Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Approach to the Malware Classification Problem using Autoencoders

Detecting malicious code or categorizing it among families has become an increasingly difficult task. Malware1 exploits vulnerabilities and employ sophisticated techniques to avoid their detection and further classification, challenging cybersecurity teams, governments, enterprises, and the ordinary user, causing uncountable losses annually. Traditional machine learning algorithms have been used to attack the problem, although, these methods are heavily relying on domain expertise to be successful. Deep Learning methods requires less dependency on feature engineering, discovering the important features straightly from the raw data, recognizing patterns that humans usually can't. This work presents a deep learning approach for malware multi-class classification based on an unsupervised pre-trained classifier, using opcodes and its operands frequencies as raw data, ignoring knowledge that could be acquired from any known features from the malware families. The results confirmed that the approach is well succeeded and our best model achieved a MacroF1 of 93.14% a competitive result comparing to best-known classifier, since it uses less information about the malware.
Automated Pneumonia Diagnosis using a Customized Sequential Convolutional Neural Network

An automated system that can detect pneumonia on chest X-rays is needed for the rapid diagnosis of the pathology. In the past, numerous attempts have been made to automate the task with varying degree of success. This paper presents a novel, 18-layer deep sequential convolutional neural network based model that is proven to outperform the state of the art system for this task. A publicly available pediatric chest X-ray images dataset consisting of 5,856 X-ray images have been exploited for the training and testing of the model. The model performs the 'normal' v. 'pneumonia' classification task with the classification accuracy of 0.9439 which is around 1.6% better than the state-of-the-art system. The model yields high sensitivity (i.e. 0.99) which is very promising. But the model's limitation is that it has yielded lower than expected specificity (i.e. 0.86). Further improvements may be possible by thoroughly investigating the application of techniques such as transfer learning, fine tuning and more relevant data augmentation.
Analysis of students’ preconceptions of concurrency

In previous literature, several authors have recommended teaching concurrent programming, as the current evolution of IT involves concurrency. However, in order to teach concurrent programming properly, in a constructivist educational learning framework, we need to know the preconceptions students have regarding it. In this paper, we report on the results found from data collected through a questionnaire submitted in secondary schools to 101 students aged from 12 to 15. We detail the preconceptions of concurrent programming we extracted from the questionnaire answers and formulate recommendations toward creating a course teaching concurrent programming.
CACV-tree: A New Computational Approach for Sentence Similarity Modeling

Sentence similarity modeling plays an important role in Natural Language Processing (NLP) tasks, and thus has received much attention. In recent years, due to the success of word embedding, the neural network method has achieved sentence embedding, obtaining attractive performance. Nevertheless, most of them focused on learning semantic information and modeling it as a continuous vector, while the syntactic information of sentences has not been fully exploited. On the other hand, prior works have shown the benefits of structured trees that include syntactic information, while few methods in this branch utilized the advantages of sentence compression. This paper makes the first attempt to absorb their advantages by merging these techniques in a unified structure, dubbed as CACV-tree (Compression Attention Constituency Vector-tree). The experimental results, based on 14 widely used datasets, demonstrate that our model is effective and competitive, compared against state-of-the-art models.
pNOVA: Optimizing Shared File I/O Operations of NVM File System on Manycore Servers

NOVA is a state-of-the-art non-volatile memory file system that logs on a per-file basis to ensure consistency. However, NOVA does not show scalability when multiple threads perform I/Os to a single shared file on Manycore servers. We identified two problems: First, when multiple threads write to a single file restricts parallel writes because of a coarse-grained lock on files in the file system layer. Second, when multiple threads read to a single file, every reader lock acquisition invalidates cachelines of waiting threads and block holders. In order to solve the aforementioned problems, we propose pNOVA, a variant of NOVA that accelerates parallel writes and reads to the same file of multiple threads. First, pNOVA employs a fine-grained range lock, for which we take two implementations, an interval tree based range locking and an atomic operation-based range locking, rather than a coarse-grained lock on files. Second, by defining a range locking variable per each file range, we alleviate the cacheline invalidation problem of a single read counter. Lastly, we address the potential consistency damage incurred by parallel writes to the shared file, and provide consistency using a commit mark based logging method. We evaluated pNOVA on a Manycore server with 120 cores. For microbenchmark, pNOVA showed up to 3.5× higher I/O throughput than NOVA for concurrent shared file write workload. In the Filebench-OLTP benchmark, pNOVA showed up to 1.66× higher transaction processing rate than NOVA.
Development and Evaluation of Optimized Thiolated Chitosan Proniosomal Gel Containing Duloxetine for Intranasal Delivery

Proniosomes offer excellent potential for improved drug delivery, through versatile routes, by overcoming the permeation barriers faced by several drugs. The study was aimed to develop a thiomer gel containing duloxetine proniosomes for the intranasal delivery, improving its bioavailability and brain delivery through olfactory system. Duloxetine-loaded proniosomes were optimized through Design-Expert Software, prepared by coacervation phase separation method and then characterized in vitro for different vesicle features, and permeation enhancement potential using various techniques. The formulation F2, out of all the trials, fulfilled the maximum requisite of highest entrapment efficiency (76.21 ± 1.24%) and minimum vesicle size (223.91 ± 11.07 nm). The F2 was embedded in thiolated chitosan gel rendering it mucoadhesive and further characterized. The in vitro release showed a sustained drug release from the mucoadhesive proniosomal gel with only 54% drug release as compared to that of 71% from proniosome over 8 h, following Higuchi drug release model. Ex vivo permeation studies showed the enhancement ratio for the mucoadhesive proniosomal gel to be 1.86-fold greater than proniosomes, indicating a significant improvement in transmucosal permeation. The results suggest that incorporation of proniosomes into thiolated gel can significantly improve its mucoadhesion and retention time in the nasal cavity for providing a sustained drug release. Thus, gel formulation could be considered as a promising approach for efficient intranasal drug delivery of duloxetine. Graphical Abstract Graphical Abstract
Optimizations with Intelligent Reflecting Surfaces (IRSs) in 6G Wireless Networks: Power Control, Quality of Service, Max-Min Fair Beamforming for Unicast, Broadcast, and Multicast with Multi-antenna Mobile Users and Multiple IRSs

Intelligent reflecting surfaces (IRSs) have received much attention recently and are envisioned to promote 6G communication networks. In this paper, for wireless communications aided by IRS units, we formulate optimization problems for power control under quality of service (QoS) and max-min fair QoS under three kinds of traffic patterns from a base station (BS) to mobile users (MUs): unicast, broadcast, and multicast. The optimizations are achieved by jointly designing the transmit beamforming of the BS and the phase shift matrix of the IRS. For power control under QoS, existing IRS studies in the literature address only the unicast setting, whereas no IRS work has considered max-min fair QoS. Furthermore, we extend our above optimization studies to the novel settings of multi-antenna mobile users or/and multiple intelligent reflecting surfaces. For all the above optimizations, we provide detailed analyses to propose efficient algorithms. To summarize, our paper presents a comprehensive study of optimization problems involving power control, QoS, and fairness in wireless networks enhanced by IRSs.
Exchange bias and quantum anomalous nomalous Hall effect in the MnBi2Te4/CrI3 heterostructure

We propose the zero-field quantum anomalous Hall effect in a 2D van der Waals magnetic heterostructure. The layered antiferromagnetic MnBi2Te4 films have been proposed to be an intrinsic quantum anomalous Hall (QAH) insulator with a large gap. It is crucial to open a magnetic gap of surface states. However, recent experiments have observed gapless surface states, indicating the absence of out-of-plane surface magnetism, and thus, the quantized Hall resistance can only be achieved at the magnetic field above 6 T. We propose to induce out-of-plane surface magnetism of MnBi2Te4 films via the magnetic proximity with magnetic insulator CrI3. A strong exchange bias of ∼40 meV originates from the long Cr-eg orbital tails that hybridize strongly with Te p orbitals. By stabilizing surface magnetism, the QAH effect can be realized in the MnBi2Te4/CrI3 heterostructure. Moreover, the high–Chern number QAH state can be achieved by controlling external electric gates. Thus, the MnBi2Te4/CrI3 heterostructure provides a promising platform to realize the electrically tunable zero-field QAH effect.
Quantifying the Propagation of Fast Coronal Mass Ejections from the Sun to Interplanetary Space by Combining Remote Sensing and Multi-point In Situ Observations

In order to have a comprehensive view of the propagation and evolution of coronal mass ejections (CMEs) from the Sun to deep interplanetary space beyond 1 au, we carry out a kinematic analysis of seven CMEs in solar cycle 23. The events are required to have coordinated coronagraph observations, interplanetary type II radio bursts, and multi-point in situ measurements at the Earth and Ulysses. A graduated cylindrical shell model, an analytical model without free parameters, and a magnetohydrodynamic model are used to derive CME kinematics near the Sun, to quantify the CME/shock propagation in the Sun–Earth space, and to connect in situ signatures at the Earth and Ulysses, respectively. We find that each of the seven CME-driven shocks experienced a major deceleration before reaching 1 au and thereafter propagated with a gradual deceleration from the Earth to larger distances. The resulting CME/shock propagation profile for each case is roughly consistent with all the data, which verifies the usefulness of the simple analytical model for CME/shock propagation in the heliosphere. The statistical analysis of CME kinematics indicates a tendency that the faster the CME, the larger the deceleration, and the shorter the deceleration time period within 1 au. For several of these events, the associated geomagnetic storms were mainly caused by the southward magnetic fields in the sheath region. In particular, the interaction between a CME-driven shock and a preceding ejecta significantly enhanced the pre-existing southward magnetic fields and gave rise to a severe complex geomagnetic storm.
Parity-violating gravity and GW170817 in non-Riemannian cosmology

The cosmological propagation of tensor perturbations is studied in the context of parity-violating extensions of the symmetric teleparallel equivalent of General Relativity theory. This non-Riemannian formulation allows for a wider variety of consistent extensions than the metric formulation of gravity theory. It is found that while many of the possible quadratic terms do not influence the propagation of the gravitational waves, a generic modification predicts a signature that distinguishes the left- and right-handed circular polarizations. The parameters of such modifications can be constrained stringently because the propagation speed of the gravitational waves is scale-dependent and differs from the speed of light.
The cohomology of Torelli groups is algebraic

Abstract The Torelli group of $W_g = \#^g S^n \times S^n$ is the group of diffeomorphisms of $W_g$ fixing a disc that act trivially on $H_n(W_g;\mathbb{Z} )$ . The rational cohomology groups of the Torelli group are representations of an arithmetic subgroup of $\text{Sp}_{2g}(\mathbb{Z} )$ or $\text{O}_{g,g}(\mathbb{Z} )$ . In this article we prove that for $2n \geq 6$ and $g \geq 2$ , they are in fact algebraic representations. Combined with previous work, this determines the rational cohomology of the Torelli group in a stable range. We further prove that the classifying space of the Torelli group is nilpotent.
Compact objects in unimodular gravity

Unimodular gravity provides a theoretical framework that allows for non-conservation of energy-momentum, with possible implications for the cosmological constant problem. It is then important to study the predictions of unimodular gravity in other gravitational regimes. In this work we study stellar dynamics under the assumption of non-conserved energy-momentum. We find that constant density objects can be as compact as Schwarzschild black holes. For polytropic objects, we find modifications due to the non-conservation of energy-momentum that lead to sizeable effects that could be constrained with observational data. Additionally, we revisit and clarify the Reissner-Nordström solution in unimodular gravity. We also study gravitational collapse and discuss possible implications for the growth of structure.
Nanoparticles manipulation in 3D nanotips excited with plasmonic vortex.

Recent advances in nanotechnologies have prompted the need for tools to accurately and non invasively manipulate individual nanoobjects. Among the possible strategies, optical forces have been widely used to enable nano optical tweezers capable of trapping or moving a specimen with unprecedented accuracy. Here, we propose an architecture consisting of a nanotip excited with a plasmonic vortex enabling effective dynamical control of nanoparticles in three dimensions. The optical field generated by the structure can be used to manipulate single dielectric nanoparticles acting on the total angular momentum of light used to illuminate the structure. We demonstrate that it is possible to stably trap or force the beaming of the particle from specific points, thus enabling a new platform for nanoparticle manipulation and sorting.
Interpolated Convolutional Networks for 3D Point Cloud Understanding

Point cloud is an important type of 3D representation. However, directly applying convolutions on point clouds is challenging due to the sparse, irregular and unordered data structure. In this paper, we propose a novel Interpolated Convolution operation, InterpConv, to tackle the point cloud feature learning and understanding problem. The key idea is to utilize a set of discrete kernel weights and interpolate point features to neighboring kernel-weight coordinates by an interpolation function for convolution. A normalization term is introduced to handle neighborhoods of different sparsity levels. Our InterpConv is shown to be permutation and sparsity invariant, and can directly handle irregular inputs. We further design Interpolated Convolutional Neural Networks (InterpCNNs) based on InterpConv layers to handle point cloud recognition tasks including shape classification, object part segmentation and indoor scene semantic parsing. Experiments show that the networks can capture both fine-grained local structures and global shape context information effectively. The proposed approach achieves state-of-the-art performance on public benchmarks including ModelNet40, ShapeNet Parts and S3DIS.
Neural Text Generation with Unlikelihood Training

Neural text generation is a key tool in natural language applications, but it is well known there are major problems at its core. In particular, standard likelihood training and decoding leads to dull and repetitive outputs. While some post-hoc fixes have been proposed, in particular top-$k$ and nucleus sampling, they do not address the fact that the token-level probabilities predicted by the model are poor. In this paper we show that the likelihood objective itself is at fault, resulting in a model that assigns too much probability to sequences containing repeats and frequent words, unlike those from the human training distribution. We propose a new objective, unlikelihood training, which forces unlikely generations to be assigned lower probability by the model. We show that both token and sequence level unlikelihood training give less repetitive, less dull text while maintaining perplexity, giving superior generations using standard greedy or beam search. According to human evaluations, our approach with standard beam search also outperforms the currently popular decoding methods of nucleus sampling or beam blocking, thus providing a strong alternative to existing techniques.
On the Origin of Solar Torsional Oscillations and Extended Solar Cycle

We present a nonlinear mean-field model of the solar interior dynamics and dynamo, which reproduces the observed cyclic variations of the global magnetic field of the Sun, as well as the differential rotation and meridional circulation. Using this model, we explain, for the first time, the extended 22 yr pattern of the solar torsional oscillations, observed as propagation of zonal variations of the angular velocity from high latitudes to the equator during the time equal to the full dynamo cycle. In the literature, this effect is usually attributed to the so-called “extended solar cycle. ” In agreement with the commonly accepted idea, our model shows that the torsional oscillations can be driven by a combination of magnetic field effects acting on turbulent angular momentum transport and the large-scale Lorentz force. We find that the 22 yr pattern of the torsional oscillations can result from a combined effect of an overlap of subsequent magnetic cycles and magnetic quenching of the convective heat transport. The latter effect results in cyclic variations of the meridional circulation in the sunspot formation zone, in agreement with helioseismology results. The variations of the meridional circulation, together with other drivers of the torsional oscillations, maintain their migration to the equator during the 22 yr magnetic cycle, resulting in the observed extended pattern of the torsional oscillations.
A Connection between Spectral Width and Energetics As Well As Peak Luminosity in Fermi Gamma-Ray Bursts

We have revisited the spectral width in the EFE spectrum of gamma-ray bursts with the BEST peak flux P and time-integrated F spectral data provided by the Fermi GBM Burst Catalog. We first compute the BEST spectral widths to compare with some typical physics models. Our analysis results consist with the previous results: blackbody emission alone cannot explain the observed spectrum and most of the observed spectra cannot be interpreted by the synchrotron radiation. We then check the correlations between the spectral width and the observable model-independent burst properties of duration, fluence, and peak flux and find that positive correlations exist between them for both the P and F spectra. Moreover, the short burst appears to extend the correlation found for the long burst. We further demonstrate that these positive correlations also exist in the cosmological rest frame; that is, the spectral width correlates with the isotropic-equivalent energy Eiso as well as the isotropic-equivalent peak luminosity Liso for different energy bands and timescales. Our results show that the wider bursts have larger energy and luminosity. Moreover, short bursts would appear to extend this trend qualitatively. Taking the Amati relation into account, we tend to believe that the spectral shape is related to energy and luminosity.
Elemental Abundances in M31: First Alpha and Iron Abundance Measurements in M31's Giant Stellar Stream

We present the first measurements of [Fe/H] and abundances, obtained using spectral synthesis modeling, for red giant branch stars in M31's giant stellar stream (GSS). The spectroscopic observations, obtained at a projected distance of 17 kpc from M31's center, yielded 61 stars with [Fe/H] measurements, including 21 stars with measurements, from 112 targets identified as M31 stars. The [Fe/H] measurements confirm the expectation from photometric metallicity estimates that stars in this region of M31's halo are relatively metal rich compared to stars in the Milky Way’s inner halo: more than half the stars in the field, including those not associated with kinematically identified substructure, have [Fe/H] abundances . The stars in this field are α-enhanced at lower metallicities, while decreases with increasing [Fe/H] above metallicities of [Fe/H] ≳ −0.9. Three kinematical components have been previously identified in this field: the GSS, a second kinematically cold feature of unknown origin, and M31's kinematically hot halo. We compare probabilistic [Fe/H] and distribution functions for each of the components. The GSS and the second kinematically cold feature have very similar abundance distributions, while the halo component is more metal poor. Although the current sample sizes are small, a comparison of the abundances of stars in the GSS field with abundances of M31 halo and dSph stars from the literature indicate that the progenitor of the stream was likely more massive, and experienced a higher efficiency of star formation, than M31's existing dSphs or the dEs NGC 147 and NGC 185.
PXRF Determination of the Obsidian Industry from the S–F Area of Piani della Corona EBA Settlement (Bagnara Calabra–RC, South Italy)

Abstract Archaeological excavations in the S-F area of Piani della Corona settlement have affected a portion of 2200 m2 of a large plateau (490 m asl). The researches, conducted between 2007 and 2008 by the Superintendence of the Museo Preistorico Etnografico “L. Pigorini” with the Archaeological Superintendence of Calabria, have revealed traces of a large village from the EBA. Before settling in the EBA, human groups belonging Recent Neolithic frequented the plateau. Two burials located near the Bronze Age ditch belong to this period, and there are also sporadic ceramic fragments recovered from the huts. The importance of Piani della Corona is mainly due to its strategic location, serving as a bridge between Calabria, Sicily and the Aeolian Islands, as proven by the archaeological record. A very important role was provided by the obsidian industry, dated after the most distinctive archaeological ceramics class, to the EBA. Out of the 238 obsidian tools found in the S-F area (blades, cores and amorphous splinters), 88 artifacts were analyzed by pXRF. Their chemical determination made it possible to clarify the procurement dynamics and exchange routes with the Aeolian Islands for the EBA, integrating new data in an on-going research.