Miracle-dz/UniFAR-FTUs huggingface dataset:
T-Cell Receptor-Like Antibodies: Targeting the Intracellular Proteome Therapeutic Potential and Clinical Applications

Major histocompatibility complex (MHC) class I molecules are key in the immune response against malignant cells by shaping the T-cell repertoire and presenting peptides from endogenous antigens to CD8+ cytotoxic T cells. Because of their unique specificity, MHC-peptide complexes are a desirable target for novel immunotherapeutic approaches. These complexes can be targeted by recombinant T-cell receptors (TCRs). However, most TCRs produced thus far have affinities which are too low for target detection under normal assay conditions, and limited stability (due to their generation in a single-chain version). Developing high-affinity soluble antibody molecules endowed with a TCR-like specificity toward tumor epitopes, termed TCR-like antibodies, addresses the low affinity of TCRs. These TCR-like antibodies are being developed as a new immunotherapeutic class for targeting tumor cells and mediating their specific killing. In addition, these antibodies are valuable research reagents enabling the study of human class I peptide-MHC ligand-presentation and TCR–peptide–MHC interactions.
A highly-integrated biomedical multiprocessor system for portable brain-heart monitoring

In this paper, a highly-integrated multiprocessor chip design enabling the real-time processing of biomedical signals in portable brain-heart monitoring systems is presented. The architecture comprises a novel diffuse optical tomography (DOT) processor for taking brain imaging, an independent component analysis (ICA) processor for removing artifacts of brain electroencephalogram (EEG) signals, and a heart rate variability (HRV) analysis processor for monitoring heart electrocardiogram (ECG) signals. The multiprocessor chip implemented in 65nm CMOS technology comprises 368k gates and occupies a core area of 462k µm2. Simulated power consumption using a full operation test case reports 3.6mW under the condition of 1.0V core supply voltage and 24MHz clock operating frequency.
Automation of Millimeter Wave Network Planning for Outdoor Coverage in Dense Urban Areas Using Wall-Mounted Base Stations

The millimeter wave (mmWave) spectrum is an important candidate band for next-generation cellular networks. Using higher frequencies, mmWaves are much more sensitive to blockage, and the presence of line-of-sight links is more desired, making the network planning more dependent on the layout of the cities. In this letter, we use computational geometry and optimization tools to fully automate the process of planning an outdoor mmWave network in dense cities, using wall-mounted below-rooftop base stations (BSs). Based on the locations found, one can analyze some properties of mmWave networks. We provide information about the BS density and coverage in two different large urban areas.
A Survey on Classification of Videos using Data Mining Techniques

Videos are in huge demand today. The internet is flooded with videos of all types like movie trailers, songs, security cameras etc. we can find so many genres but the only difficulty we face is the proper search of these videos. Sometimes we are irritated and get sick of the irrelevant search result. To sort out this difficulty we aim to classify videos on the basis of different attributes. Here in this paper we survey the video classification literature. Much work has been done in this field and much is awaited. We describe the general features chosen and summarize the research in this area. We conclude with ideas for further research.
Satisfiability Allows No Nontrivial Sparsification unless the Polynomial-Time Hierarchy Collapses

Consider the following two-player communication process to decide a language <i>L</i>: The first player holds the entire input <i>x</i> but is polynomially bounded; the second player is computationally unbounded but does not know any part of <i>x</i>; their goal is to decide cooperatively whether <i>x</i> belongs to <i>L</i> at small cost, where the cost measure is the number of bits of communication from the first player to the second player. For any integer <i>d</i> ≥ 3 and positive real <i>ε</i>, we show that, if satisfiability for <i>n</i>-variable <i>d</i>-CNF formulas has a protocol of cost <i>O</i>(<i>nd</i> − <i>ε</i>), then coNP is in NP/poly, which implies that the polynomial-time hierarchy collapses to its third level. The result even holds when the first player is conondeterministic, and is tight as there exists a trivial protocol for <i>ε</i> = 0. Under the hypothesis that coNP is not in NP/poly, our result implies tight lower bounds for parameters of interest in several areas, namely sparsification, kernelization in parameterized complexity, lossy compression, and probabilistically checkable proofs. By reduction, similar results hold for other NP-complete problems. For the vertex cover problem on <i>n</i>-vertex <i>d</i>-uniform hypergraphs, this statement holds for any integer <i>d</i> ≥ 2. The case <i>d</i> = 2 implies that no NP-hard vertex deletion problem based on a graph property that is inherited by subgraphs can have kernels consisting of <i>O</i>(<i>k</i>2 − <i>ε</i>) edges unless coNP is in NP/poly, where <i>k</i> denotes the size of the deletion set. Kernels consisting of <i>O</i>(<i>k</i>2) edges are known for several problems in the class, including vertex cover, feedback vertex set, and bounded-degree deletion.
Parametric amplification of microwave magnetoelastic and magnetostatic waves in yttrium iron garnet

It has been known for more than a decade that the behavior of ferrimagnetic devices at high microwave power levels is dominated by nonlinear processes of spin wave excitation. The first nonlinear effects were observed in ferromagnetic resonance of nickel ferrite, and resulted in a premature decline of the main resonance and the observation of a secondary absorption at a lower magnetic field. Both these effects were shown to be due to the unstable growth of spin waves. The linear behavior of spin waves, elastic waves and especially the coupled mode, magnetoelastic waves, has also received a great deal of attention recently, in part due to the importance of devices utilizing these modes of propagation as delay lines in signal processing systems. In this thesis we are concerned with the combination of the linear and nonlinear processes, resulting in the parametric generation and amplification of microwave magnetoelastic and magnetostatic waves in yttrium iron garnet. A series of experiments is described showing the linear propagation of magnetoelastic and magnetostatic waves in the frequency range of 1 — 10 GHz in single crystal YIG disks and rods. Their behavior as a function of field is completely determined, using the expression for the free energy of a ferrimagnetic crystal to obtain the equations of motion, and combining these with the shape of the internal field of the various samples used, The parametric processes involve a puiqp field with twice the signal frequency and at right angles to the dc field. The amplification of magnetoelastic waves in a rod is largest when the dc field is directed ~ 15 off the rod axis, and in this case the pump must be applied approximately when the signal is in the magnetoelastic crossover region. The parametric amplification of magnetostatic waves results in the pulse recall with variable time delay; a microwave signal, injected into the YIG rod, will
Global Economic Burden of Norovirus Gastroenteritis

Background Despite accounting for approximately one fifth of all acute gastroenteritis illnesses, norovirus has received comparatively less attention than other infectious pathogens. With several candidate vaccines under development, characterizing the global economic burden of norovirus could help funders, policy makers, public health officials, and product developers determine how much attention and resources to allocate to advancing these technologies to prevent and control norovirus. Methods We developed a computational simulation model to estimate the economic burden of norovirus in every country/area (233 total) stratified by WHO region and globally, from the health system and societal perspectives. We considered direct costs of illness (e.g., clinic visits and hospitalization) and productivity losses. Results Globally, norovirus resulted in a total of $4.2 billion (95% UI: $3.2–5.7 billion) in direct health system costs and $60.3 billion (95% UI: $44.4–83.4 billion) in societal costs per year. Disease amongst children <5 years cost society $39.8 billion, compared to $20.4 billion for all other age groups combined. Costs per norovirus illness varied by both region and age and was highest among adults ≥55 years. Productivity losses represented 84–99% of total costs varying by region. While low and middle income countries and high income countries had similar disease incidence (10,148 vs. 9,935 illness per 100,000 persons), high income countries generated 62% of global health system costs. In sensitivity analysis, the probability of hospitalization had the largest impact on health system cost estimates ($2.8 billion globally, assuming no hospitalization costs), while the probability of missing productive days had the largest impact on societal cost estimates ($35.9 billion globally, with a 25% probability of missing productive days). Conclusions The total economic burden is greatest in young children but the highest cost per illness is among older age groups in some regions. These large costs overwhelmingly are from productivity losses resulting from acute illness. Low, middle, and high income countries all have a considerable economic burden, suggesting that norovirus gastroenteritis is a truly global economic problem. Our findings can help identify which age group(s) and/or geographic regions may benefit the most from interventions.
Monte Carlo surface scattering simulation in MOSFET structures

The Monte Carlo method has been applied to MOSFET devices with the gate lengths less than 1 µm. The electric field in the channel was obtained by an analytical approach. Since the classical situation is approached in the submicrometer gate device, the partial diffusive model is employed for surface scattering process. Transient phenomena such as velocity overshoot have been predicted with drain biases causing a large field gradient in the channel. Comparison of the results of the Monte Carlo simulation with those obtained by an analytical approach based on static mobility shows that the carrier transit time in the channel is shorter (as much as two times) than that predicted by the analytical approach for a 0.3 µm gate device.
Retinoid signaling distinguishes a subpopulation of olfactory receptor neurons in the developing and adult mouse

We asked whether retinoic acid (RA) influences olfactory receptor neurons (ORNs) in the developing and mature mouse olfactory epithelium (oe). The distribution of retinoid receptors and binding proteins in the oe changes between embryonic days 11.5 and 13.5, the period when ORNs first differentiate and send axons into the nascent olfactory nerve. Coincident with this change, RA, which is produced in the frontonasal mesenchyme at these ages, begins to activate gene expression in a bilaterally symmetric subset of ORNs in the dorsolateral oe, as judged by the expression of an RA‐responsive transgene. Axons from these RA‐activated ORNs are segregated in the olfactory nerve as it extends through the frontonasal mesenchyme toward the forebrain. In vitro, RA potentiates ORN neurite growth on laminin, which, in the embryo, is found in a stripe of frontonasal mesenchyme directly associated with the olfactory nerve. RA does not modify growth on fibronectin, type IV collagen, or L1, which olfactory axons encounter in different regions of the territory between the olfactory epithelium and the brain. The pattern of RA‐mediated transcriptional activation and axon segregation persists in early postnatal mice, and RA signaling can be recognized in a subset of adult ORNs in the dorsolateral oe. Thus, RA‐mediated gene expression distinguishes a subpopulation of ORNs in a distinct region of the oe during the early development of the olfactory pathway, and may influence differentiation and axonal projections of ORNs in this region throughout life. J. Comp. Neurol. 394:445–461, 1998. © 1998 Wiley‐Liss, Inc.
Queue Dynamics Analysis of TCP Veno with RED

This paper aims to study the queue dynamics of TCP Veno with RED in the wired-wireless heterogeneous networks. The authors first develop a fluid-flow model of TCP Veno with RED over heterogeneous network, and then use the classical linear feedback control theory to analyze it. Analysis results reveal the relationship between the RED queue oscillation and the network parameters. The authors use simulation tool to validate our analysis, and show how to stabilize the router queue and improve the co-existence of TCP Veno with TFRC.
Surface effects on spinodal decomposition in binary mixtures and the interplay with wetting phenomena.

The phase separation of binary mixtures in a semi-infinite geometry is investigated both by a phenomenological theory and by numerical calculations using a discrete equivalent of the descriptive equations. In the framework of ``model B'' (which describes solid binary mixtures), attention is paid to a proper treatment of the boundary conditions at the free surfaces. We confine ourselves to short-range surface forces and consider parameter values that correspond to both nonwet and wet surfaces in thermal equilibrium. During the initial stages of spinodal decomposition, after a quench from considering an initial condition that corresponds to a completely random concentration distribution, one finds a rather rapid growth of a thin surface enrichment layer of the component that is energetically preferred by the surface. This layer stabilizes a growing concentration wave in the direction normal to the surface, whose oscillations are damped to zero as one goes into the bulk. Studying concentration correlations in the direction parallel to the surface at a distance z from it, one can define a length scale ${\mathit{l}}_{\mathrm{\ensuremath{\parallel}}}$(z,t) describing the coarsening of the growing domains. We find that ${\mathit{l}}_{\mathrm{\ensuremath{\parallel}}}$(z,t)\ensuremath{\approxeq}A(z)+B(z)${\mathit{t}}^{\mathit{a}}$ for large t, with an amplitude B(z) that increases as z\ensuremath{\rightarrow}0 and an exponent a which is close to that for Lifshitz-Slyozov growth, viz., a\ensuremath{\approxeq}1/3. This increase is due to an orientation of the growing elongated domains parallel to the surface near z=0. There is surprisingly little influence of the wetting transition on these phenomena\char21{}even for a wet surface the growth of the wetting layer is only logarithmic in time for short-range surface forces, and hence does not significantly affect phenomena on the faster time scales of spinodal decomposition. Experimental findings on the interplay of spinodal decomposition and wetting are critically discussed.
Learning to Plan Using Harmonic Analysis of Diffusion Models

This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan have traditionally been studied: the indirect model-based approach infers the state transition matrix and reward function from samples, and then solves the Bellman equation to find the optimal (action) value function; the direct model-free approach, most notably Q-learning, estimates the action value function directly. This paper describes a new harmonic analysis framework for planning based on estimating a diffusion model that captures information flow on a graph (discrete state space) or a manifold (continuous state space) using the Laplace heat equation. Diffusion models are significantly easier to learn than transition models, and yet provide similar speedups in performance over model-free methods. Two methods for constructing novel plan representations from diffusion models are described: Fourier methods diagonalize a symmetric diffusion operator called the Laplacian; Wavelet methods dilate unit basis functions progressively using powers of the diffusion operator. A new variant of policy iteration – called representation policy iteration – is described consisting of an outer loop that estimates new basis functions by diagonalization or dilation, and an inner loop that learns the best policy representable within the linear span of the current basis functions. Results from continuous and discrete MDPs are provided to illustrate the new approach.
Shunting: a hardware/software architecture for flexible, high-performance network intrusion prevention

Stateful, in-depth, inline traffic analysis for intrusion detection and prevention is growing increasingly more difficult as the data rates of modern networks rise. Yet it remains the case that in many environments, much of the traffic comprising a high-volume stream can, after some initial analysis, be qualified as of "likely uninteresting. " We present a combined hardware/software architecture, Shunting, that provides a lightweight mechanism for an intrusion prevention system (IPS) to take advantage of the "heavy-tailed" nature of network traffic to offload work from software to hardware. The primary innovation of Shunting is the introduction of a simple in-line hardware element that caches rules for IP addresses and connection 5-tuples, as well as fixed rules for IP/TCP flags. The caches, using a highest-priority match, yield a per-packet decision: forward the packet; drop it; or divert it through the IPS. By manipulating cache entries, the IPS can specify what traffic it no longer wishes to examine, including directly blocking malicious sources or cutting through portions of a single flow once the it has had an opportunity to "vet" them, all on a fine-grained basis. We have implemented a prototype Shunt hardware design using the NetFPGA 2 platform, capable of Gigabit Ethernet operation. In addition, we have adapted the Bro intrusion detection system to utilize the Shunt framework to offload less-interesting traffic. We evaluate the effectiveness of the resulting system using traces from three sites, finding that the IDS can use this mechanism to offload 55%-90% of the traffic, as well as gaining intrusion prevention functionality.
Reduce, reuse & recycle: Efficiently solving multi-label MRFs

In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multi-label energy functions arising from discrete MRFs or CRFs. These methods are motivated by the observations that the performance of minimization algorithms depends on: (a) the initialization used for the primal and dual variables; and (b) the number of primal variables involved in the energy function. Our first method (dynamic alpha-expansion) works by dasiarecyclingpsila results from previous problem instances. The second method simplifies the energy function by dasiareducingpsila the number of unknown variables, and can also be used to generate a good initialization for the dynamic alpha-expansion algorithm by dasiareusingpsila dual variables. We test the performance of our methods on energy functions encountered in the problems of stereo matching, and colour and object based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of alpha-expansion, as well as other popular algorithms such as sequential tree-reweighted message passing, and max-product belief propagation. In most cases we achieve a 10-15 times speed-up in the computation time. Our modified alpha-expansion algorithm provides similar performance to Fast-PD. However, it is much simpler and can be made orders of magnitude faster by using the initialization schemes proposed in the paper.
Generation of information and complexity: Different forms of learning and innovation: A simple mechanism of learning

W hat is the difference in evolutionary optimization of molecules, viruses, bacteria, plants, animals, and man? There are thousands of features in which the elements of the optimization process differ, in particular, the degree of complexity increases tremendously from molecules to man. The Darwinian principle of multiplication with inheritance, variation, and selection, however, applies to all of them. This is true despite the fact that the mechanistic details can be very different: RNA molecules and viruses are replicated by a complementary mechanism—the plus strand is the template for the synthesis of the minus strand and vice versa— bacteria and all other organisms duplicate their genetic material by means of direct double strand replication, and finally the higher species undergo a complex process of development from the fertilized egg to the adult organism. Inheritance of the genetic information for a repertoire of properties is an indispensable prerequisite and it exists in all the cases mentioned above. Needless to say, inheritance of properties in molecules is ridiculously simple compared to man. Variation is again different for the different systems and may be the result of simple mutation as in asexual species or a combination of recombination and mutation for sexual reproduction [1]. From the point of mechanistic details molecules and man have indeed very little in common. Selection, eventually, is again in action on the level of populations correcting the overshooting of growth by adjustment to the carrying capacity of the ecosystem. The power of the Darwinian principle is its insensitivity to changes in mechanistic details. Otherwise, Darwin would have failed miserably in case the mechanism of inheritance mattered, because he believed most of his life in “panspermia” and therefore got this part of his concept completely wrong. In summary, Darwin’s insight revealed a principle that operates on the population level and at the same There are thousands of features in which the elements of the optimization process differ, in particular, the degree of complexity increases tremendously from molecules to man. PETER SCHUSTER
State estimation for genetic regulatory networks with time-varying delay using stochastic sampled-data

This paper considers genetic regulatory networks with time-varying delay. By construction of a suitable Lyapunov-Krasovskii functional and utilization of stochastic sampled-data, a delay-dependent state estimation for the concerned systems is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. One numerical example is given to illustrate the effectiveness of the proposed method.