Miracle-dz/UniFAR-FTUs huggingface dataset:
Scene Understanding and Semantic Mapping for Unmanned Ground Vehicles Using 3D Point Clouds

The perception and understanding of the surrounding environment are the foundation of UGV navigation and mapping. This paper proposed a semantic mapping method for UGV in large-scale outdoor environment. The 3D laser point clouds are transformed into 2D optimal depth and vector length graph models. The ODVL images are divided into super pixels, and 20 dimensional texture features are extracted from each super pixel. Based on the texture features, the Gentle-AdaBoost algorithm is used to classify the super pixels to achieve scene understanding. According to result of scene understanding, the environments are divided into scene nodes and road nodes. The semantic map of the outdoor environment is obtained by generating topological relations between the scene nodes and the road nodes. Real semantic map for large-scale outdoor environment is built to verify the effectiveness and practicability of the proposed method.
Disrupting Gram-Negative Bacterial Outer Membrane Biosynthesis through Inhibition of the Lipopolysaccharide Transporter MsbA

There is a critical need for new antibacterial strategies to counter the growing problem of antibiotic resistance. In Gram-negative bacteria, the outer membrane (OM) provides a protective barrier against antibiotics and other environmental insults. ABSTRACT There is a critical need for new antibacterial strategies to counter the growing problem of antibiotic resistance. In Gram-negative bacteria, the outer membrane (OM) provides a protective barrier against antibiotics and other environmental insults. The outer leaflet of the outer membrane is primarily composed of lipopolysaccharide (LPS). Outer membrane biogenesis presents many potentially compelling drug targets as this pathway is absent in higher eukaryotes. Most proteins involved in LPS biosynthesis and transport are essential; however, few compounds have been identified that inhibit these proteins. The inner membrane ABC transporter MsbA carries out the first essential step in the trafficking of LPS to the outer membrane. We conducted a biochemical screen for inhibitors of MsbA and identified a series of quinoline compounds that kill Escherichia coli through inhibition of its ATPase and transport activity, with no loss of activity against clinical multidrug-resistant strains. Identification of these selective inhibitors indicates that MsbA is a viable target for new antibiotics, and the compounds we identified serve as useful tools to further probe the LPS transport pathway in Gram-negative bacteria.
Optimizing data stream processing for large‐scale applications

Stream processing systems are designed to analyze data arriving in real time and using continuous queries and respond when a specific event or sequence of events are detected. An important aspect of these systems is Streaming Analytics, which facilitates statistical calculations on continuous data within the stream. These systems must be designed to handle high volumes of data, be scalable, and accommodate a multitude of long‐lived concurrently running analytics. The challenges involved in the development of stream processing include on‐the‐fly transformation of data streams to match the query needs of users and the ability to model stream transformations to detect overlaps and possibilities for optimizations and to specify a methodology to deliver optimizations. In particular, this work focuses on exposing data stream application internals in order to detect reusable parts and then consolidate applications to optimize computational resource usage. The Streaming Data Analytics Model presented in this paper adopts a declarative approach that enables processing and manipulation of data streams in a simple manner while facilitating powerful optimizations necessary for managing high volumes of streaming data in real time. An evaluation is provided to demonstrate in both theoretical and quantitative aspects the high performance offered by our approach.
The Genetics of Pemphigus Vulgaris

Pemphigus vulgaris (PV) is a severe autoimmune blistering disease caused by auto-antibodies (auto-Abs) directed against epithelial desmosomal components and leading to disruption of cell-cell adhesion. The exact mechanisms underlying the disease pathogenesis remain unknown and treatment is still based on immunosuppressive drugs, such as corticosteroids, which are associated with potentially significant side effects. Ethnic susceptibility, familial occurrence, and autoimmune comorbidity, suggest a genetic component to the pathogenesis of the disease, which, if discovered, could advance our understanding of PV pathogenesis and thereby point to novel therapeutic targets for this life-threatening disorder. In this article, we review the evidence for a genetic basis of PV, summarize the different approaches used to investigate susceptibility traits for the disease and describe past and recent discoveries regarding genes associated with PV, most of which belong to the human leukocyte antigen (HLA) locus with limited data regarding association of non-HLA genes with the disease.
Lipid Profile of Xylella fastidiosa Subsp. pauca Associated With the Olive Quick Decline Syndrome

Lipids, components of the plasma and intracellular membranes as well as of droplets, provide different biological functions related to energy, carbon storage, and stress responses. Bacterial species display diverse membrane composition that changes in response to the different environmental conditions. During plant–pathogen interactions, lipids might have roles in several aspects such as recognition, signal transduction, and downstream responses. Among lipid entities, free fatty acids (FFAs) and their oxidized form, the oxylipins, represent an important class of signaling molecules in host–pathogen perception, especially related to virulence and defense. In bacteria, FFAs (e.g., diffusible signaling factors) and oxylipins have a crucial role in modulating motility, biofilm formation, and virulence. In this study, we explore by LC-TOF and LC-MS/MS the lipid composition of Xylella fastidiosa subsp. pauca strain De Donno in pure culture; some specific lipids (e.g., ornithine lipids and the oxylipin 7,10-diHOME), characteristic of other pathogenic bacteria, were revealed. Nicotiana tabacum was used for testing the ability of this pathogen in producing such lipids in the host. Different lipid compounds present a clear distribution pattern within the infected plant tissues compared to the uninfected ones.
An Erase Efficiency Boosting Strategy for 3D Charge Trap NAND Flash

Owing to the fast-growing demands of larger and faster NAND flash devices, new manufacturing techniques have accelerated the down-scaling process of NAND flash memory. Among these new techniques, 3D charge trap flash is considered to be one of the most promising candidates for the next-generation NAND flash devices. However, the long erase latency of 3D charge trap flash becomes a critical issue. This issue is exacerbated because the distinct transient voltage shift phenomenon is worsened when the number of program/erase cycle increases. In contrast to existing works that aim to tackle the erase latency issue by reducing the number of block erases, we tackle this issue by utilizing the “multi-block erase” feature. In this work, an erase efficiency boosting strategy is proposed to boost the garbage collection efficiency of 3D charge trap flash via enabling multi-block erase inside flash chips. A series of experiments was conducted to demonstrate the capability of the proposed strategy on improving the erase efficiency and access performance of 3D charge trap flash. The results show that the erase latency of 3D charge trap flash memory is improved by 75.76 percent on average even when the P/E cycle reaches <inline-formula> <tex-math notation="LaTeX">$10^{4}$</tex-math><alternatives><inline-graphic xlink:href="chang-ieq1-2818118.gif"/> </alternatives></inline-formula>.
Comparative genome‐wide analysis of codon usage of different bacterial species infecting Oryza sativa

Oryza sativa is vastly affected by microbial pathogen, causing blight‐related diseases, which in turn deplete the growth and productivity of rice. In this study, we analyzed four bacterial rice pathogen genomes and reported on their codon usage that might have greater implication in mutation‐related research. Differential codon usage indices, such as codon adaptation index (CAI), codon bias index (CBI), effective number of codons (ENc), relative synonymous codon usage (RSCU), correspondence analysis (COA), and parity plots, were applied on coding sequences of Pseudomonas fuscovaginae, Pseudomonas syringae, Xanthomonas oryzae, and Pseudomonas avenae speices. The RSCU results proposed a high‐frequency usage of CUG and CGC that codes for leucine and arginine in all of the species. The CBI and CAI values between the genomes range from 0.17 to 0.3 and from 0.26 to 0.35, respectively, indicating a direct proportionality between these indexes. The mean ENc value of P. avenae coding sequence showed high codon bias compared with other genomes. The axis I variation from COA analysis shows a mean value of 42.28% codon variations in these bacterial species. Correlation studies between axis I and ENc‐GC3, along with CAI and CBI, suggested the presence of nucleotide bias and mutational pressure as major forces for codon bias within these species. Hence, certain genes with high CAI‐CBI have been correlated for better gene expression. Our study highlights the importance of nucleotide biasness, mutation pressure, and natural selection in shaping protein‐coding genes in these four rice‐affecting bacteria. This would further help in investigating the evolution of pathogenic gene families, which may direct research toward synthetic genes that could be suppressed or overrepresented based on their codon usage pattern toward pathogenicity.
Document Informed Neural Autoregressive Topic Models

Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks. Generative topic models infer topic-word distributions, taking no or only little context into account. Here, we extend a neural autoregressive topic model to exploit the full context information around words in a document in a language modeling fashion. This results in an improved performance in terms of generalization, interpretability and applicability. We apply our modeling approach to seven data sets from various domains and demonstrate that our approach consistently outperforms stateof-the-art generative topic models. With the learned representations, we show on an average a gain of 9.6% (0.57 Vs 0.52) in precision at retrieval fraction 0.02 and 7.2% (0.582 Vs 0.543) in F1 for text categorization.
Controlling Underfill Lateral Flow to Improve Component Density in Heterogeneously Integrated Packaging Systems

This paper investigates the control of lateral underfill flow through the use of laser ablation demarcations. It is shown that such demarcations can be successfully created on various substrate surfaces, such as solder masks or polyimides, by adapting industry standard laser ablation processes used in the microelectronic packaging portfolio. In particular, both IR-based laser sources used for substrate identification marking and UV-based laser sources used for laser grooving and dicing of semiconductor devices are studied. Laser demarcations of various patterns, sizes and proximities are investigated to assess their underfill and resin bleed spread control capability. For IR laser demarcations, lower energy ablations on solder mask surfaces produced regions of material build-up that were not capable of impeding the underfill flow. Higher energy ablations produced grooves that controlled underfill spread but only grooves that penetrated the entire thickness of the solder mask could be created, thus exposing the underlying copper circuitry and introducing a reliability concern. A UV laser source produced narrow grooves at different energy levels. Investigation of various patterns subjected to underfill spread testing demonstrated that a first 30µm wide groove of about 5 µm in depth successfully pinned the underfill. Subsequent flip chip assembly experiments validated these results with UV laser grooves located as close as 0.5mm from chip edge. Cross-sections at the groove regions support the pinning phenomenon at the first edge of the groove where the apparent contact angle has increased in accordance with the geometrical Gibbs criterion. Despite effective underfill control, the demarcations were not able to impede progression of resin bleed. However, given its negligible thickness, the bleed material was readily removed by an ArH2 RIE plasma treatment. Finally, preliminary experimentation using UV laser demarcations on polyimide and Si3N4 surfaces showed similar underfill control, suggesting that this approach can be extended to 2.5D and 3D integration applications.
A Security Sandbox Approach of Android Based on Hook Mechanism

As the most widely applied mobile operating system for smartphones, Android is challenged by fast growing security problems, which are caused by malicious applications. Behaviors of malicious applications have become more and more inconspicuous, which largely increase the difficulties of security detection. This paper provides a new security sandbox approach of Android based on hook mechanism, to further enrich Android malware detection technologies. This new sandbox monitors the behaviors of target application by using a process hook-based dynamic tracking method during its running period. Compared to existing techniques, (1) this approach can create a virtual space where apk can be installed, run, and uninstalled, and it is isolated from the outside and (2) a risk assessment approach based on behavior analysis is given so that users can obtain an explicit risk prognosis for an application to improve their safety. Tests on malware and normal application samples verify this new security sandbox.
Angular-Based Word Meta-Embedding Learning

Ensembling word embeddings to improve distributed word representations has shown good success for natural language processing tasks in recent years. These approaches either carry out straightforward mathematical operations over a set of vectors or use unsupervised learning to find a lower-dimensional representation. This work compares meta-embeddings trained for different losses, namely loss functions that account for angular distance between the reconstructed embedding and the target and those that account normalized distances based on the vector length. We argue that meta-embeddings are better to treat the ensemble set equally in unsupervised learning as the respective quality of each embedding is unknown for upstream tasks prior to meta-embedding. We show that normalization methods that account for this such as cosine and KL-divergence objectives outperform meta-embedding trained on standard $\ell_1$ and $\ell_2$ loss on \textit{defacto} word similarity and relatedness datasets and find it outperforms existing meta-learning strategies.
Containment Maneuvering for a Class of Uncertain Nonlinear Systems Based on Concurrent Learning

In this paper, we investigate a containment maneuvering problem for uncertain nonlinear systems in MIMO strict-feedback form. The outputs of followers are driven to converge to a convex hull spanned by multiple parameterized paths and path variables need to satisfy a given dynamic task. A containment maneuvering controller is proposed based on a modular design approach. First, an estimation module is developed based on an RBF network, and adaption laws are proposed based on a concurrent learning method. Then, a controller module is proposed based on a modified dynamic surface control method using a second-order nonlinear tracking differentiator. At last, a path update law is designed by using a distributed maneuvering error feedback. Input-to-state stability theory and cascade theory are utilized to analyze the stability of the closed-loop system. The proposed design is a distributed method and attains adaption without the persistent excitation condition.
Secure Compilation of Side-Channel Countermeasures: The Case of Cryptographic “Constant-Time”

Software-based countermeasures provide effective mitigation against side-channel attacks, often with minimal efficiency and deployment overheads. Their effectiveness is often amenable to rigorous analysis: specifically, several popular countermeasures can be formalized as information flow policies, and correct implementation of the countermeasures can be verified with state-of-the-art analysis and verification techniques. However, in absence of further justification, the guarantees only hold for the language (source, target, or intermediate representation) on which the analysis is performed. We consider the problem of preserving side-channel counter-measures by compilation for cryptographic “constant-time”, a popular countermeasure against cache-based timing attacks. We present a general method, based on the notion of constant-time-simulation, for proving that a compilation pass preserves the constant-time countermeasure. Using the Coq proof assistant, we verify the correctness of our method and of several representative instantiations.
DNA metabarcoding reveals changes in the contents of carnivorous plants along an elevation gradient

Resource variation along abiotic gradients influences subsequent trophic interactions and these effects can be transmitted through entire food webs. Interactions along abiotic gradients can provide clues as to how organisms will face changing environmental conditions, such as future range shifts. However, it is challenging to find replicated systems to study these effects. Phytotelmata, such as those found in carnivorous plants, are isolated aquatic communities and thus form a good model for the study of replicated food webs. Due to the degraded nature of the prey, molecular techniques provide a useful tool to study these communities. We studied the pitcher plant Sarracenia purpurea L. in allochthonous populations along an elevational gradient in the Alps and Jura. We predicted that invertebrate richness in the contents of the pitcher plants would decrease with increasing elevation, reflecting harsher environmental conditions. Using metabarcoding of the COI gene, we sequenced the invertebrate contents of these pitcher plants. We assigned Molecular Operational Taxonomic Units at ordinal level as well as recovering species‐level data. We found small but significant changes in community composition with elevation. These recovered sequences could belong to invertebrate prey, rotifer inquilines, pollinators and other animals possibly living inside the pitchers. However, we found no directional trend or site‐based differences in MOTU richness with elevational gradient. Use of molecular techniques for dietary or contents analysis is a powerful way to examine numerous degraded samples, although factors such as DNA persistence and the relationship with species presence still have to be completely determined.
Mid-wavelength Infrared Supercontinuum Generation Spanning 1.4 Octaves in a Silicon-Germanium Waveguide

We report mid-wavelength infrared supercontinuum generation, from 2.6 to 7. 3μm in a CMOS compatible silicon-germanium waveguide. This 1.4 octave bright supercontinuum (∼7.5mW on chip generated average power) has been achieved in a low loss (<0.4dB/cm) dispersion engineered air-clad Si<inf>0.6</inf>Ge<inf>0.4</inf>/Si waveguide pumped close to the first zero-dispersion wavelength using a tunable OPA laser delivering ∼200fs pulses at 4. 15μm.
In Vivo Virulence Characterization of Pregnancy-Associated Listeria monocytogenes Infections

Listeria monocytogenes is a foodborne pathogen that infects the placenta and can cause pregnancy complications. Listeriosis usually occurs as a sporadic infection, but large outbreaks are also reported. ABSTRACT Listeria monocytogenes is a foodborne pathogen that infects the placenta and can cause pregnancy complications. Listeriosis usually occurs as a sporadic infection, but large outbreaks are also reported. Virulence from clinical isolates is rarely analyzed due to the large number of animals required, but this knowledge could help guide the response to an outbreak. We implemented a DNA barcode system using signature tags that allowed us to efficiently assay variations in virulence across a large number of isolates. We tested 77 signature-tagged clones of clinical L. monocytogenes strains from 72 infected human placentas and 5 immunocompromised patients, all of which were isolated since 2000. These strains were tested for virulence in a modified competition assay in comparison to that of the laboratory strain 10403S. We used two in vivo models of listeriosis: the nonpregnant mouse and the pregnant guinea pig. Strains that were frequently found at a high abundance within infected organs were considered hypervirulent, while strains frequently found at a low abundance were considered hypovirulent. Virulence split relatively evenly among hypovirulent strains, hypervirulent strains, and strains as virulent as 10403S. The laboratory strain was found to have an intermediate virulence phenotype, supporting its suitability for use in pathogenesis studies. Further, we found that splenic virulence and placental virulence are closely linked in both the guinea pig and mouse models. This suggests that outbreak and sporadic pregnancy-associated L. monocytogenes strains are not generally more virulent than lab reference strains. However, some strains did show consistent and reproducible virulence differences, suggesting that their further study may reveal deeper insights into the biological underpinnings of listeriosis.
A Clustering-Based Algorithm for De Novo Motif Discovery in DNA Sequences

Motif discovery is a challenging problem in molecular biology and has been attracting researcher's attention for years. Different kind of data and computational methods have been used to unravel this problem, but there is still room for improvement. In this study, our goal was to develop a method with the ability to identify all the TFBS signals, including known and unknown, inside the input set of sequences. We developed a clustering method specialized as part of our algorithm which outperforms other existing clustering methods such as DNACLUST and CD-HIT-EST in clustering short sequences. A scoring system was needed to determine how much a cluster is close to being a real motif. Multiple features are calculated based on the contents of each cluster to determine the score of the cluster. These features contain a set of divergence measures, positional, and occurrence information. These scores are combined in a way that a trade-off between them determines the clusters situation. There is an option to compare the final results with the motif databases such as Jolma2013, and UniProbe using Tomtom motif comparison tool. Algorithm Evaluation has been performed on three datasets from ABS database.