Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploration of Low Numeric Precision Deep Learning Inference Using Intel FPGAs

CNNs have been shown to maintain reasonable classification accuracy when quantized to lower precisions. Quantizing to sub 8-bit activations and weights can result in accuracy falling below an acceptable threshold. Techniques exist for closing the accuracy gap of limited numeric precision typically by increasing computation. This results in a trade-off between throughput and accuracy and can be tailored for different networks through various combinations of activation and weight data widths. Hardware architectures like FPGAs provide the opportunity for data width specific computation through unique logic configurations leading to highly optimized processing that is unattainable by full precision networks. Ternary and binary weighted networks offer an efficient method of inference for 2-bit and 1-bit data respectively. Most hardware architectures can take advantage of the memory storage and bandwidth savings that come along with smaller datapaths, but very few architectures can take advantage of limited numeric precision at the computation level. In this paper, we present a hardware design for FPGAs that takes advantage of bandwidth, memory, power, and computation savings of limited numerical precision data. We provide insights into the trade-offs between throughput and accuracy for various networks and how they map to our framework. Further, we show how limited numeric precision computation can be efficiently mapped onto FPGAs for both ternary and binary cases. Starting with Arria 10, we show a 2-bit activation and ternary weighted AlexNet running in hardware that achieves 3,700 images per second on the ImageNet dataset with a top-1 accuracy of 0.49. Using a hardware modeler designed for our low numeric precision framework we project performance most notably for a 55.5 TOPS Stratix 10 device running a modified ResNet-34 with only 3.7% accuracy degradation compared with single precision.
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversation Systems in E-commerce

Multi-turn information-seeking conversation systems are an important and challenging research topic. Although some advanced neural matching models have been proposed for this task, there are at least two problems with them: the models are generally not efficient for industrial applications, and they rely on a large amount of labeled data, which may not be available. In this paper, we study transfer learning for multi-turn information seeking conversation systems. We propose an efficient and effective multi-turn conversation model based on convolutional neural networks. We further extend our model to adapt the knowledge learned from a resource-rich domain to further boost our model performance. We have deployed our model in an industrial bot 1 application and observed a significant improvement over the existing online model.
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I. Roth,1 R. Kueng,2 S. Kimmel,3 Y. -K. Liu,4, 5 D. Gross,6 J. Eisert,1 and M. Kliesch7 Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, Germany Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, USA Department of Computer Science, Middlebury College, USA National Institute of Standards and Technology, Gaithersburg, USA Joint Center for Quantum Information and Computer Science (QuICS), University of Maryland, College Park, USA Institute for Theoretical Physics, University of Cologne, Germany Institute of Theoretical Physics and Astrophysics, National Quantum Information Centre, University of Gdańsk, Poland
Cross-Lingual Argumentation Mining

Argumentation mining is an important task given the multitude of textual sources and contexts it can be applied to. Many previous studies aimed at the analysis of legal documents, or posts in web fora, also focusing on English contents. However, there are other languages and other types of natural language content where such techniques can be applied. In this document we present an argumentation mining sys-tem to extract arguments from news articles or essays, in Portuguese and English, using a cross-language machine learning approach based on deep neural networks. The document describes the methodology and the datasets used for experimentation, as well as the achieved results of both monolingual and cross-lingual experiments
Diagnostics of ionization front and plasma within femtosecond laser interferometer

Based on analyzing the condition of transmission through high density plasma and its ionization front, and the theory of detecting plasma with holographic interferometer, diagnostic experiments of ionization front and plasma ionized by high laser electric field has been reported in this paper. The Mach-Zehnder interferometer with the femosecond laser of center wavelength of 800nm and pulse duration of 58fs has been used to get interference images in CCD camera. By Abel inversion, the density map of plasma has been obtained based on the interference image. Evolution pictures of ionization front within the time or propagation distance have been reported and ionization front’s size has been estimated with the help of 4mm diameter of nozzle. One good diagnosis method for ionization front and plasma has been given within these experiments.
PENGARUH TEKNIK RELAKSASI NAFAS DALAM TERHADAP KECEMASAN DALAM MENGHADAPI PERSALINAN PADA IBU HAMIL

Kecemasan pada ibu hamil trimester III terkesan lebih kompleks dan meningkat kembali dibandingkan trimester sebelumnya, salah satu penyebabnya karena akan menghadapi persalinan. Beberapa penelitian menunjukkan bahwa ibu hamil sebagian besar mengalami kecemasan dalam menghadapi persalinan. Selain itu, survei yang dilakukan oleh spesialis kejiwaan menunjukkan lebih dari 60% wanita hamil mengalami distress. Teknik Relaksasi Nafas Dalam dapat meningkatkan ventilasi alveoli, memelihara pertukaran gas sehingga dapat mengurangi stress dan menurunkan kecemasan. Pemberian terapi tersebut dapat mengurangi kecemasan pada ibu hamil dalam menghadapi persalinan. Tujuan penelitian adalah mengetahui pengaruh pemberian Teknik Relaksasi Nafas Dalam terhadap kecemasan menghadapi persalinan pada ibu hamil di Wilayah Kerja Puskesmas Sukorame Kota Kediri. Pada penelitian ini dilakukan identifikasi tingkat kecemasan sebelum diberikan Teknik Relaksasi Nafas Dalam kemudian responden diberikan terapi tersebut dan selanjutnya dilakukan post test untuk mengetahui tingkat kecemasan setelah diberikan perlakuan. Desain penelitian one group pre- post test design menggunakan pendekatan cross sectional yang dilaksanakan pada bulan Juli 2017 selama 2 minggu dengan jumlah sampel sebanyak 16 responden dan pengambilan sampel dilakukan dengan cara simple random sampling. Hasil penelitian menunjukkan nilai p=0.03 (p<0.05) yang berarti bahwa H0 ditolak dan H1 diterima yaitu ada pengaruh pemberian Teknik Relaksasi Nafas Dalam terhadap tingkat kecemasan dalam menghadapi persalinan pada ibu hamil. Hasil penelitian ini diharapkan dapat digunakan sebagai salah satu alternatif terapi yang dapat digunakan dalam memberikan asuhan kebidanan pada ibu hamil yang mengalami kecemasan dan menambah wawasan pengetahuan kebidanan terutama tentang kehamilan
Multi-Objective Optimization of Energy Consumption of GUIs in Android Apps

The number of mobile devices sold worldwide has exponentially increased in recent years, surpassing that of personal computers in 2011. Such devices daily download and run millions of apps that take advantage of modern hardware features (e.g., multi-core processors, large Organic Light-Emitting Diode—OLED—screens, etc.) to offer exciting user experiences. Clearly, there is a cost to pay in terms of energy consumption and, in particular, of reduced battery life. This has pushed researchers to investigate how to reduce the energy consumption of apps, for example, by optimizing the color palette used in the app’s GUI. Whilst past research in this area aimed at optimizing energy while keeping an acceptable level of contrast, this article proposes an approach, named Gui Energy Multi-objective optiMization for Android apps (GEMMA), for generating color palettes using a multi-objective optimization technique, which produces color solutions optimizing energy consumption and contrast while using consistent colors with respect to the original color palette. The empirical evaluation demonstrates (i) substantial improvements in terms of the three different objectives, (ii) a concrete reduction of the energy consumption as assessed by a hardware power monitor, (iii) the attractiveness of the generated color compositions for apps’ users, and (iv) the suitability of GEMMA to be adopted in industrial contexts.
PENGARUH MEDIA PEMBELAJARAN INTERAKTIF TERHADAP PENINGKATKAN KETERAMPILAN BERPIKIR KRITIS SISWA

Tujuan penelitian ini untuk mengetahui pengaruh media pembelajaran interaktif pada konsep termokimia terhadap peningkatan keterampilan berpikir kritis siswa di kelas XI-IPA MAN Banda Aceh 1.Desain penelitian eksperimen semu ( quasy exsperimen ) dengan rancangan the pretest and posttest nonequivalent control group design . Data yang diperoleh dianalisis melalui pendekatan kuantitatif dengan uji statistik uji t dan N-Gain . Berdasarkan hasil analisis data, didapatkan sebagai berikut: pemanfaatan media pembelajaran interaktif pada materi termokimia terjadi peningkatan keterampilan berpikir kritis siswa. Peningkatan tertinggi terjadi pada memfokuskan pertanyaan indikator dengan N-Gain sebesar 74,583% dan terdapat perbedaan keterampilan berpikir kritis antara kelas eksperimen dengan kelas kontrol dengan nilai rata-rata hasil belajar kelas eksperimen sebesar 76,75, dan kelas kontrol sebesar 70,5. dengan hasil analisis uji t sebesar t hitung 2,107 > t tabel 2,093, maka dapat disimpulkan bahwa pemanfaatan media pembelajaran interaktif pada materi termokimia terdapat perbedaan nyata terhadap peningkatan keterampilan berpikir kritis siswa
A Fuzzy Logic Feedback Filter Design Tuned with PSO for L 1 Adaptive Controller I

L1adaptive controller has been recognized for having a structure that allows decoupling between robustness and adaption owing to the introduction of a low pass filter with adjustable gain in the feedback loop. The trade-off between performance, fast adaptation and robustness, is the main criteria when selecting the structure or the coefficients of the filter. Several off-line methods with varying levels of complexity exist to help finding bounds or initial values for these coefficients. Such values may require further refinement using trial-and-error procedures upon implementation. Subsequently, these approaches suggest that once implemented these values are kept fixed leading to sub-optimal performance in both speed of adaptation and robustness. In this paper, a new practical approach based on fuzzy rules for online continuous tuning of these coefficients is proposed. The fuzzy controller is optimally tuned using Particle Swarm Optimization (PSO) taking into accounts both the tracking error and the controller output signal range. The simulation of Email addresses: h.a. hashiim@gmail.com (Hashim A. Hashim), selferik@kfupm.edu. sa (Sami El-Ferik), mabido@kfupm.edu. sa (Mohamed A. Abido) Expert Systems with Applications To cite this article: Hashim A. Hashim, Sami El-Ferik, and Mohamed A. Abido. ”A fuzzy logic feedback filter design tuned with PSO for L1 adaptive controller. ” Expert Systems with Applications 42, no. 23 (2015): 9077-9085. several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance when benchmarked to L1adaptive controller with fixed filter coefficients.
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima

We propose stochastic optimization algorithms that can find local minima faster than existing algorithms for nonconvex optimization problems, by exploiting the third-order smoothness to escape non-degenerate saddle points more efficiently. More specifically, the proposed algorithm only needs $\tilde{O}(\epsilon^{-10/3})$ stochastic gradient evaluations to converge to an approximate local minimum $\mathbf{x}$, which satisfies $\|\nabla f(\mathbf{x})\|_2\leq\epsilon$ and $\lambda_{\min}(\nabla^2 f(\mathbf{x}))\geq -\sqrt{\epsilon}$ in the general stochastic optimization setting, where $\tilde{O}(\cdot)$ hides logarithm polynomial terms and constants. This improves upon the $\tilde{O}(\epsilon^{-7/2})$ gradient complexity achieved by the state-of-the-art stochastic local minima finding algorithms by a factor of $\tilde{O}(\epsilon^{-1/6})$. For nonconvex finite-sum optimization, our algorithm also outperforms the best known algorithms in a certain regime.
Fierce Selection and Interference in B-Cell Repertoire Response to Chronic HIV-1

Abstract During chronic infection, HIV-1 engages in a rapid coevolutionary arms race with the host’s adaptive immune system. While it is clear that HIV exerts strong selection on the adaptive immune system, the characteristics of the somatic evolution that shape the immune response are still unknown. Traditional population genetics methods fail to distinguish chronic immune response from healthy repertoire evolution. Here, we infer the evolutionary modes of B-cell repertoires and identify complex dynamics with a constant production of better B-cell receptor (BCR) mutants that compete, maintaining large clonal diversity and potentially slowing down adaptation. A substantial fraction of mutations that rise to high frequencies in pathogen-engaging CDRs of BCRs are beneficial, in contrast to many such changes in structurally relevant frameworks that are deleterious and circulate by hitchhiking. We identify a pattern where BCRs in patients who experience larger viral expansions undergo stronger selection with a rapid turnover of beneficial mutations due to clonal interference in their CDR3 regions. Using population genetics modeling, we show that the extinction of these beneficial mutations can be attributed to the rise of competing beneficial alleles and clonal interference. The picture is of a dynamic repertoire, where better clones may be outcompeted by new mutants before they fix.
Altered circadian behavior and light sensing in mouse models of Alzheimer’s disease

Circadian symptoms have long been observed in Alzheimer’s disease (AD) and often appear before cognitive symptoms, but the mechanisms underlying circadian alterations in AD are poorly understood. We studied circadian re-entrainment in AD model mice using a “jet lag” paradigm, observing their behavior on a running wheel after a six hour advance in the light:dark cycle. Female 3xTg mice, which carry mutations producing progressive amyloid beta and tau pathology, re-entrained following jet lag more rapidly than age-matched wild type controls at both 8 and 13 months of age. This re-entrainment phenotype has not been previously reported in a murine AD model. Because microglia are activated in AD and in AD models, and inflammation can affect circadian rhythms, we hypothesized that microglia contribute to this re-entrainment phenotype. To test this, we used the colony stimulating factor 1 receptor (CSF1R) inhibitor PLX3397, which rapidly depletes microglia from the brain. Microglia depletion did not alter re-entrainment in either wild type or 3xTg mice, demonstrating that microglia activation is not acutely responsible for the re-entrainment phenotype. To test whether mutant tau pathology is necessary for this behavioral phenotype, we repeated the jet lag behavioral test with the 5xFAD mouse model, which develops amyloid plaques, but not neurofibrillary tangles. As with 3xTg mice, 7-month-old female 5xFAD mice re-entrained more rapidly than controls, demonstrating that mutant tau is not necessary for the re-entrainment phenotype. Because AD pathology affects the retina, we tested whether differences in light sensing may contribute to altered entrainment behavior. 3xTg mice demonstrated heightened negative masking, an SCN-independent circadian behavior measuring responses to different levels of light, and re-entrained dramatically faster than WT mice in a jet lag experiment performed in dim light. 3xTg mice show a heightened sensitivity to light as a circadian cue that may contribute to accelerated photic re-entrainment. Together, these experiments demonstrate novel circadian behavioral phenotypes with heightened responses to photic cues in AD model mice which are not dependent on tauopathy or microglia.
FuSeBMC v4: Improving code coverage with smart seeds via fuzzing and static analysis

Bounded model checking (BMC) and fuzzing techniques are among the most eﬀective methods for detecting errors and security vulnerabilities in software. However, there is still a shortcoming in detecting these errors due to the inability to cover large areas in the target code. Coverage standards and measures are also an excellent way to ascertain the eﬀectiveness of the test suite. We propose FuSeBMC v4, a test generator that relies on smart seeds to improve the hybrid fuzzer to achieve high C programs coverage. First, FuSeBMC analyses and incrementally injects goal labels into the given C program to guide BMC and Evolutionary Fuzzing engines. Also, It ranks these goal labels according to the given strategy. After that, the engines are employed to produce smart seeds quickly to use later. Then, FuSeBMC coordinates between the engines and seed distribution by the Tracer. This Tracer generally manages the tool to record the goals covered and transfer the information between the engines by providing a shared memory to harness the power and take advantage of the power of each engine. So that the BMC engine helps give the seed that makes the fuzzing engine not struggle with complex mathematical guards. Furthermore, Tracer evaluates test cases dynamically to convert high-impact cases into seeds for subsequent test fuzzing. As a result, we received three awards for participation in the fourth international competition in software testing (Test-Comp 2022), outperforming all state-of-the-art tools in every category, including the coverage category.
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs: Supplementary Material

Part 1: EI-PUC. To show the result for EI-PUC, we construct a problem instance with a discrete finite domain and no observation noise. Let and δ be two strictly positive real numbers and let K = d(1 + δ)/ e. Suppose the domain is X = {0, 1, . ,K + 1} and that the overall budget of the problem is 1 + δ. The prior on f(x) is independent across x ∈ X, all points have zero mean under the prior, and the costs c(x) are all known. We assume that the point x = 0 has already been measured and has value f(0) = 0. The next K points are “low-variance, low-cost” and the final point is “high-variance, high-cost. ”
A multiscale study of stochastic spatially-extended conductance-based models for excitable systems

The purpose of the present thesis is the mathematical study of probabilistic models for the generation and propagation of an action potential in neurons and more generally of stochastic models for excitable cells. Indeed, we want to study the effect of noise on multiscale spatially extended excitable systems. We address the intrinsic as well as the extrinsic source of noise in such systems. Below, we first describe the mathematical content of the thesis. We then consider the physiological situation described by the considered models. To study the intrinsic or internal noise, we consider Hilbert-valued Piecewise Deterministic Markov Processes (PDMPs). We are interested in the multiscale and long time behavior of these processes. In a first part, we study the case where the fast component is a discrete component of the PDMP. We prove a limit theorem when the speed of the fast component is accelerated. In this way, we obtain the convergence of a class of Hilbert-valued PDMPs with multiple timescales toward so-called averaged processes which are, in some cases, still PDMPs. Then, we study the fluctuations of the multiscale model around the averaged one and show that the fluctuations are Gaussians through the proof of a Central Limit Theorem. In a second part, we consider the case where the fast component is itself a PDMP. This requires knowledge about the invariant measure of Hilbert-valued PDMPs. We show, under some conditions, the existence and uniqueness of an invariant measure and the exponential convergence of the process toward this measure. For a particular class of PDMPs that we call diagonals, the invariant measure is made explicit. This, in turn, allow us to obtain averaging results for "fast" PDMPs fully coupled to "slow" continuous time Markov chains. To study the extrinsic or external noise, we consider systems of Stochastic Partial Differential Equations (SPDEs) driven by colored noises. On bounded domains of $\mathbb{R}^2$ or $\mathbb{R}^3$, we analyze numerical schemes based on finite differences in time and finite elements in space. For a class of linear SPDEs, we obtain the strong error of convergence of such schemes. For simulations, we show the emergence of re-entrant patterns due to the presence of noise in spatial domains of dimension two for the Barkley and Mitchell-Schaeffer models.
Joint Learning of Intrinsic Images and Semantic Segmentation Supplementary Material

Our models are implemented using the Adadelta [1] optimizer with learning rate of 0.01. Convolution weights are initialized using a normal distribution with a weight decay factor of 1e-9. The input to the networks are fixed to a resolution of 352 × 480. The images are normalized to the range of [0, 1] as the preprocessing step. The batch sizes are fixed at 16 for all experiments. In addition, in the last decoder block, the feature dimension is reduced to the expected output dimensions. For the semantic segmentation task, the loss is weighted per class, since the classes are not equally distributed. Furthermore, for the output of the semantic segmentation task, the feature dimensions are unchanged and is simply convolved at the same feature dimension one additional time. This produces an output corresponding to the 16 class labels. Baseline network architecture: The encoder part is composed of 6 convolution blocks with 3 × 3 kernels and stride of 2 having [16, 32, 64, 128, 256, 256] feature maps. The encoder part is mirrored to build the decoder. Before convolving the feature maps of the decoder part, previous layer’s feature maps are first up-sampled and concatenated with their corresponding encoder features. All convolutions are followed by batch normalization [2] and ReLU. Gamma parameters for SMSE: For all the experiments involving intrinsic image decomposition task, to form the combined MSE and SMSE loss, we followed the setup of [3] and set γSMSE to 0.95 and γMSE to 0.05 for Equation 6 in the main manuscript. Nonetheless, we conducted a small experiment to see the effect of the gamma parameters for SMSE. Table 1 provides the average intrinsic image decomposition errors. The small experiment suggest that giving higher weight to γSMSE tends to improve the results.