Miracle-dz/UniFAR-FTUs huggingface dataset:
Chitosan in Non-Viral Gene Delivery: Role of Structure, Characterization Methods, and Insights in Cancer and Rare Diseases Therapies

Non-viral gene delivery vectors have lagged far behind viral ones in the current pipeline of clinical trials of gene therapy nanomedicines. Even when non-viral nanovectors pose less safety risks than do viruses, their efficacy is much lower. Since the early studies to deliver pDNA, chitosan has been regarded as a highly attractive biopolymer to deliver nucleic acids intracellularly and induce a transgenic response resulting in either upregulation of protein expression (for pDNA, mRNA) or its downregulation (for siRNA or microRNA). This is explained as the consequence of a multi-step process involving condensation of nucleic acids, protection against degradation, stabilization in physiological conditions, cellular internalization, release from the endolysosome (“proton sponge” effect), unpacking and enabling the trafficking of pDNA to the nucleus or the siRNA to the RNA interference silencing complex (RISC). Given the multiple steps and complexity involved in the gene transfection process, there is a dearth of understanding of the role of chitosan’s structural features (Mw and degree of acetylation, DA%) on each step that dictates the net transfection efficiency and its kinetics. The use of fully characterized chitosan samples along with the utilization of complementary biophysical and biological techniques is key to bridging this gap of knowledge and identifying the optimal chitosans for delivering a specific gene. Other aspects such as cell type and administration route are also at play. At the same time, the role of chitosan structural features on the morphology, size and surface composition of synthetic virus-like particles has barely been addressed. The ongoing revolution brought about by the recent discovery of CRISPR-Cas9 technology will undoubtedly be a game changer in this field in the short term. In the field of rare diseases, gene therapy is perhaps where the greatest potential lies and we anticipate that chitosans will be key players in the translation of research to the clinic.
Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be fully re-trained to handle a different disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel cross-entropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.
Efficient Crowdsourcing via Proxy Voting

Crowdsourcing platforms offer a way to label data by aggregating answers of multiple unqualified workers. We introduce a \textit{simple} and \textit{budget efficient} crowdsourcing method named Proxy Crowdsourcing (PCS). PCS collects answers from two sets of workers: \textit{leaders} (a.k.a proxies) and \textit{followers}. Each leader completely answers the survey while each follower answers only a small subset of it. We then weigh every leader according to the number of followers to which his answer are closest, and aggregate the answers of the leaders using any standard aggregation method (e.g., Plurality for categorical labels or Mean for continuous labels). We compare empirically the performance of PCS to unweighted aggregation, keeping the total number of questions (the budget) fixed. We show that PCS improves the accuracy of aggregated answers across several datasets, both with categorical and continuous labels. Overall, our suggested method improves accuracy while being simple and easy to implement.
A Step Forward in the Conceptualization and Measurement of Parental Burnout: The Parental Burnout Assessment (PBA)

So far, the conceptualization and measurement of parental burnout have been deduced from those of job burnout. As a result, it is unclear whether current measures of parental burnout constitute the best representation of the parental burnout construct/syndrome: the possibility cannot be excluded that some dimensions ought to be added, which would change the structure and definition of parental burnout. In this study, the conceptualization and measurement of parental burnout were approached using an inductive method, in which the parental burnout phenomenon was (re)constructed based solely on the testimonies of burned-out parents. Items extracted from their testimonies were presented to a sample of French-speaking and English-speaking parents (N = 901) and submitted to factor analyses. An identifiable parental burnout syndrome including four dimensions was found (exhaustion in one's parental role, contrast with previous parental self, feelings of being fed up with one's parental role and emotional distancing from one's children). The resulting instrument, the Parental Burnout Assessment (PBA) presents good validity. Factorial invariance across gender and languages was also found. Finally, the results of this study replicate previous findings that psychological traits of the parents, parenting factors, and family functioning account for more variance in parental burnout than sociodemographic factors.
Facile and Eco-Friendly Fabrication of Colored and Bioactive Silk Materials Using Silver Nanoparticles Synthesized by Two Flavonoids

Recently, there has been an increasing tendency towards the functionalization of silk using silver nanoparticles (AgNPs) to inhibit bacterial multiplication and disease spread. Considering environmental factors and sustainable development, the preparation of AgNPs using natural extracts is becoming a research hotspot. This study aims at fabricating colored and bioactive silk fabric using AgNPs synthesized by two representative flavonoids (quercetin and rutin). The effects of pH, temperature, and flavonoid concentration on the yield and particle size of AgNPs were studied. The color features and functionalities of the AgNPs-treated silk were also evaluated. The results showed that the AgNPs synthesized by quercetin were generated faster but displayed poorer size uniformity than those prepared by rutin. The as-prepared AgNPs showed good stability. The AgNPs prepared by rutin displayed a more uniform distribution on silk than those synthesized by quercetin. The antibacterial activity of AgNPs-treated silk remained over 90% against E. coli and S. aureus even after 30 washing cycles. The antioxidant activity of the treated silk gradually decreased during washing. The present research proposes a facile and eco-friendly method for the preparation of AgNPs-coated silk material using flavonoids, which can serve as hygiene-related and medical textile materials.
A New Wireless Communication Paradigm through Software-Controlled Metasurfaces

Electromagnetic waves undergo multiple uncontrollable alterations as they propagate within a wireless environment. Free space path loss, signal absorption, as well as reflections, refractions, and diffractions caused by physical objects within the environment highly affect the performance of wireless communications. Currently, such effects are intractable to account for and are treated as probabilistic factors. This article proposes a radically different approach, enabling deterministic, programmable control over the behavior of wireless environments. The key enabler is the so-called HyperSurface tile, a novel class of planar meta-materials that can interact with impinging electromagnetic waves in a controlled manner. The HyperSurface tiles can effectively re-engineer electromagnetic waves, including steering toward any desired direction, full absorption, polarization manipulation, and more. Multiple tiles are employed to coat objects such as walls, furniture, and overall, any objects in indoor and outdoor environments. An external software service calculates and deploys the optimal interaction types per tile to best fit the needs of communicating devices. Evaluation via simulations highlights the potential of the new concept.
Adapting Neural Text Classification for Improved Software Categorization

Software Categorization is the task of organizing software into groups that broadly describe the behavior of the software, such as "editors" or "science. " Categorization plays an important role in several maintenance tasks, such as repository navigation and feature elicitation. Current approaches attempt to cast the problem as text classification, to make use of the rich body of literature from the NLP domain. However, as we will this paper, algorithms are generally not applicable off-the-shelf to source code; we found that they work well when high-level project descriptions are available, but suffer very large performance penalties when classifying sourcecode and comments only. We propose a set of adaptations to a state-of-the-art neural classification algorithm and perform two evaluations: one with reference data from Debian end-user programs, and one with a set of C/C++ libraries that we hired professional programmers to annotate. We show that our proposed approach achieves performance exceeding that of previous software classification techniques as well as a state-of-the-art neural text classification technique.
Blended Feedback II: Video screen capture assessment feedback for individual students, as a matter of course, on an undergraduate computer programming unit

The use of video feedback is popular, even usual, in fields involving social behaviour and interaction or physical performance. In other academic subject areas, the use of video as feedback is, as yet, uncommon. The work of others in this field covers group work, generic feedback, small numbers of students, samples and trials. We believe this may be one of the first studies on returning individual personalised feedback to a sizeable number (over 300) first year undergraduate students taking Computer Programming, or any other academic subject, for every assessment submitted on the unit. Student engagement with feedback is often lacking and in that case, a valuable learning opportunity is missed. Previous work using audio as feedback showed 80% of students would prefer audio to written feedback. However, the separation of submitted programming code from audio comments limits ease of reference to the work. The next natural step was to use video screen capture to augment the student experience by improving easy reference to work by simultaneously providing contextually relevant narrative and visually referring to elements of the work.
ACES: Automatic Compartments for Embedded Systems

Securing the rapidly expanding Internet of Things (IoT) is critical. Many of these “things” are vulnerable baremetal embedded systems where the application executes directly on hardware without an operating system. Unfortunately, the integrity of current systems may be compromised by a single vulnerability, as recently shown by Google’s P0 team against Broadcom’s WiFi SoC. We present ACES (Automatic Compartments for Embedded Systems)1, an LLVM-based compiler that automatically infers and enforces inter-component isolation on bare-metal systems, thus applying the principle of least privileges. ACES takes a developer-specified compartmentalization policy and then automatically creates an instrumented binary that isolates compartments at runtime, while handling the hardware limitations of baremetal embedded devices. We demonstrate ACES’ ability to implement arbitrary compartmentalization policies by implementing three policies and comparing the compartment isolation, runtime overhead, and memory overhead. Our results show that ACES’ compartments can have low runtime overheads (13% on our largest test application), while using 59% less Flash, and 84% less RAM than the Mbed μVisor—the current state-of-theart compartmentalization technique for bare-metal systems. ACES ‘ compartments protect the integrity of privileged data, provide control-flow integrity between compartments, and reduce exposure to ROP attacks by 94.3% compared to μVisor.
Light Stress Responses by the Eelgrass, Zostera marina (L)

Zostera marina is the dominant seagrass species in the Northern Hemisphere where it grows in sheltered bays and estuaries. As a consequence of its distribution its conservation is commonly threatened by poor coastal water quality. The high minimum light requirements of seagrasses results in water quality degradation (high turbidity and eutrophication) being a significant risk. Bioindicators of light stress can be used to interpret seagrass responses to light limitation and therefore act as sentinels for conservation management. However, there exists limited experimental inter-comparison of the effectiveness of multiple individual bioindicator responses. Meta-analysis suggests that rhizome sugars, shoot C:N, shoot growth and number of leaves per shoot provide the most consistent response variables to increasing light limitation in seagrass, but this premise remains largely untested at the plant level as a direct comparison of multiple bioindicators. The present study aimed to test the morphological, physiological and photo-physiological bioindicator responses of Z. marina to light stress applied within controlled laboratory conditions. These bioindicators were used to assign minimum light thresholds. Growth rate and photophysiological parameters (alpha, Ek and ETRmax) were rapidly (1st week) and drastically affected by low light shade treatments (20.12 µmol photons m-2s-1 and lower). After three weeks at low light, significant reductions in maximum leaf length and leaf width were observed. Principal Component Analysis identified leaf length, shoot growth, shoot surface area, ETRmax, Ek and alpha as having the strongest responses to reduced light. Shoot growth, ETRmax, Ek and alpha were found to provide the best early warning of light limitation after 5-8 days. These results provide evidence for bioindicators of light stress in Z. marina and highlights the importance of understanding these responses for the successful management and conservation of this species.
Synthesis of an Efficient S/N-Based Flame Retardant and Its Application in Polycarbonate

Considering the poor compatibility and water-resistance of sulfonate flame retardants for polycarbonate (PC), an efficient S/N-based flame retardant named 1,3,5,7-tetrakis(phenyl-4-sulfonyl-melamine)adamantane (ASN) has been developed. Fire properties studies of PC/ASN blends indicate that the addition of 0.10 wt % ASN imparts a V-0 rating and a limited oxygen index (LOI) value of 30.1% to PC specimens, and ASN can suppress the heat and toxic gas release of PC composites. Additionally, PC/ASN blends are believed to be exceptional materials for outdoor PC applications due to their superior water-resistance properties. Moreover, mechanical properties were further systematically investigated, and the correlative results indicate that the tensile strength and rigidity of specimens are improved with the addition of ASN.
A Critique of Recent Epidemiologic Studies of Cancer Mortality Among Nuclear Workers

Current justification by linear no-threshold (LNT) cancer risk model advocates for its use in low-dose radiation risk assessment is now mainly based on results from flawed and unreliable epidemiologic studies that manufacture small risk increases (ie, phantom risks). Four such studies of nuclear workers, essentially carried out by the same group of epidemiologists, are critiqued in this article. Three of the studies that forcibly applied the LNT model (inappropriate null hypothesis) to cancer mortality data and implicated increased mortality risk from any radiation exposure, no matter how small the dose, are demonstrated to manufacture risk increases for doses up to 100 mSv (or 100 mGy). In a study where risk reduction (hormetic effect/adaptive response) was implicated for nuclear workers, it was assumed by the researchers to relate to a “strong healthy worker effect” with no consideration of the possibility that low radiation doses may help prevent cancer mortality (which is consistent with findings from basic radiobiological research). It was found with basic research that while large radiation doses suppress our multiple natural defenses (barriers) against cancer, these barriers are enhanced by low radiation doses, thereby decreasing cancer risk, essentially rendering the LNT model to be inconsistent with the data.
Neural Hidden Markov Model for Machine Translation

Attention-based neural machine translation (NMT) models selectively focus on specific source positions to produce a translation, which brings significant improvements over pure encoder-decoder sequence-to-sequence models. This work investigates NMT while replacing the attention component. We study a neural hidden Markov model (HMM) consisting of neural network-based alignment and lexicon models, which are trained jointly using the forward-backward algorithm. We show that the attention component can be effectively replaced by the neural network alignment model and the neural HMM approach is able to provide comparable performance with the state-of-the-art attention-based models on the WMT 2017 German↔English and Chinese→English translation tasks.
Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors

Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR’s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method.
Fast Dynamic Programming on Graph Decompositions

In this paper, we consider tree decompositions, branch decompositions, and clique decompositions. We improve the running time of dynamic programming algorithms on these graph decompositions for a large number of problems as a function of the treewidth, branchwidth, or cliquewidth, respectively. On tree decompositions of width $k$, we improve the running time for Dominating Set to $O(3^k)$. We generalise this result to $[\rho,\sigma]$-domination problems with finite or cofinite $\rho$ and $\sigma$. For these problems, we give $O(s^k)$-time algorithms, where $s$ is the number of `states' a vertex can have in a standard dynamic programming algorithm for such a problems. Furthermore, we give an $O(2^k)$-time algorithm for counting the number of perfect matchings in a graph, and generalise this to $O(2^k)$-time algorithms for many clique covering, packing, and partitioning problems. On branch decompositions of width $k$, we give an $O(3^{\frac{\omega}{2}k})$-time algorithm for Dominating Set, an $O(2^{\frac{\omega}{2}k})$-time algorithm for counting the number of perfect matchings, and $O(s^{\frac{\omega}{2}k})$-time algorithms for $[\rho,\sigma]$-domination problems involving $s$ states with finite or cofinite $\rho$ and $\sigma$. Finally, on clique decompositions of width $k$, we give $O(4^k)$-time algorithms for Dominating Set, Independent Dominating Set, and Total Dominating Set. The main techniques used in this paper are a generalisation of fast subset convolution, as introduced by Bj\"orklund et al., now applied in the setting of graph decompositions and augmented such that multiple states and multiple ranks can be used. Recently, Lokshtanov et al. have shown that some of the algorithms obtained in this paper have running times in which the base in the exponents is optimal, unless the Strong Exponential-Time Hypothesis fails.
Financial Distress , Dealers ' Behavior and Asset Pricing in the Foreign Exchange Market . by Val re Fourel Submitted

Exploiting a high frequency dealer-specific quote database in the FX market, I show that shocks to the CDS of a financial intermediary, proxy for its financial wealth, makes her quote larger bid-ask spreads when uncertainty about the underlying traded asset is high or when market competition is low. I first establish that markets are dominated by a handful of dealers who are responsible for more than 90% of the quotes in the different FX spot markets. I then document that, when exchange rate volatility is high, a 1% increase in intermediary's default probability does translate into a 4 bps increase in the bid-ask spread that she quotes. When competition is low, a similar deterioration in financial wealth leads to a 6.4 bps increase in bid-ask spread size. I finally show that in the case of emerging country currencies, the average CDS spread of the financial intermediaries quoting in the FX market is a statistically significant predictor for the volatility of the idiosyncratic component of the currency risk premium. More surprisingly, the dispersion in terms of financial wealth across financial intermediaries, measured as the variance of the financial intermediaries CDS spreads, is also an important determinant of this volatility for a large set of emerging country currencies. Thesis Supervisor: Adrien Verdelhan Title: Class of 1956 Career Development Professor
Wireless Video Caching and Dynamic Streaming Under Differentiated Quality Requirements

This paper considers one-hop device-to-device-assisted wireless caching networks that cache video files of varying quality levels, with the assumption that the base station can control the video quality but cache-enabled devices cannot. Two problems arise in such a caching network: file placement problem and node association problem. This paper suggests a method to cache videos of different qualities, and thus of varying file sizes, by maximizing the sum of video quality measures that users can enjoy. There exists an interesting tradeoff between video quality and video diversity, i.e., the ability to provision diverse video files. By caching high-quality files, the cache-enabled devices can provide high-quality video, but cannot cache a variety of files. Conversely, when the device caches various files, it cannot provide a good quality for file-requesting users. In addition, when multiple devices cache the same file but their qualities are different, advanced node association is required for file delivery. This paper proposes a node association algorithm that maximizes time-averaged video quality for multiple users under a playback delay constraint. In this algorithm, we also consider request collision, the situation where several users request files from the same device at the same time, and we propose two ways to cope with the collision: scheduling of one user and non-orthogonal multiple access. Simulation results verify that the proposed caching method and the node association algorithm work reliably.