Miracle-dz/UniFAR-FTUs huggingface dataset:
Is it worth to predict overflows during video streaming over wireless networks?

No. In the paper, we consider compressed video streaming over the wireless channel. From time to time, dynamically changing channel characteristics cause queue overflows, packet drops and, finally, video quality degradation. To eliminate it, the well known I-Frame delay algorithm drops the least important packets when the queue overflows. However, this algorithm starts working only when the queue overflows. Inspired by many works on video bitrate and channel quality prediction, we study how I-Frame Delay would benefit if it knew about future queue overflows and filtered packets in advance. We show that even with the ideal prediction the improvement is negligible.
C&C session detection using random forest

DDoS (Distributed Denial of Service) attack is one of the most used DoS (Denial of Service) attack. It is a distributed attack in which an attacker uses a multitude of compromised computers to attack a single target. Those compromised computers that actually execute the attack are called botnet. To hide their identity, the attacker usually uses a third-party server to control and send attack command to bots, this kind of server is called C&C (command & control) server. The detection of C&C sessions is a strong proof of botnet detection and early detection of DDoS attacks as C&C connections occur before a DDoS attack. Network traffic analysis is an effective method to detect C&C sessions as it is hard to avoid encrypting the payload or change command code. We consider a new feature vector with 55 features, and use a random forest algorithm to build the classifier. Random forest is an ensemble of classifiers that can deal with high-dimension problems. In fact, it can also calculate the importance of features that will help us find the key features responsible for the detection of C&C sessions. Experimental results show that our approach has better performance on C&C session detection.
Changes in T-Cell and Monocyte Phenotypes In Vitro by Schistosoma mansoni Antigens in Cutaneous Leishmaniasis Patients

High levels of proinflammatory cytokines such as IFN-γ and TNF are associated with tissue lesions in cutaneous leishmaniasis (CL). We previously demonstrated that Schistosoma mansoni antigens downmodulate the in vitro cytokine response in CL. In the current study we evaluated whether S. mansoni antigens alter monocyte and T-lymphocyte phenotypes in leishmaniasis. Peripheral blood mononuclear cells of CL patients were cultured with L. braziliensis antigen in the presence or absence of the S. mansoni antigens rSm29, rSmTSP-2- and PIII. Cells were stained with fluorochrome conjugated antibodies and analyzed by flow cytometry. The addition of rSm29 to the cultures decreased the expression of HLA-DR in nonclassical (CD14+CD16++) monocytes, while the addition of PIII diminished the expression of this molecule in classical (CD14++CD16−) and intermediate (CD14++CD16+) monocytes. The addition of PIII and rSmTSP-2 resulted in downmodulation of CD80 expression in nonclassical and CD86 expression in intermediate monocytes, respectively. These two antigens increased the expression of CTLA-4 in CD4+ T cells and they also expanded the frequency of CD4+CD25highFoxp3+ T cells. Taken together, we show that S. mansoni antigens, mainly rSmTSP-2 and PIII, are able to decrease the activation status of monocytes and also to upregulate the expression of modulatory molecules in T lymphocytes.
A Compile-Time Cost Model for Automatic OpenMP Decoupled Software Pipelining Parallelization

The prevalence of control flow, recursive data structures, and general pointer accesses in ordinary programs renders the traditional automatic parallelization techniques unsuitable. OpenMP Decoupled Software Pipelining (DSWP) is proposed to exploit pipeline parallelism lurking in ordinary programs, which cannot be dealt with by traditional techniques. While cost model is important in helping evaluate compiler transformations, guiding the compiler in its optimization process and helping achieve load balancing, existing cost models are too simple to be sufficient for the profit evaluation of OpenMP, especially for DSWPed loops. We propose a compile-time cost model for automatic parallelization profit estimate by extending the existing cost model in Open64 loop nest optimizer (LNO) phase in this paper. Moreover, we improve the OpenMP DSWP algorithm based on our cost model, which increases execution efficiency of automatic parallelization. We evaluate our cost model with loops containing complex memory access patterns and control flow structure, which cannot be dealt with by traditional techniques, and NAS Parallel Benchmarks (NPB) 3.3. 1. As a result, evident performance improvement for generated DSWPed loops and programs are obtained by using our model.
Correlating Multi-Session Attacks via Replay

Intrusion analysis is a manual and time-consuming operation today. It is especially challenging because attacks often span multiple sessions which makes it is hard to diagnose all the damage caused by an attack. One approach for determining dependencies between the sessions of an attack is system-call taint analysis, but this method can generate large numbers of false dependencies due to shared objects such as a password file. In this paper, we propose a novel solution to this problem that replays sessions with tainted and untainted inputs and reasons about multisession dependencies by comparing the session's outputs in the two cases. We present some initial experiments that show that this approach is promising and may allow building powerful intrusion analysis and recovery systems.
Maximum likelihood estimates for exponential type density families

We consider a parametric family of density functions of the type exp(-|x|/sup /spl alpha//2/) for modeling acoustic feature vectors used in automatic recognition of speech. The parameter /spl alpha/ is a measure of the impulsiveness as well as the nongaussian nature of the data. While previous work has focused on estimating the mean and the variance of the data here we attempt to estimate the impulsiveness /spl alpha/ from the data on a maximum likelihood basis. We show that there is a balance between /spl alpha/ and the number of data points N that must be satisfied before maximum likelihood estimation is carried out. Numerical experiments are performed on multidimensional vectors obtained from speech data.
HTLV-1 infection is associated with a history of active tuberculosis among family members of HTLV-1-infected patients in Peru

SUMMARY The purpose of this study was to assess the association between human T-lymphotropic virus 1 (HTLV-1) and a lifetime history of active tuberculosis (TB) among relatives of HTLV-1-infected patients. We reviewed clinical charts of all relatives of HTLV-1-infected index cases who attended our institute in Lima from 1990–2004. The data of 1233 relatives was analysed; 394 (32·0%) were HTLV-1 positive. Eighty-one subjects (6·6%) had a history of active TB, including 45/394 (11·4%) HTLV-1-positive and 36/839 (4·3%) HTLV-1-negative relatives (P<0·001). On multivariate analysis, three factors were associated with TB history: HTLV-1 infection (adjusted OR 2·5, 95% CI 1·6–3·9), age (adjusted OR 1·3, 95% CI 1·1–1·5 per 10-year age increase) and relation to the index case (adjusted OR 2·6, 95% CI 1·3–5·1, for siblings vs. spouses of index cases). In conclusion, HTLV-1 infection may increase the susceptibility to active TB. In populations where both infections are frequent, such an association could affect the dynamics of TB.
A Hybrid Co-evolutionary Particle Swarm Optimization Algorithm for Solving Constrained Engineering Design Problems

This paper presents an effective hybrid co-evolutionary particle swarm optimization algorithm for solving constrained engineering design problems, which is based on simulated annealing (SA) , employing the notion of co-evolution to adapt penalty factors. By employing the SA-based selection for the best position of particles and swarms when updating the velocity in co-evolutionary particle swarm optimization algorithm . Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed, and can reach a high precision .
Reply to Soman et al , Alffenaar

TO THE EDITOR—We thank Metcalfe et al, Alffenaar et al, Soman et al, and Raoult for their interest in our study [1]. Metcalfe et al raise 2 issues about the analysis and reporting of results [2]. Alffenaar and colleagues raise issues related to drug dosing [3]. Soman and colleagues raise concern about standardized treatment regimens [4]. Raoult refers to the potential utility of existing drugs that are not standard antituberculosis drugs [5]. We respond to each of these letters in turn. Metcalfe and colleagues suggest that patients who remain culture negative after 1 month of treatment could not have acquired drug resistance and therefore might have been included in the denominator when calculating the proportion of patients with acquired drug resistance [2]. However, the reality and the math are more complicated for at least 3 reasons. First, we disagree that the target population “is presented as all patients with MDR [multidrug-resistant] tuberculosis starting treatment with [second-line drugs]. ” The target population for this analysis was patients with at least one positive follow-up cultures as displayed in our Figure 1 [1]. Second, we described the excluded subset of patients as having no positive follow-up cultures rather than as having all negative follow-up cultures because these are not the same: 20.8% of the excluded group of patients did not complete treatment (ie, were classified as defaulting) after a median of <12 months (interquartile range, 5–16 months). Because “default” is a World Health Organization (WHO)–defined standard outcome category [6], it was the endpoint in our follow-up of these patients, and we cannot know whether these patients had any subsequent positive cultures. However, the duration of treatment for this group of patients is inadequate. These patients would be at high risk for again becoming culture positive and for acquired drug resistance. Third, many of these patients already had baseline resistance to fluoroquinolones, second-line injectable drugs, or both. It would not be appropriate to include them in the denominator when calculating the frequency of acquired resistance to these same drugs. The exact percentages are uncertain because we did not receive baseline cultures for all these patients and did not recover viable mycobacteria from all cultures received. However, of the 340 viable baseline isolates we received among patients with no positive follow-up cultures, 6.8% had fluoroquinolone resistance, 8.5% had resistance to 1 or more second-line injectable drugs, 11.8% had resistance to either, and 3.5% had resistance to both. Metcalfe and colleagues also discuss our use of propensity scores to control for potential confounding factors. Unlike large randomized controlled trials, in observational studies there is always the possibility of unmeasured confounders. This does not preclude the use of multivariable
Elaboration of Luminescent and Magnetic Hybrid Networks Based on Lanthanide Ions and Imidazolium Dicarboxylate Salts: Influence of the Synthesis Conditions

The syntheses and characterization of four new hybrid coordination networks based on lanthanide ions (Ln = Nd, Sm) and 1,3-carboxymethylimidazolium (L) salt in the presence of oxalic acid (H2ox) are reported. The influence of the synthesis parameters, such as the nature of the lanthanide ion (Nd3+ or Sm3+), the nature of the imidazolium source (chloride [H2L][Cl] or zwitterionic [HL] form) and the presence or not of oxalic acid (H2ox), is discussed. In the presence of oxalic acid, the samarium salt gives only one compound [Sm(L)(ox)(H2O)]·H2O, whatever the nature of the imidazolium ligand, while the neodymium salt leads to three different compounds, [Nd(L)(ox)(H2O)]·H2O, [Nd(L)(ox)0.5(H2O)2][Cl] or [Nd2(L)2(ox)(NO3)(H2O)3][NO3], depending on the imidazolium ligand. In the absence of oxalic acid, gels are obtained, except for the reaction between the neodymium salt and [H2L][Cl], which leads to [Nd(L)(ox)(H2O)]·H2O. All compounds crystallized and their structures were determined by single crystal diffraction. The description of these new phases was consistently supported by ancillary techniques, such as powder X-ray diffraction, thermal analyses and UV-visible-near infrared spectroscopy. The luminescent and magnetic properties of the three pure compounds [Sm(L)(ox)(H2O)]·H2O, [Nd(L)(ox)(H2O)]·H2O and [Nd2(L)2(ox)(NO3)(H2O)3][NO3] were also studied.
PERCEPTUAL DIFFERENCES BETWEEN SPONTANEOUS AND READ ALOUD SPEECH

In this study we tried to find out to what extent the intonation contour, phoneme durations, and spectral features of an utterance contribute to the character of a speech style. For this purpose a listening experiment was carried out with speech utterances from 2 male speakers who each told 'spontaneously' something about themselves and afterwards read out their own literally transcribed spontaneously spoken text. We selected utterances from both speech styles that were identical in wording and that were fluently spoken. By means of TD-PSOLA we systematically exchanged the prosodic features pitch, duration, and energy between the two speech styles. The task of the subjects in the listening experiment was to classify the speech style of the stimuli as either spontaneous or read. It appeared that the subjects could not unif01mly classify the speech style of the original (unmanipulated) utterances, which indicates that there was no clear dividing line between the 'spontaneous' and 'read' speech. It also appeared that intonation, phoneme durations, and spectral features all contain cues to a particular speech style, albeit that their separate influence does not dominate over the rest of the information sources of a speech style.
Using Hardware Performance Events for Instruction-Level Monitoring on the x86 Architecture

Full virtualization has become one of the basic technologies for the development of security applications. This is due to the fact that full virtualization provides important properties such as isolation and transparency that are essential for the development of robust security mechanisms. However, a fact that is often overlooked is that full virtualization also enables developers to make full use of the existing hardware features. By using these features in novel ways, it is possible to create new robust hardware-based security mechanisms. In this paper we make use of the Performance Monitoring Counters (PMCs), which are available on most mainstream processors, to provide PMC-based trapping, a general concept for trapping hardware performance events to the hypervisor. We make use of this concept by proposing a novel approach to monitoring applications running within a virtual machine on the instruction-level from the hypervisor. In contrast to existing approaches, this course of action allows us to not only monitor all instructions of a program, but also enables us to limit the monitoring to specific instruction types. To demonstrate the possibilities of such an approach we implemented a shadow stack that protects the return addresses of functions running within a virtual machine from the hypervisor by only trapping call and return instructions.
Winograd ' s Short DFT Algorithms ∗ C .

In 1976, S. Winograd [20] presented a new DFT algorithm which had signi cantly fewer multiplications than the Cooley-Tukey FFT which had been published eleven years earlier. This new Winograd Fourier Transform Algorithm (WFTA) is based on the typeone index map from Multidimensional Index Mapping with each of the relatively prime length short DFT's calculated by very e cient special algorithms. It is these short algorithms that this section will develop. They use the index permutation of Rader described in the another module to convert the prime length short DFT's into cyclic convolutions. Winograd developed a method for calculating digital convolution with the minimum number of multiplications. These optimal algorithms are based on the polynomial residue reduction techniques of Polynomial Description of Signals: Equation 1 to break the convolution into multiple small ones [2], [12], [14], [23], [21], [9]. The operation of discrete convolution de ned by
Proactive Thermal-Budget-Based Beltway Routing algorithm for thermal-aware 3D NoC systems

The thermal problems of three-dimensional Network-on-Chip (3D NoC) systems become more serious because of die stacking. Besides, for high-performance requirement, the minimal adaptive routing algorithms result in unbalanced traffic load and worse temperature distribution in the system. On the other hand, the conventional selection strategies determine the routing path based on the traffic information, which leads to unawareness of the potential thermal hotspot and huge performance impact. To solve the problems, in this paper, we first define a novel thermal-aware routing index, Mean Time To Throttle (MTTT), which represents the remaining active time of the node before the temperature achieves the alarming level. Based on the information of MTTT, we propose a Proactive Thermal-Budget-Based Beltway Routing (PTB3R) to balance the temperature distribution of the NoC system. The experimental results show that the proposed PTB3R can help to reduce the number of throttled nodes by 25.56%~86.95% and improve network throughput by around 15.04%~19.87%.
Generative and Latent Mean Map Kernels

We introduce two kernels that extend the mean map, which embeds probability measures in Hilbert spaces. The generative mean map kernel (GMMK) is a smooth similarity measure between probabilistic models. The latent mean map kernel (LMMK) generalizes the non-iid formulation of Hilbert space embeddings of empirical distributions in order to incorporate latent variable models. When comparing certain classes of distributions, the GMMK exhibits beneficial regularization and generalization properties not shown for previous generative kernels. We present experiments comparing support vector machine performance using the GMMK and LMMK between hidden Markov models to the performance of other methods on discrete and continuous observation sequence data. The results suggest that, in many cases, the GMMK has generalization error competitive with or better than other methods.
Design Optimization of Supersonic Wings Using Evolutionary Algorithms

Feasibility of evolutionary computations for supersonic wing design optimization was demonstrated by the single-objective aerodynamic optimization and multiobjective, multidisciplinary optimization. The aerodynamic optimization problem seeks an optimal supersonic wing shape using the Euler equations. The multidisciplinary optimization problem seeks an optimal supersonic wing planform shape using linearized aerodynamics and wing weight algebraic estimation.
MHD simulations of radiative jets from young stellar objects Hα emission

We study the Hα emission from jets using two-dimensional axisymmetrical simulations. We compare the emission obtained from hydrodynamic (HD) simulations with that obtained from magnetohydrodynamics (MHD) simulations. The magnetic field is supposed to be present in the jet only, and with a toroidal configuration. The simulations have time-dependent ejection velocities and different intensities for the initial magnetic field. The results show an increase in the Hα emission along the jet for the magnetized cases with respect to the HD case. The increase in the emission is due to a better collimation of the jet in the MHD case, and to a small increase in the shock velocity. These results could have important implications for the interpretation of the observations of jets from young stellar objects.
Toward Full-Duplex Multihop Multiflow—A Study of Non-Layered Two Unicast Wireless Networks

Starting from the elemental 2 × 2 × 2 interference channel, there has been much progress in the understanding of multihop multiflow wireless networks through degrees of freedom (DoF) studies that have produced important ideas such as (aligned) interference neutralization. However, much of this progress has been limited to layered connectivity models that are essentially motivated by the assumption that wireless networks can only operate in half-duplex mode. Motivated by recent breakthroughs in full-duplex radio technology, in this work, we expand the 2 × 2 × 2 interference channel model beyond layered connectivity in order to study the impact of full-duplex operation. In particular, we study the impact of intra-layer connectivity between relays that are in the same layer, the impact of direct inter-layer connectivity between sources and destinations, and the impact of intermediate inter-layer connectivity that connects sources or destinations, not directly to each other, but to relay nodes in non-adjacent layers in a 2 × 2 × 2 × 2 interference channel. We show that intra-layer links and intermediate inter-layer interference links do not cause a collapse of DoF, whereas direct interference links do cause a collapse of DoF.
Functional Control of Transplantable Human ESC‐Derived Neurons Via Optogenetic Targeting

Current methods to examine and regulate the functional integration and plasticity of human ESC (hESC)‐derived neurons are cumbersome and technically challenging. Here, we engineered hESCs and their derivatives to express the light‐gated channelrhodopsin‐2 (ChR2) protein to overcome these deficiencies. Optogenetic targeting of hESC‐derived neurons with ChR2 linked to the mCherry fluorophore allowed reliable cell tracking as well as light‐induced spiking at physiological frequencies. Optically induced excitatory and inhibitory postsynaptic currents could be elicited in either ChR2+ or ChR2− neurons in vitro and in acute brain slices taken from transplanted severe combined immunodeficient (SCID) mice. Furthermore, we created a clonal hESC line that expresses ChR2‐mCherry under the control of the synapsin‐1 promoter. On neuronal differentiation, ChR2‐mCherry expression was restricted to neurons and was stably expressed for at least 6 months, providing more predictable light‐induced currents than transient infections. This pluripotent cell line will allow both in vitro and in vivo analysis of functional development as well as the integration capacity of neuronal populations for cell‐replacement strategies. STEM CELLS 2010;28:2008–2016
Plagiarism detection among source codes using adaptive local alignment of keywords

This paper proposes a new method for detecting plagiarized pairs of source codes among a large set of source codes. The typical algorithms for detecting code plagiarism, which are largely exploited up to now, are based on Greedy-String Tiling or on local alignments of the two strings. This paper introduces a variant of the local alignment, namely, the adaptive local alignment, which exploits an adaptive similarity matrix. Each entry of the adaptive similarity matrix is the logarithm of the probabilities of the keywords based on the frequencies in a given set of programs. We experimented with this method using a set of programs submitted to more than 10 real programming contests. According to the experimental results, the distribution of the adaptive local alignment is more sensitive than that of the previous local alignments that used a fixed similarity matrix (+1 for match, −1 for mismatch, and −2 for gap), and the performance of the adaptive local alignment is superior to Greedy-String Tiling for detecting various plagiarism cases.
TV Sparsifying MR Image Reconstruction in Compressive Sensing

In this paper, we apply alternating minimization method to sparse image reconstruction in compressed sensing. This approach can exactly reconstruct the MR image from under-sampled k-space data, i.e., the partial Fourier data. The convergence analysis of the fast method is also given. Some MR images are employed to test in the numerical experi-ments, and the results demonstrate that our method is very efficient in MRI reconstruction.