Miracle-dz/UniFAR-FTUs huggingface dataset:
Upregulation of Cardiac IL-10 and Downregulation of IFN-γ in Balb/c IL-4−/− in Acute Chagasic Myocarditis due to Colombian Strain of Trypanosoma cruzi

Inflammatory response in Chagas disease is related to parasite and host factors. However, immune system regulation has not been fully elucidated. Thus, this study is aimed at evaluating IL-4 influence on acute phase of Trypanosoma cruzi experimental infection through dosage of cytokine levels in cardiac homogenate of infected Balb/c WT and Balb/c IL-4−/− as well as its histopathological repercussions. For such purpose, mice were divided into two groups: an infected group with 100 forms of the Colombian strain and an uninfected group. After 21 days of infection, animals were euthanized and the blood, spleen, and heart were collected. The spleen was used to culture splenic cells in 48 h. Subsequently, cytokines TNF-α, IL-12p70, IL-10, IFN-γ, and IL-17 were measured in the blood, culture supernatant, and heart apex by ELISA. The base of the heart was used for histopathological analysis. From these analysis, infected Balb/c IL-4−/− mice showed milder inflammatory infiltrate compared to Balb/c WT, but without changes in nest density and collagen deposition. IL-4 absence culminated in lower cardiac tissue IFN-γ production, although it did not affect TNF-α expression in situ. It also decreased TNF-α systemic production and increased IL-10, both systemically and in situ. In addition, IL-4 absence did not influence IL-17 expression. Splenocytes of IL-4-deficient mice produced higher amounts of IFN-γ, TNF-α, and IL-17 and lower amounts of IL-10. Thus, IL-4 absence in acute phase of experimental infection with T. cruzi Colombian strain reduces myocarditis due to lower IFN-γ production and greater IL-10 production in situ and this pattern is not influenced by splenocyte general repertoire.
Analysis of a Two-Layer Neural Network via Displacement Convexity

Fitting a function by using linear combinations of a large number $N$ of `simple' components is one of the most fruitful ideas in statistical learning. This idea lies at the core of a variety of methods, from two-layer neural networks to kernel regression, to boosting. In general, the resulting risk minimization problem is non-convex and is solved by gradient descent or its variants. Unfortunately, little is known about global convergence properties of these approaches. Here we consider the problem of learning a concave function $f$ on a compact convex domain $\Omega\subseteq {\mathbb R}^d$, using linear combinations of `bump-like' components (neurons). The parameters to be fitted are the centers of $N$ bumps, and the resulting empirical risk minimization problem is highly non-convex. We prove that, in the limit in which the number of neurons diverges, the evolution of gradient descent converges to a Wasserstein gradient flow in the space of probability distributions over $\Omega$. Further, when the bump width $\delta$ tends to $0$, this gradient flow has a limit which is a viscous porous medium equation. Remarkably, the cost function optimized by this gradient flow exhibits a special property known as displacement convexity, which implies exponential convergence rates for $N\to\infty$, $\delta\to 0$. Surprisingly, this asymptotic theory appears to capture well the behavior for moderate values of $\delta, N$. Explaining this phenomenon, and understanding the dependence on $\delta,N$ in a quantitative manner remains an outstanding challenge.
New Algorithms and Lower Bounds for All-Pairs Max-Flow in Undirected Graphs

We investigate the time-complexity of the All-Pairs Max-Flow problem: Given a graph with $n$ nodes and $m$ edges, compute for all pairs of nodes the maximum-flow value between them. If Max-Flow (the version with a given source-sink pair $s,t$) can be solved in time $T(m)$, then an $O(n^2) \cdot T(m)$ is a trivial upper bound. But can we do better? For directed graphs, recent results in fine-grained complexity suggest that this time bound is essentially optimal. In contrast, for undirected graphs with edge capacities, a seminal algorithm of Gomory and Hu (1961) runs in much faster time $O(n)\cdot T(m)$. Under the plausible assumption that Max-Flow can be solved in near-linear time $m^{1+o(1)}$, this half-century old algorithm yields an $nm^{1+o(1)}$ bound. Several other algorithms have been designed through the years, including $\tilde{O}(mn)$ time for unit-capacity edges (unconditionally), but none of them break the $O(mn)$ barrier. Meanwhile, no super-linear lower bound was shown for undirected graphs. We design the first hardness reductions for All-Pairs Max-Flow in undirected graphs, giving an essentially optimal lower bound for the $\textit{node-capacities}$ setting. For edge capacities, our efforts to prove similar lower bounds have failed, but we have discovered a surprising new algorithm that breaks the $O(mn)$ barrier for graphs with unit-capacity edges! Assuming $T(m)=m^{1+o(1)}$, our algorithm runs in time $m^{3/2 +o(1)}$ and outputs a cut-equivalent tree (similarly to the Gomory-Hu algorithm). Even with current Max-Flow algorithms we improve state-of-the-art as long as $m=O(n^{5/3-\varepsilon})$. Finally, we explain the lack of lower bounds by proving a $\textit{non-reducibility}$ result. This result is based on a new quasi-linear time $\tilde{O}(m)$ $\textit{non-deterministic}$ algorithm for constructing a cut-equivalent tree and may be of independent interest.
Joint Source-Channel Coding for the Transmission of Correlated Sources over Two-Way Channels

A joint source-channel coding (JSCC) scheme based on hybrid digital/analog coding is proposed for the transmission of correlated sources over discrete-memoryless two-way channels (DM-TWCs). The scheme utilizes the correlation between the sources in generating channel inputs, thus enabling the users to coordinate their transmission to combat channel noise. The hybrid scheme also subsumes prior coding methods such as rate-one separate source-channel coding and uncoded schemes for two-way lossy transmission, as well as the correlation-preserving coding scheme for (almost) lossless transmission. Moreover, we derive a distortion outer bound for the source-channel system using a genie-aided argument. A complete JSSC theorem for a class of correlated sources and DM-TWCs whose capacity region cannot be enlarged via interactive adaptive coding is also established. Examples that illustrate the theorem are given.
Extracting Secrets from Encrypted Virtual Machines

AMD SEV is a hardware extension for main memory encryption on multi-tenant systems. SEV uses an on-chip coprocessor, the AMD Secure Processor, to transparently encrypt virtual machine memory with individual, ephemeral keys never leaving the coprocessor. The goal is to protect the confidentiality of the tenants' memory from a malicious or compromised hypervisor and from memory attacks, for instance via cold boot or DMA. The SEVered attack has shown that it is nevertheless possible for a hypervisor to extract memory in plaintext from SEV-encrypted virtual machines without access to their encryption keys. However, the encryption impedes traditional virtual machine introspection techniques from locating secrets in memory prior to extraction. This can require the extraction of large amounts of memory to retrieve specific secrets and thus result in a time-consuming, obvious attack. We present an approach that allows a malicious hypervisor quick identification and theft of secrets, such as TLS, SSH or FDE keys, from encrypted virtual machines on current SEV hardware. We first observe activities of a virtual machine from within the hypervisor in order to infer the memory regions most likely to contain the secrets. Then, we systematically extract those memory regions and analyze their contents on-the-fly. This allows for the efficient retrieval of targeted secrets, strongly increasing the chances of a fast, robust and stealthy theft.
How Reliable is the Crowdsourced Knowledge of Security Implementation?

Stack Overflow (SO) is the most popular online Q&A site for developers to share their expertise in solving programming issues. Given multiple answers to a certain question, developers may take the accepted answer, the answer from a person with high reputation, or the one frequently suggested. However, researchers recently observed that SO contains exploitable security vulnerabilities in the suggested code of popular answers, which found their way into security-sensitive highprofile applications that millions of users install every day. This observation inspires us to explore the following questions: How much can we trust the security implementation suggestions on SO? If suggested answers are vulnerable, can developers rely on the community’s dynamics to infer the vulnerability and identify a secure counterpart? To answer these highly important questions, we conducted a comprehensive study on security-related SO posts by contrasting secure and insecure advice with the community-given content evaluation. Thereby, we investigated whether SO’s gamification approach on incentivizing users is effective in improving security properties of distributed code examples. Moreover, we traced the distribution of duplicated samples over given answers to test whether the community behavior facilitates or prevents propagation of secure and insecure code suggestions within SO. We compiled 953 different groups of similar security-related code examples and labeled their security, identifying 785 secure answer posts and 644 insecure answer posts. Compared with secure suggestions, insecure ones had higher view counts (36,508 vs. 18,713), received a higher score (14 vs. 5), and had significantly more duplicates (3.8 vs. 3.0) on average. 34% of the posts provided by highly reputable so-called trusted users were insecure. Our findings show that based on the distribution of secure and insecure code on SO, users being laymen in security rely on additional advice and guidance. However, the communitygiven feedback does not allow differentiating secure from insecure choices. The reputation mechanism fails in indicating trustworthy users with respect to security questions, ultimately leaving other users wandering around alone in a software security minefield.
On the Capacity Region of the Layered Packet Erasure Broadcast Channel with Feedback

In this paper the capacity region of the Layered Packet Erasure Broadcast Channel (LPE-BC) with Channel Output Feedback (COF) available at the transmitter is investigated. The LPE-BC is a high-SNR approximation of the fading Gaussian BC recently proposed by Tse and Yates, who characterized the capacity region for any number of users and any number of layers when there is no COF. This paper derives capacity inner and outer bounds for the LPE-BC with COF for the case of two users and any number of layers. The inner bounds generalize past results for the two-user erasure BC, which is a special case of the LPE-BC with COF with only one layer. The novelty lies in the use of inter-user & inter-layer network coding retransmissions (for those packets that have only been received by the unintended user), where each random linear combination may involve packets intended for any user originally sent on any of the layers. Analytical and numerical examples show that the proposed outer bound is optimal for some LPE-BCs.
Decision-making and Fuzzy Temporal Logic

This paper shows that the fuzzy temporal logic can model figures of thought to describe decision-making behaviors. In order to exemplify, some economic behaviors observed experimentally were modeled from problems of choice containing time, uncertainty and fuzziness. Related to time preference, it is noted that the subadditive discounting is mandatory in positive rewards situations and, consequently, results in the magnitude effect and time effect, where the last has a stronger discounting for earlier delay periods (as in, one hour, one day), but a weaker discounting for longer delay periods (for instance, six months, one year, ten years). In addition, it is possible to explain the preference reversal (change of preference when two rewards proposed on different dates are shifted in the time). Related to the Prospect Theory, it is shown that the risk seeking and the risk aversion are magnitude dependents, where the risk seeking may disappear when the values to be lost are very high.
Enhancing Sound Texture in CNN-based Acoustic Scene Classification

Acoustic scene classification is the task of identifying the scene from which the audio signal is recorded. Convolutional neural network (CNN) models are widely adopted with proven successes in acoustic scene classification. However, there is little insight on how an audio scene is perceived in CNN, as what have been demonstrated in image recognition research. In the present study, the Class Activation Mapping (CAM) is utilized to analyze how the log-magnitude Mel-scale filter-bank (log-Mel) features of different acoustic scenes are learned in a CNN classifier. It is noted that distinct high-energy time-frequency components of audio signals generally do not correspond to strong activation on CAM, while the background sound texture are well learned in CNN. In order to make the sound texture more salient, we propose to apply the Difference of Gaussian (DoG) and Sobel operator to process the log-Mel features and enhance edge information of the time-frequency image. Experimental results on the DCASE 2017 ASC challenge show that using edge enhanced log-Mel images as input feature of CNN significantly improves the performance of audio scene classification.
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair

This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SequenceR, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SequenceR on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SequenceR is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SequenceR captures a wide range of repair operators without any domain-specific top-down design.
Optimal Age over Erasure Channels

Given a source that produces a letter every Ts seconds and an erasure channel that can be used every Tc seconds, we ask what is the coding strategy that minimizes the time-average "age of information" that an observer of the channel output incurs. We will see that one has to distinguish the cases when the source and channel-input alphabets have equal or different size. In the first case, we show that a trivial coding strategy is optimal and a closed form expression for the age may be derived. In the second, we use random coding argument to bound the average age and show that the average age achieved using random codes converges to the optimal average age as the source alphabet becomes large.
StreamBox-HBM: Stream Analytics on High Bandwidth Hybrid Memory

Stream analytics has an insatiable demand for memory and performance. Emerging hybrid memories combine commodity DDR4 DRAM with 3D-stacked High Bandwidth Memory (HBM) DRAM to meet such demands. However, achieving this promise is challenging because (1) HBM is capacity-limited and (2) HBM boosts performance best for sequential access and high parallelism workloads. At first glance, stream analytics appears a particularly poor match for HBM because they have high capacity demands and data grouping operations, their most demanding computations, use random access. This paper presents the design and implementation of StreamBox-HBM, a stream analytics engine that exploits hybrid memories to achieve scalable high performance. StreamBox-HBM performs data grouping with sequential access sorting algorithms in HBM, in contrast to random access hashing algorithms commonly used in DRAM. StreamBox-HBM solely uses HBM to store Key Pointer Array (KPA) data structures that contain only partial records (keys and pointers to full records) for grouping operations. It dynamically creates and manages prodigious data and pipeline parallelism, choosing when to allocate KPAs in HBM. It dynamically optimizes for both the high bandwidth and limited capacity of HBM, and the limited bandwidth and high capacity of standard DRAM. StreamBox-HBM achieves 110 million records per second and 238 GB/s memory bandwidth while effectively utilizing all 64 cores of Intel's Knights Landing, a commercial server with hybrid memory. It outperforms stream engines with sequential access algorithms without KPAs by 7x and stream engines with random access algorithms by an order of magnitude in throughput. To the best of our knowledge, StreamBox-HBM is the first stream engine optimized for hybrid memories.
On hybrid modular recommendation systems for video streaming

The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. We propose the enabler, a hybrid recommendation system which employs various machine-learning (ML) algorithms for learning an efficient combination of several recommendation algorithms and selects the best blending for a given input.Specifically, it integrates three layers, namely, the trainer which trains the underlying recommenders, the blender which determines the most efficient combination of the recommenders, and the tester for assessing the performance of the system. The enabler incorporates a variety of recommendation algorithms that span from collaborative filtering and content-based techniques to ones based on neural networks. It uses the nested cross validation for automatically selecting the best ML algorithm along with its hyper-parameter values for the given input, according to a specific metric. The enabler can be easily extended to include other recommenders and blenders. The enabler has been extensively evaluated in the context of video-streaming. It outperforms various other algorithms, when tested on the Movielens 1M benchmark dataset.encouraging results. Moreover For example, it achieves an RMSE of 0.8206, compared to the state-of-the-art performance of the AutoRec and SVD, 0.827 and 0.845, respectively. A pilot web-based recommendation system was developed and tested in the production environment of a large telecom operator in Greece. Volunteer customers of the video-streaming service provided by the telecom operator employed the system in the context of an out-in-the-wild field study with a post-analysis of the enabler, using the collected ratings of the pilot, demonstrated that it significantly outperforms several popular recommendation algorithms.
Evaluating software defect prediction performance: an updated benchmarking study

Accurately predicting faulty software units helps practitioners target faulty units and prioritize their efforts to maintain software quality. Prior studies use machine-learning models to detect faulty software code. We revisit past studies and point out potential improvements. Our new study proposes a revised benchmarking configuration. The configuration considers many new dimensions, such as class distribution sampling, evaluation metrics, and testing procedures. The new study also includes new datasets and models. Our findings suggest that predictive accuracy is generally good. However, predictive power is heavily influenced by the evaluation metrics and testing procedure (frequentist or Bayesian approach). The classifier results depend on the software project. While it is difficult to choose the best classifier, researchers should consider different dimensions to overcome potential bias.
Multi-Fidelity Recursive Behavior Prediction

Predicting the behavior of surrounding vehicles is a critical problem in automated driving. We present a novel game theoretic behavior prediction model that achieves state of the art prediction accuracy by explicitly reasoning about possible future interaction between agents. We evaluate our approach on the NGSIM vehicle trajectory data set and demonstrate lower root mean square error than state-of-the-art methods.
Deeper and Wider Siamese Networks for Real-Time Visual Tracking

Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed. However, the backbone networks used in Siamese trackers are relatively shallow, such as AlexNet, which does not fully take advantage of the capability of modern deep neural networks. In this paper, we investigate how to leverage deeper and wider convolutional neural networks to enhance tracking robustness and accuracy. We observe that direct replacement of backbones with existing powerful architectures, such as ResNet and Inception, does not bring improvements. The main reasons are that 1) large increases in the receptive field of neurons lead to reduced feature discriminability and localization precision; and 2) the network padding for convolutions induces a positional bias in learning. To address these issues, we propose new residual modules to eliminate the negative impact of padding, and further design new architectures using these modules with controlled receptive field size and network stride. The designed architectures are lightweight and guarantee real-time tracking speed when applied to SiamFC and SiamRPN. Experiments show that solely due to the proposed network architectures, our SiamFC+ and SiamRPN+ obtain up to 9.8%/6.3% (AUC), 23.3%/8.8% (EAO) and 24.4%/25.0% (EAO) relative improvements over the original versions on the OTB-15, VOT-16 and VOT-17 datasets, respectively.
Scaling description of generalization with number of parameters in deep learning

Supervised deep learning involves the training of neural networks with a large number N of parameters. For large enough N, in the so-called over-parametrized regime, one can essentially fit the training data points. Sparsity-based arguments would suggest that the generalization error increases as N grows past a certain threshold N*. Instead, empirical studies have shown that in the over-parametrized regime, generalization error keeps decreasing with N. We resolve this paradox through a new framework. We rely on the so-called Neural Tangent Kernel, which connects large neural nets to kernel methods, to show that the initialization causes finite-size random fluctuations of the neural net output function f N around its expectation . These affect the generalization error for classification: under natural assumptions, it decays to a plateau value in a power-law fashion ∼N−1/2. This description breaks down at a so-called jamming transition N = N*. At this threshold, we argue that diverges. This result leads to a plausible explanation for the cusp in test error known to occur at N*. Our results are confirmed by extensive empirical observations on the MNIST and CIFAR image datasets. Our analysis finally suggests that, given a computational envelope, the smallest generalization error is obtained using several networks of intermediate sizes, just beyond N*, and averaging their outputs.