for oriented quarter-wave reflection.) Furthermore, this paper is extended by implementing these physical models into a six-component scattering power model-based decomposition. To this date, the developed novel decompositions account for the maximum elements of the coherency matrix in a physical manner compared to the existing model-based decompositions. The proposed novel decomposition is tested on L-band and X-band fully polarimetric SAR data sets of the Advanced Land Observing Satellite-2/Phased Array L-band Synthetic Aperture Radar-2 and the X-band TerraSAR-X, respectively. This new decomposition produces additional two scattering submatrix components. Such scattering components are prevalent in vegetation and urban areas and even dominant over highly oriented urban scenarios. The new method enhances the truly existing double-bounce scattering contributions and reduces the overrated volume scattering from double-bounce scatterers. By comparing the results, it is found that the proposed decomposition considerably enhances the SAR image quality and its more correct visualizing presentation compared to existing decompositions. It is also found to be more robust over the oriented urban areas than the existing decompositions, resulting from the utilization of both the real and imaginary components of $T_{13}$ polarimetric information in a physical scattering manner.
Assessment of the Public Health Threats Posed by Vector-Borne Disease in the United Kingdom (UK)

In recent years, the known distribution of vector-borne diseases in Europe has changed, with much new information also available now on the status of vectors in the United Kingdom (UK). For example, in 2016, the UK reported their first detection of the non-native mosquito Aedes albopictus, which is a known vector for dengue and chikungunya virus. In 2010, Culex modestus, a principal mosquito vector for West Nile virus was detected in large numbers in the Thames estuary. For tick-borne diseases, data on the changing distribution of the Lyme borreliosis tick vector, Ixodes ricinus, has recently been published, at a time when there has been an increase in the numbers of reported human cases of Lyme disease. This paper brings together the latest surveillance data and pertinent research on vector-borne disease in the UK, and its relevance to public health. It highlights the need for continued vector surveillance systems to monitor our native mosquito and tick fauna, as well as the need to expand surveillance for invasive species. It illustrates the importance of maintaining surveillance capacity that is sufficient to ensure accurate and timely disease risk assessment to help mitigate the UK’s changing emerging infectious disease risks, especially in a time of climatic and environmental change and increasing global connectivity.
Solving Time-Domain Audio Inverse Problems Using Nonnegative Tensor Factorization

Nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) are important tools for modeling nonnegative data, which gained increasing popularity in various fields, a significant one of which is audio processing. However, there are still many problems in audio processing, for which the NMF (or NTF) model has not been successfully utilized. In this paper, we propose a new algorithm based on the NMF (and NTF) in the short-time Fourier domain for solving a large class of audio inverse problems with missing or corrupted time-domain samples. The proposed approach overcomes the difficulty of employing a model in the frequency domain to recover time-domain samples with the help of probabilistic modeling. Its performance is demonstrated for the following applications: audio declipping and declicking (never solved with NMF/NTF modeling prior to this paper); joint audio declipping/declicking and source separation (never solved with NMF/NTF modeling or any other method prior to this paper); and compressive sampling recovery and compressive sampling-based informed source separation (an extremely low complexity encoding scheme that is possible with the proposed approach and has never been proposed prior to this paper).
How Can We Define “Optimal Microbiota?”: A Comparative Review of Structure and Functions of Microbiota of Animals, Fish, and Plants in Agriculture

All multicellular organisms benefit from their own microbiota, which play important roles in maintaining the host nutritional health and immunity. Recently, the number of studies on the microbiota of animals, fish, and plants of economic importance is rapidly expanding and there are increasing expectations that productivity and sustainability in agricultural management can be improved by microbiota manipulation. However, optimizing microbiota is still a challenging task because of the lack of knowledge on the dominant microorganisms or significant variations between microbiota, reflecting sampling biases, different agricultural management as well as breeding backgrounds. To offer a more generalized view on microbiota in agriculture, which can be used for defining criteria of “optimal microbiota” as the goal of manipulation, we summarize here current knowledge on microbiota on animals, fish, and plants with emphasis on bacterial community structure and metabolic functions, and how microbiota can be affected by domestication, conventional agricultural practices, and use of antimicrobial agents. Finally, we discuss future tasks for defining “optimal microbiota,” which can improve host growth, nutrition, and immunity and reduce the use of antimicrobial agents in agriculture.
Discriminative out-of-distribution detection for semantic segmentation

Most classification and segmentation datasets assume a closed-world scenario in which predictions are expressed as distribution over a predetermined set of visual classes. However, such assumption implies unavoidable and often unnoticeable failures in presence of out-of-distribution (OOD) input. These failures are bound to happen in most real-life applications since current visual ontologies are far from being comprehensive. We propose to address this issue by discriminative detection of OOD pixels in input data. Different from recent approaches, we avoid to bring any decisions by only observing the training dataset of the primary model trained to solve the desired computer vision task. Instead, we train a dedicated OOD model which discriminates the primary training set from a much larger "background" dataset which approximates the variety of the visual world. We perform our experiments on high resolution natural images in a dense prediction setup. We use several road driving datasets as our training distribution, while we approximate the background distribution with the ILSVRC dataset. We evaluate our approach on WildDash test, which is currently the only public test dataset that includes out-of-distribution images. The obtained results show that the proposed approach succeeds to identify out-of-distribution pixels while outperforming previous work by a wide margin.
World Health Organization life‐skills training is efficacious in reducing youth self‐report scores in primary school going children in Kenya

Documented evidence shows that training in life skills for school going children improves their physical and mental health status. Although Kenya has a curriculum and a policy for life‐skills training in schools, these have not been implemented because lack of local evidence for efficacy. Therefore, the objective of this study was to determine the efficacy and effectiveness of the life‐skill training curriculum for primary schools developed by the Ministry of Education, Kenya.
Pregnancy outcome in type 2 diabetes mellitus: a retrospective analysis from the Netherlands.

MAIN OBJECTIVES The objective was to describe pre-gestational history and the maternal, fetal and neonatal outcome in pregnancies in women with pre-gestational type 2 diabetes during the period between 1992 and 2006 from one center in the Netherlands. METHODS Patients attending the obstetric-diabetology outpatient clinic of a tertiary referral center were studied. This center also has a regular diabetes clinic and a community midwifery service. Patients were identified from the database. Maternal outcome (pre-eclampsia, pre-term delivery, Caesarean section) and fetal and neonatal outcome (macrosomia, congenital malformations, perinatal mortality, neonatal hypoglycemia) were analyzed as well HbA1c levels, planning of pregnancy, gestational age at first antenatal visit and ethnic background. RESULTS Sixty-six singleton pregnancies from 48 women were analyzed. Their age was 34 +/- 5 yr, the BMI 31.7 +/- 7.4 and the median duration of diabetes was 3 yr. 52% were Caucasian and 35% were of Moroccan descent. 49% did not complete secondary school. Moroccan descent was associated with a lower educational level and a BMI comparable with the whole study group. The proportion of planned pregnancies was approximately 70%. The mean HbA1c in the first trimester was 6.4 +/- 1.1% and the gestational age at first visit was 10 +/- 5 wk, in one-quarter before 6 wk. The prevalences of variables related to maternal and neonatal outcome were as follows: spontaneous abortion 13.6%, pre-eclampsia 8.9%, pre-term delivery 21.4%, spontaneous labor 25.0%, induced labor 48.2%, Caesarean section 42.9%, macrosomia (>/=90th percentile) 41.1%, severe hypoglycemia 41.5% and major congenital malformations 5.1%. CONCLUSIONS Pre-gestational type 2 diabetes is associated with an increased incidence of adverse pregnancy outcome despite reasonable mean HbA1c level and despite a high frequency of planned pregnancies. Many women report relatively late. Improvement in the outcome requires more active peri-gestational specialist care and a tailored approach is required towards migrant communities.
Optimization of Circuits for IBM's Five-Qubit Quantum Computers

IBM has made several quantum computers available to researchers around the world via cloud services. Two architectures with five qubits, one with 16, and one with 20 qubits are available to run experiments. The IBM architectures implement gates from the Clifford+T gate library. However, each architecture only implements a subset of the possible CNOT gates. In this paper, we show how Clifford+T circuits can efficiently be mapped into the two IBM quantum computers with 5 qubits. We further present an algorithm and a set of circuit identities that may be used to optimize the Clifford+T circuits in terms of gate count and number of levels. It is further shown that the optimized circuits can considerably reduce the gate count and number of levels and thus produce results with better fidelity.