Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparison of Deep Learning performances with others machine learning algorithms on credit scoring unbalanced data

Training models on highly unbalanced data is admitted to be a challenging task for machine learning algorithms. Current studies on deep learning mainly focus on data sets with balanced class labels or unbalanced data, but with massive amount of samples available, like in speech recognition. However, the capacities of deep learning on imbalanced data with little samples is not deeply investigated in literature, while it is a very common application context in numerous industries. To contribute to fill this gap, this paper compares the performances of several popular machine learning algorithms previously applied with success to unbalanced data set with deep learning algorithms. We conduct those experiments on a highly unbalanced data set, used for credit scoring. We evaluate various configuration including neural network optimization techniques and try to determine their capacities when they operate with imbalanced corpora.
Trait-space patterning and the role of feedback in antigen-immunity coevolution

Coevolutionary arms races form between interacting populations that constitute each other's environment and respond to mutual changes. This inherently far-from-equilibrium process finds striking manifestations in the adaptive immune system, where highly variable antigens and a finite repertoire of immune receptors coevolve on comparable timescales. This unique challenge to the immune system motivates general questions: How do ecological and evolutionary processes interplay to shape diversity? What determine the endurance and fate of coevolution? Here, we take the perspective of responsive environments and develop a phenotypic model of coevolution between receptors and antigens that both exhibit cross-reactivity (one-to-many responses). The theory predicts that the extent of asymmetry in cross-reactivity is a key determinant of repertoire composition: small asymmetry supports persistent large diversity, whereas strong asymmetry yields long-lived transients of quasispecies in both populations. The latter represents a new type of Turing mechanism. More surprisingly, patterning in the trait space feeds back on population dynamics: spatial resonance between the Turing modes breaks the dynamic balance, leading to antigen extinction or unrestrained growth. Model predictions can be tested via combined genomic and phenotypic measurements. Our work identifies cross-reactivity as an important regulator of diversity and coevolutionary outcome, and reveals the remarkable effect of ecological feedback in pattern-forming systems, which drives evolution toward non-steady states different than the Red Queen persistent cycles.
Decentralized Utility- and Locality-Aware Replication for Heterogeneous DHT-Based P2P Cloud Storage Systems

As a Distributed Hash Table (DHT), Skip Graph routing overlays are exploited in several peer-to-peer (P2P) services, including P2P cloud storage. The fully decentralized replication algorithms that are applicable to the Skip Graph-based P2P cloud storage fail on improving the performance of the system with respect to both the availability of replicas as well as their response time. Additionally, they presume the system as homogeneous with respect to the nodes’ latency distribution, availability behavior, and bandwidth, or storage. In this article, we propose Pyramid, which is the first fully decentralized utility- and locality-aware replication approach for Skip Graph-based P2P cloud storage systems. Pyramid considers the nodes as heterogeneous with respect to their latency distribution, availability behavior, bandwidth, and storage. Pyramid is utility-aware as it maximizes the average available bandwidth of replicas per time slot (e.g., per hour). Additionally, Pyramid is locality-aware as it minimizes the average latency between nodes and their closest replica. Our simulation results show that compared to the state-of-the-art solutions that either perform good in utility-awareness, or in locality-awareness, our proposed Pyramid improves both the utility- and locality-awareness of replicas with a gain of about 1.2 and 1.1 times at the same time, respectively.
Spatiotemporal Information Processing with a Reservoir Decision-making Network

Spatiotemporal information processing is fundamental to brain functions. The present study investigates a canonic neural network model for spatiotemporal pattern recognition. Specifically, the model consists of two modules, a reservoir subnetwork and a decision-making subnetwork. The former projects complex spatiotemporal patterns into spatially separated neural representations, and the latter reads out these neural representations via integrating information over time; the two modules are combined together via supervised-learning using known examples. We elucidate the working mechanism of the model and demonstrate its feasibility for discriminating complex spatiotemporal patterns. Our model reproduces the phenomenon of recognizing looming patterns in the neural system, and can learn to discriminate gait with very few training examples. We hope this study gives us insight into understanding how spatiotemporal information is processed in the brain and helps us to develop brain-inspired application algorithms.
Solving RED With Weighted Proximal Methods

REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors. A drawback of this approach is the high computational complexity of denoisers, which dominate the computation time. In this paper, we apply a general framework called weighted proximal methods (WPMs) to solve RED efficiently. We first show that two recently introduced RED solvers (using the fixed point and accelerated proximal gradient methods) are particular cases of WPMs. Then we show by numerical experiments that slightly more sophisticated variants of WPM can lead to reduced run times for RED by requiring a significantly smaller number of calls to the denoiser.
Effective Beginning Reading Programs: A Best-Evidence Synthesis.

of Education (Grant No. R305A040082). However, any opinions expressed are those of the authors and do not necessarily represent IES positions or policies. Abstract This article systematically reviews research on the achievement outcomes of four types of approaches to improving the beginning reading success of children in kindergarten and first grade: Reading curricula, instructional technology, instructional process programs, and combinations of curricula and instructional process. Study inclusion criteria included use of randomized or matched control groups, a study duration of at least 12 weeks, valid achievement measures independent of the experimental treatments, and a final assessment at the end of grade 1 or later. A total of 62 studies met these criteria. The review concludes that instructional process programs designed to change daily teaching practices have substantially greater research support than programs that focus on curriculum or technology alone. In particular, positive achievement effects were found for Success for All, PALS, phonological awareness training, and other programs focused on professional development. 3 From the first day of kindergarten to the last day of first grade, most children go through an extraordinary transformation as readers. If all goes well, children at the end of first grade know the sounds of all the letters and can form them into words, know the most common sight words, and can read and comprehend simple texts. The K-1 period is distinct from other stages of reading development because during this stage, children are learning all the basic skills of turning print into meaning. From second grade on, children build fluency, comprehension, and vocabulary for reading ever more complex text in many genres, but the K-1 period is qualitatively different in its focus on basic skills. Success in beginning reading is a key prerequisite for success in reading in the later years. Longitudinal studies (e.g., Juel, 1988) have shown that children with poor reading skills at the end of first grade are unlikely to catch up later on, and are likely to have difficulties in reading throughout their schooling. It is in the early elementary grades where the gap in performance between children of different races first appears, and this gap is perhaps the most important policy issue in education in the U.S. On the fourth grade National Assessment of Educational Progress (NAEP, 2007), 43% of White children achieved at the " proficient " level on the National Assessment of Educational Progress, but only 14% of …
Specifically Induces the IgA Class Switch β The Surprising Discovery That TGF

Janet Stavnezer and Joonsoo Kanghttp://www.jimmunol.org/content/182/1/5J Immunol€2009; 182:5-7; ;Referenceshttp://www.jimmunol.org/content/182/1/5.full#ref-list-1This article cites 49 articles, 33 of which you can access for free at: Subscriptionshttp://jimmunol.org/subscriptionsInformation about subscribing to The Journal of Immunology is online at: Permissionshttp://www.aai. org/ji/copyright.htmlSubmit copyright permission requests at: Email Alertshttp://jimmunol.org/cgi/alerts/etocReceive free email-alerts when new articles cite this article. Sign up at:
Extreme gas kinematics in an off-nuclear HII region of SDSS J143245.98+404300.3

We present and discuss the properties of an ionized gas component with extreme kinematics in a recently reported off-nuclear HII region located at ∼0.8−1.0 kpc from the nucleus of SDSS J143245.98+404300.3. The high-velocity-gas component is identified by the detection of very broad emission wings in the Hα line, with full width at half maximum (FWHM) ≥ 850−1000 km s−1. Such gas kinematics are outstandingly high compared to other HII regions in local galaxies and are similar to those reported in some star-forming clumps of galaxies at z ∼ 2. The spatially resolved analysis indicates that the high-velocity gas extends at least ∼90 pc and it could be compatible with an ionized outflow entraining gas at a rate between approximately seven and nine times faster than the rate at which gas is being converted into stars. We do not detect broad emission wings in other emission lines such as Hβ, perhaps due to moderate dust extinction, nor in [N II]λλ6548, 6584 or [S II]λλ6717, 6731, which could be due to the presence of turbulent mixing layers originated by the impact of fast-flowing winds. The lack of spectral signatures associated to the presence of Wolf–Rayet stars points towards stellar winds from a large number of massive stars and/or supernovae as the likely mechanisms driving the high-velocity gas.
Jerk Control of Floating Base Systems With Contact-Stable Parameterized Force Feedback

Nonlinear controllers for floating base systems in contact with the environment are often framed as quadratic programming (QP) optimization problems. Common drawbacks of such QP-based controllers are: the control input often experiences discontinuities; no force feedback from force/torque (FT) sensors installed on the robot is taken into account. This article attempts to address these limitations using jerk-based control architectures. The proposed controllers assume the rate-of-change of the joint torques as control input, and exploit the system position, velocity, accelerations, and contact wrenches as measurable quantities. The key ingredient of the presented approach is a one-to-one correspondence between free variables and an inner approximation of the manifold defined by the contact stability constraints. More precisely, the proposed correspondence covers completely the contact stability manifold except for the socalled friction cone, for which there exists a unique correspondence for more than 90% of its elements. The correspondence allows us to transform the underlying constrained optimization problem into one that is unconstrained. Then, we propose a jerk control framework that exploits the proposed correspondence and uses FT measurements in the control loop. Furthermore, we present Lyapunov stable controllers for the system momentum in the jerk control framework. The approach is validated with simulations and experiments using the iCub humanoid robot.
Local Differential Privacy: a tutorial

In the past decade analysis of big data has proven to be extremely valuable in many contexts. Local Differential Privacy (LDP) is a state-of-the-art approach which allows statistical computations while protecting each individual user's privacy. Unlike Differential Privacy no trust in a central authority is necessary as noise is added to user inputs locally. In this paper we give an overview over different LDP algorithms for problems such as locally private heavy hitter identification and spatial data collection. Finally, we will give an outlook on open problems in LDP.
ANALYTICAL SOLUTIONS FOR THE DYNAMICAL CLOCK A+ INDICATOR IN A TOY MODEL OF PURE DYNAMICAL FRICTION

Blue straggler stars are more massive than the average star in globular clus- ters, as they originate from the merger of two stars. Consequently, they experience dynamical friction, progressively sinking to the cluster center. Recently, several indicators of the degree of dynamical relaxation of a globular cluster have been proposed, based on the observed radial distribution of blue straggler stars. The most successful is the Alessandrini indicator, or A+ for short, which is the integral of the cumulative distribution of the blue straggler stars minus that of a lighter reference population. A+ correlates with the dynamical age of a cluster both in realistic simulations and in observations. Here I calculate the temporal dependence of the A+ indicator analytically in a simplified model of the evolution of the blue straggler star distribution under dynamical friction only.
Amplitude Retrieval for Channel Estimation of MIMO Systems With One-Bit ADCs

This letter revisits the channel estimation problem for MIMO systems with one-bit analog-to-digital converters (ADCs) through a novel algorithm–Amplitude Retrieval (AR). Unlike the state-of-the-art methods such as those based on one-bit compressive sensing, AR takes a different approach. It accounts for the lost amplitudes of the one-bit quantized measurements, and performs channel estimation and amplitude completion jointly. This way, the direction information of the propagation paths can be estimated via accurate direction finding algorithms in array processing, e.g., maximum likelihood. The upsot is that AR is able to handle off-grid angles and provide more accurate channel estimates. Simulation results are included to showcase the advantages of AR.
Estimating the Random Effect in Big Data Mixed Models.

We consider three problems in high-dimensional Gaussian linear mixed models. Without any assumptions on the design for the fixed effects, we construct an asymptotic $F$-statistic for testing whether a collection of random effects is zero, derive an asymptotic confidence interval for a single random effect at the parametric rate $\sqrt{n}$, and propose an empirical Bayes estimator for a part of the mean vector in ANOVA type models that performs asymptotically as well as the oracle Bayes estimator. We support our results with numerical simulations and provide comparisons with oracle estimators. The procedures developed are applied to the Trends in International Mathematics and Sciences Study (TIMSS) data.
Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-Learning Based Defense Framework

Software Defined Networking (SDN) enables flexible and scalable network control and management. However, it also introduces new vulnerabilities that can be exploited by attackers. In particular, low-rate and slow or stealthy Denial-of-Service (DoS) attacks are recently attracting attention from researchers because of their detection challenges. In this paper, we propose a novel machine learning based defense framework named Q-MIND, to effectively detect and mitigate stealthy DoS attacks in SDN-based networks. We first analyze the adversary model of stealthy DoS attacks, the related vulnerabilities in SDN-based networks and the key characteristics of stealthy DoS attacks. Next, we describe and analyze an anomaly detection system that uses a Reinforcement Learning-based approach based on Q-Learning in order to maximize its detection performance. Finally we outline the complete Q-MIND defense framework that incorporates the optimal policy derived from the Q- Learning agent to efficiently defeat stealthy DoS attacks in SDN-based networks. An extensive comparison of the Q-MIND framework and currently existing methods shows that significant improvements in attack detection and mitigation performance are obtained by Q-MIND.
Inverse quantum measurement problem

Quantum mechanics relates probability of an observable event to the absolute square of the corresponding probability amplitude. It may, therefore, seem that the information about the amplitudes' phases must be irretrievably lost in the experimental data. Yet, there are experiments which report measurements of wave functions, and closely related quantities such as Bohm's velocities and positions of bohmian particles. We invert the question, and ask under which conditions the values of quantum amplitudes can be recovered from observed probability distributions and averages.
Schauder bases having many good block basic sequences

In the study of asymptotic geometry in Banach spaces, a basic sequence which gives rise to a spreading model has been called a good sequence. It is well known that every normalized basic sequence in a Banach space has a subsequence which is good. We investigate the assumption that every normalized block tree relative to a basis has a branch which is good. This combinatorial property turns out to be very strong and is equivalent to the space being $1$-asymptotic $\ell_p$ for some $1\leq p\leq\infty$. We also investigate the even stronger assumption that every block basic sequence of a basis is good. Finally, using the Hindman-Milliken-Taylor theorem, we prove a stabilization theorem which produces a basic sequence all of whose normalized constant coefficient block basic sequences are good, and we present an application of this stabilization.
The Category $\mathcal O$ for Lie Algebras of Vector Fields (I): Tilting Modules and Character Formulas

In this article, we exploit the theory of graded module category with semi-infinite character developed by Soergel in \cite{Soe} to study representations of the infinite dimensional Lie algebras of vector fields $W(n), S(n)$ and $H(n)$ $(n\geq 2)$, and obtain the description of indecomposable tilting modules. The character formulas for those tilting modules are determined.
hood2vec: Identifying Similar Urban Areas Using Mobility Networks

Which area in NYC is the most similar to Lower East Side? What about the NoHo Arts District in Los Angeles? Traditionally this task utilizes information about the type of places located within the areas and some popularity/quality metric. We take a different approach. In particular, urban dwellers' time-variant mobility is a reflection of how they interact with their city over time. Hence, in this paper, we introduce an approach, namely hood2vec, to identify the similarity between urban areas through learning a node embedding of the mobility network captured through Foursquare check-ins. We compare the pairwise similarities obtained from hood2vec with the ones obtained from comparing the types of venues in the different areas. The low correlation between the two indicates that the mobility dynamics and the venue types potentially capture different aspects of similarity between urban areas.
Effects of Bacillus thuringiensis Cry Toxins on Developmental and Reproductive Characteristics of the Predator Orius albidipennis (Hemiptera: Anthocoridae) Under Laboratory Conditions

Abstract The effects of Cry toxins from Bacillus thuringiensis (Berliner) (Bt) on the anthocorid Orius albidipennis Reuter were studied under laboratory conditions. Tritrophic experiments were performed, in which Orius nymphs were fed Helicoverpa armigera (Hübner) larvae reared on a diet with Cry1Ac, Cry1Ab, or Cry2Ab toxins at different concentrations (0, 1, and 10 μg/ml), when supplemented with Ephestia kuehniella Zeller eggs. In complementary experiments, the Bt Cry1Ac toxin was directly fed to Orius nymphs at a very high concentration (1 mg/ml). No effects on prey consumption, developmental time, nymph survival, fecundity, and egg hatching of O. albidipennis were found in either experiment. It can be concluded that the toxins tested do not seem to pose a risk for the anthocorid O. albidipennis, especially when it is exposed through the prey.
MATING BEHAVIOR AND FERTILIZATION SUCCESS OF THREE ONTOGENETIC STAGES OF MALE ROCK SHRIMP RHYNCHOCINETES TYPUS (DECAPODA: CARIDEA)

Abstract The mating behavior and fertilization capacity of three different ontogenetic stages of male rock shrimp Rhynchocinetes typus were examined. The first ontogenetic stage is the typus morphotype, which is similar in morphology to the female. The last ontogenetic stage, the robustus morphotype, is characterized by extremely long third maxillipeds and powerful chelae. During ontogenetic development between these two morphs, males undergo several intermediate molts, which are termed “intermedius. ” In mating experiments with pairs of single males and females, all ontogenetic male stages (typus, intermedius, robustus) behaved in a similar manner. First, they followed the female, then embraced it with the second pereiopods and held it beneath their bodies, encaged by the pereiopods, the 3rd maxillipeds, and the abdomen. Various stimulating and checking behaviors preceded the placement of the first spermatophore, which usually coincided with the start of the spawning process. Most spermatophores were placed during the first 30 minutes of spawning. Following spermatophore placement, males guarded the females during almost the entire spawning process, which could last longer than 120 minutes. The number of spermatophore transfer events during the mating process varied significantly between the two extreme ontogenetic male stages, being typus greater than robustus. No significant differences were found in the percentage of eggs lost by females that were fertilized by the three ontogenetic male stages. These results suggest that all male stages have the same potential to mate successfully with females in a competition-free environment. However, we propose that male mating success may change drastically when different ontogenetic male stages compete directly for access to reproductive females.
Design and Evaluation of an Ultra-Low Power Low Noise Amplifier LNA

This master thesis deals with the study of ultra low power Low Noise Amplifier (LNA) for use in medical implant device. Usually, low power consumption is required for a long battery lifetime and longer operation. The target technology is 90nm CMOS process. First basic principle of LNA is discussed. Then based on a literature review of LNA design, the proposed LNA is presented in sub-threshold region which reduce power consumption through scaling the supply voltage and through scaling current. The circuit implementation and simulations is presented to testify the performance of LNA . Besides the power consumption simulated under the typical supply voltage (1V), it is also measured under some other low supply voltages (down to 0.5V) to investigate the minimum power consumption and the minimum noise figure. Evaluation results show that at a supply voltage of 1V the LNA performs a total power consumption of 20mW and a noise of 1dB. Proper performance is achieved with a current of down to 200uA and supply voltage of down to 0.45V, and a total power consumption of 200uW.