Miracle-dz/UniFAR-FTUs huggingface dataset:
Design, synthesis and antibacterial activity of a novel hybrid antimicrobial peptide LFM23

Antimicrobial peptides produced by many tissues and cell types of invertebrates, insects and humans as part of their innate immune system, have received increasing attention as potential candidates due to their administration as pharmaceutical agents. In the present study, a novel hybrid antimicrobial peptide LFM23 consisting of 23 amino acid residues was designed based on the primary sequences of bovine lactoferricin (LfcinB) and melittin. The peptide was synthesized by chemical method of solidphase synthesis with a purity of more than 98% after reverse phase high performance liquid chromatography. Antimicrobial activity assay showed that LFM23 had strong antibacterial abilities, and the minimum inhibitory concentrations against Escherichia coli AT CC25922, Salmonella typhimurium ATCC12291, Pseudomonas aeruginosa ATCC27853, Staphylococcus aureus ATCC25923, Pichia pastoris GS115, were 32, 32, 64, 32 and 256 μg/ml, respectively. The hemolytic assays indicated that LFM23 had no hemolytic action in vitro at antimicrobial concentration. The results demonstrate that the peptide LFM23 has a good application prospect as clinically useful antimicrobial agents.
A General Framework for the Analysis of Adaptive Experiments

Adaptive experiments have design features that adapt to the accumulating data and are therefore informative about the parameter of interest. As a consequence, the overall information in an adaptive experiment is a combination of information from two sources, the realized design and the observed outcomes. This paper presents a general framework for the analysis of adaptive experiments, based on the decomposition of overall information into design information and outcome information. Likelihood inference is discussed, beginning with assumptions that guarantee insensitivity of the likelihood to the adaptive design. We then focus on the relative merits of unconditional and conditional inference. Although conditional inference is inefficient due to the non-ancillary design, unconditional inference may be biased conditional on the realized design. Identifying such conditional bias in a given experiment is a motivation of the proposed framework. We show that conditional bias stems from correlation between the total information and the design information, and that this bias is most pronounced in samples where the design information is inconsistent with the outcome information. Thus, by viewing the unconditional likelihood as the aggregation of information from a design likelihood and a conditional likelihood, we can use meta-analysis principles to assess heterogeneity between the two information sources. When such heterogeneity is detected, conditional inference may be more appropriate. Interpretation from a Bayesian perspective is also discussed. Running title: Analysis of adaptive experiments. 2010 subject classifications: Primary 62B15; Secondary 62L12.
Aeolus: A Building Block for Proactive Transport in Datacenters

As datacenter network bandwidth keeps growing, proactive transport becomes attractive, where bandwidth is proactively allocated as "credits" to senders who then can send "scheduled packets" at a right rate to ensure high link utilization, low latency, and zero packet loss. While promising, a fundamental challenge is that proactive transport requires at least one-RTT for credits to be computed and delivered. In this paper, we show such one-RTT "pre-credit" phase could carry a substantial amount of flows at high link-speeds, but none of existing proactive solutions treats it appropriately. We present Aeolus, a solution focusing on "pre-credit" packet transmission as a building block for proactive transports. Aeolus contains unconventional design principles such as scheduled-packet-first (SPF) that de-prioritizes the first-RTT packets, instead of prioritizing them as prior work. It further exploits the preserved, deterministic nature of proactive transport as a means to recover lost first-RTT packets efficiently. We have integrated Aeolus into ExpressPass[14], NDP[18] and Homa[29], and shown, through both implementation and simulations, that the Aeolus-enhanced solutions deliver signiicant performance or deployability advantages. For example, it improves the average FCT of ExpressPass by 56%, cuts the tail FCT of Homa by 20x, while achieving similar performance as NDP without switch modifications.
Enabling Morally Sensitive Robotic Clarification Requests

The design of current natural language-oriented robot architectures enables certain architectural components to circumvent moral reasoning capabilities. One example of this is reflexive generation of clarification requests as soon as referential ambiguity is detected in a human utterance. As shown in previous research, this can lead robots to (1) miscommunicate their moral dispositions and (2) weaken human perception or application of moral norms within their current context. We present a solution to these problems by performing moral reasoning on each potential disambiguation of an ambiguous human utterance and responding accordingly, rather than immediately and naively requesting clarification. We implement our solution in the Distributed Integrated Cognition Affect and Reflection robot architecture, which, to our knowledge, is the only current robot architecture with both moral reasoning and clarification request generation capabilities. We then evaluate our method with a human subjects experiment, the results of which indicate that our approach successfully ameliorates the two identified concerns.
The structure and characteristic scales of molecular clouds

The structure of molecular clouds holds important clues regarding the physical processes that lead to their formation and subsequent dynamical evolution. While it is well established that turbulence imprints a self-similar structure onto the clouds, other processes, such as gravity and stellar feedback, can break their scale-free nature. The break of self-similarity can manifest itself in the existence of characteristic scales that stand out from the underlying structure generated by turbulent motions. In this work, we investigate the structure of the Cygnus-X North and Polaris Flare molecular clouds, which represent two extremes in terms of their star formation activity. We characterize the structure of the clouds using the delta-variance (Δ-variance) spectrum. In the Polaris Flare, the structure of the cloud is self-similar over more than one order of magnitude in spatial scales. In contrast, the Δ-variance spectrum of Cygnus-X North exhibits an excess and a plateau on physical scales of ≈0.5−1.2 pc. In order to explain the observations for Cygnus-X North, we use synthetic maps where we overlay populations of discrete structures on top of a fractal Brownian motion (fBm) image. The properties of these structures, such as their major axis sizes, aspect ratios, and column density contrasts with the fBm image, are randomly drawn from parameterized distribution functions. We are able to show that, under plausible assumptions, it is possible to reproduce a Δ-variance spectrum that resembles that of the Cygnus-X North region. We also use a “reverse engineering” approach in which we extract the compact structures in the Cygnus-X North cloud and reinject them onto an fBm map. Using this approach, the calculated Δ-variance spectrum deviates from the observations and is an indication that the range of characteristic scales (≈0.5−1.2 pc) observed in Cygnus-X North is not only due to the existence of compact sources, but is a signature of the whole population of structures that exist in the cloud, including more extended and elongated structures.
On the Complexity of Binomialization for Polynomial Differential Equations

Chemical reaction networks (CRNs) are a standard formalism used in chemistry and biology to reason about the dynamics of molecular interaction networks. In their interpretation by ordinary differential equations, CRNs provide a Turing-complete model of analog computation , in the sense that any computable function over the reals can be computed by a finite number of molecular species with a continuous CRN which approximates the result of that function in one of its components in arbitrary precision. The proof of that result is based on a previous result of Bournez et al. on the Turing-completeness of polynomial ordinary differential equations with polynomial initial conditions (PIVP). It uses an encoding of real variables by two non-negative variables for concentrations , and a transformation to an equivalent binomial PIVP with degree at most 2 for restricting to at most bimolecular reactions. In this paper, we study the theoretical and practical complexities of the bino-mial transformation. We show that both problems of minimizing either the number of variables (i.e., molecular species) or the number of mono-mials (i.e. elementary reactions) in a binomial transformation of a PIVP are NP-hard. We present an encoding of those problems in MAX-SAT and show the practical complexity of this algorithm on a benchmark of binomialization problems inspired from CRN design problems.
Parameter estimation of stellar-mass black hole binaries with LISA

Stellar-mass black hole binaries (SBHBs), like those currently being detected with the ground-based gravitational-wave (GW) observatories LIGO and Virgo, are also an anticipated GW source for LISA. LISA will observe them during the early inspiral stage of evolution; some of them will chirp through the LISA band and reappear some time later in the band of $3^{rd}$ generation ground-based detectors. SBHBs could serve as laboratories for testing the theory of General Relativity and inferring the astrophysical properties of the underlying population. In this study, we assess LISA's ability to infer the parameters of those systems, a crucial first step in understanding and interpreting the observation of those binaries and their use in fundamental physics and astrophysics. We simulate LISA observations for several fiducial sources and perform a full Bayesian analysis. We demonstrate and explain degeneracies in the parameters of some systems. We show that the redshifted chirp mass and the sky location are always very well determined, with typical errors below $10^{-4}$ (fractional) and $0.4 \ {\rm deg^2}$. The luminosity distance to the source is typically measured within $40-60\%$, resulting in a measurement of the chirp mass in the source frame of $\mathcal{O}(1 \%)$. The error on the time to coalescence improves from $\mathcal{O}(1 \ {\rm day})$ to $\mathcal{O}(30 \ {\rm s})$ as we observe the systems closer to their merger. We introduce an augmented Fisher-matrix analysis which gives reliable predictions for the intrinsic parameters compared to the full Bayesian analysis. Finally, we show that combining the use of the long-wavelength approximation for the LISA instrumental response together with the introduction of a degradation function at high frequencies yields reliable results for the posterior distribution when used self-consistently, but not in the analysis of real LISA data.
3HWC: The Third HAWC Catalog of Very-high-energy Gamma-Ray Sources

We present a new catalog of TeV gamma-ray sources using 1523 days of data from the High-Altitude Water Cherenkov (HAWC) Observatory. The catalog represents the most sensitive survey of the northern gamma-ray sky at energies above several TeV, with three times the exposure compared to the previous HAWC catalog, 2HWC. We report 65 sources detected at ≥5σ significance, along with the positions and spectral fits for each source. The catalog contains eight sources that have no counterpart in the 2HWC catalog, but are within 1° of previously detected TeV emitters, and 20 sources that are more than 1° away from any previously detected TeV source. Of these 20 new sources, 14 have a potential counterpart in the fourth Fermi Large Area Telescope catalog of gamma-ray sources. We also explore potential associations of 3HWC sources with pulsars in the Australia Telescope National Facility (ATNF) pulsar catalog and supernova remnants in the Galactic supernova remnant catalog.
Bayesian hierarchical models for the prediction of the driver flow and passenger waiting times in a stochastic carpooling service

Carpooling is an integral component in smart carbon-neutral cities, in particular to facilitate home-work commuting. We study an innovative carpooling service which offers stochastic passenger-driver matching. Stochastic matching is when a passenger makes a carpooling request, and then waits for the first driver from a population of drivers who are already en route. Crucially a designated driver is not assigned as in a traditional carpooling service. For this new form of stochastic carpooling, we propose a two-stage Bayesian hierarchical model to predict the driver flow and the passenger waiting times. The first stage focuses on prediction of the aggregated daily driver flows, and the second stage processes these daily driver flow into hourly predictions of the passenger waiting times. We demonstrate, for an operational carpooling service, that the predictions from our Bayesian hierarchical model outperform the predictions from a frequentist model and a Bayesian non-hierarchical model. The inferences from our proposed model provide insights for the service operator in their evidence-based decision making.
Large spin to charge conversion in the topological superconductor 
β−PdBi2
 at room temperature

\b{eta}-PdBi2 has attracted much attention for its prospective ability to possess simultaneously topological surface and superconducting states due to its unprecedented spin-orbit interaction (SOC). Whereas most works have focused solely on investigating its topological surface states, the coupling between spin and charge degrees of freedom in this class of quantum material remains unexplored. Here we first report a study of spin-to-charge conversion in a \b{eta}-PdBi2 ultrathin film grown by molecular beam epitaxy, utilizing a spin pumping technique to perform inverse spin Hall effect measurements. We find that the room temperature spin Hall angle of Fe/\b{eta}-PdBi2, {\theta}_SH=0.037. This value is one order of magnitude larger than that of reported conventional superconductors, and is comparable to that of the best SOC metals and topological insulators. Our results provide an avenue for developing superconductor-based spintronic applications.
Improved Approximation Algorithms for Tverberg Partitions

$\newcommand{\floor}[1]{\left\lfloor {#1} \right\rfloor} \renewcommand{\Re}{\mathbb{R}}$ Tverberg's theorem states that a set of $n$ points in $\Re^d$ can be partitioned into $\floor{n/(d+1)}$ sets with a common intersection. A point in this intersection (aka Tverberg point) is a centerpoint of the input point set, and the Tverberg partition provides a compact proof of this, which is algorithmically useful. Unfortunately, computing a Tverberg point exactly requires $n^{O(d^2)}$ time. We provide several new approximation algorithms for this problem, which improve either the running time or quality of approximation, or both. In particular, we provide the first strongly polynomial (in both $n$ and $d$) approximation algorithm for finding a Tverberg point.
Two-stream Fusion Model for Dynamic Hand Gesture Recognition using 3D-CNN and 2D-CNN Optical Flow guided Motion Template

The use of hand gestures can be a useful tool for many applications in the human-computer interaction community. In a broad range of areas hand gesture techniques can be applied specifically in sign language recognition, robotic surgery, etc. In the process of hand gesture recognition, proper detection, and tracking of the moving hand become challenging due to the varied shape and size of the hand. Here the objective is to track the movement of the hand irrespective of the shape, size, and color of the hand. And, for this, a motion template guided by optical flow (OFMT) is proposed. OFMT is a compact representation of the motion information of a gesture encoded into a single image. In the experimentation, different datasets using bare hand with an open palm, and folded palm wearing green-glove are used, and in both cases, we could generate the OFMT images with equal precision. Recently, deep network-based techniques have shown impressive improvements as compared to conventional hand-crafted feature-based techniques. Moreover, in the literature, it is seen that the use of different streams with informative input data helps to increase the performance in the recognition accuracy. This work basically proposes a two-stream fusion model for hand gesture recognition and a compact yet efficient motion template based on optical flow. Specifically, the two-stream network consists of two layers: a 3D convolutional neural network (C3D) that takes gesture videos as input and a 2D-CNN that takes OFMT images as input. C3D has shown its efficiency in capturing spatio-temporal information of a video. Whereas OFMT helps to eliminate irrelevant gestures providing additional motion information. Though each stream can work independently, they are combined with a fusion scheme to boost the recognition results. We have shown the efficiency of the proposed two-stream network on two databases.
Reinforcement Learning-Enabled Decision-Making Strategies for a Vehicle-Cyber-Physical-System in Connected Environment

As a typical vehicle-cyber-physical-system (V-CPS), connected automated vehicles attracted more and more attention in recent years. This paper focuses on discussing the decision-making (DM) strategy for autonomous vehicles in a connected environment. First, the highway DM problem is formulated, wherein the vehicles can exchange information via wireless networking. Then, two classical reinforcement learning (RL) algorithms, Q-learning and Dyna, are leveraged to derive the DM strategies in a predefined driving scenario. Finally, the control performance of the derived DM policies in safety and efficiency is analyzed. Furthermore, the inherent differences of the RL algorithms are embodied and discussed in DM strategies.
Entangling the vibrational modes of two massive ferromagnetic spheres using cavity magnomechanics

We present a scheme to entangle the vibrational phonon modes of two massive ferromagnetic spheres in a dual-cavity magnomechanical system. In each cavity, a microwave cavity mode couples to a magnon mode (spin wave) via the magnetic dipole interaction, and the latter further couples to a deformation phonon mode of the ferromagnetic sphere via a nonlinear magnetostrictive interaction. We show that by directly driving the magnon mode with a red-detuned microwave field to activate the magnomechanical anti-Stokes process a cavity–magnon–phonon state-swap interaction can be realized. Therefore, if the two cavities are further driven by a two-mode squeezed vacuum field, the quantum correlation of the driving fields is successively transferred to the two magnon modes and subsequently to the two phonon modes, i.e., the two ferromagnetic spheres become remotely entangled. Our work demonstrates that cavity magnomechanical systems allow to prepare quantum entangled states at a more massive scale than currently possible with other schemes.
A mixed integer programming approach for asset protection during escaped wildfires

Incident management teams (IMTs) are responsible for managing the response to wildfires. One of the objectives of IMTs is the protection of assets and infrastructure. In this paper, we develop a mathematical model to assist IMTs in assigning resources to asset protection activities during wildfires. We present a mixed integer programming model for resource allocation with the aim of protecting the maximum possible total value of assets. The model allows for mixed vehicle types with interchangeable capabilities and with travel times determined by vehicle-specific speed and road network information. We define location-specific protection requirements in terms of vehicle capabilities. The formulated model extends classic variants of the team orienteering problem with time windows. The model capabilities are demonstrated using a hypothetical fire scenario impacting South Hobart, Tasmania, Australia. Computational testing shows that realistically sized problems can be solved within a reasonable time.
A Hölderian backtracking method for min-max and min-min problems

We present a new algorithm to solve min-max or min-min problems out of the convex world. We use rigidity assumptions, ubiquitous in learning, making our method applicable to many optimization problems. Our approach takes advantage of hidden regularity properties and allows us to devise a simple algorithm of ridge type. An original feature of our method is to come with automatic step size adaptation which departs from the usual overly cautious backtracking methods. In a general framework, we provide convergence theoretical guarantees and rates. We apply our findings on simple GAN problems obtaining promising numerical results.
Sparse-grid sampling recovery and deep ReLU neural networks in high-dimensional approximation

We investigate approximations of functions from the Holder-Zygmund space of mixed smoothness $H^\alpha_\infty({\mathbb I}^d)$ defined on the $d$-dimensional unit cube ${\mathbb I}^d:=[0,1]^d$, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension $d$ may be very large. The approximation error is measured in the norm of the isotropic Sobolev space $W^{1,p}_0({\mathbb I}^d)$. The optimality of this sampling recovery is studied in terms of sampling $n$-widths. Optimal linear sampling algorithms are constructed on sparse grids using the piece-wise linear B-spline interpolation representation. We prove some tight dimension-dependent bounds of the sampling $n$-widths explicit in $d$ and $n$. Based on the results on sampling recovery, we investigate the expressive power of deep ReLU neural networks to approximate functions in Holder-Zygmund space. Namely, for any function $f\in H^\alpha_\infty({\mathbb I}^d)$, we explicitly construct a deep ReLU neural network having an output that approximates $f$ in the $W_0^{1,p}({\mathbb I}^d)$-norm with a prescribed accuracy $\varepsilon$, and prove tight dimension-dependent bounds of the computation complexity of this approximation, characterized as the number of weights and the depth of this deep ReLU neural network, explicitly in $d$ and $\varepsilon$. Moreover, we show that under a certain restriction the curse of dimensionality can be avoided in the approximations by sparse-grid sampling recovery and deep ReLU neural networks.
Tackling scalability issues in mining path patterns from knowledge graphs: a preliminary study

Features mined from knowledge graphs are widely used within multiple knowledge discovery tasks such as classification or fact-checking. Here, we consider a given set of vertices, called seed vertices, and focus on mining their associated neighboring vertices, paths, and, more generally, path patterns that involve classes of ontologies linked with knowledge graphs. Due to the combinatorial nature and the increasing size of real-world knowledge graphs, the task of mining these patterns immediately entails scalability issues. In this paper, we address these issues by proposing a pattern mining approach that relies on a set of constraints (e.g., support or degree thresholds) and the monotonicity property. As our motivation comes from the mining of real-world knowledge graphs, we illustrate our approach with PGxLOD, a biomedical knowledge graph.