Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilizing Fiber-Optic Cherenkov Radiation for Electron Beam Dosimetry at Ultra High Dose Rates

As a standard method of cancer treatment, conventional radiation therapy (RT) involves a frac-tionated dose, delivered over approximately 10 to 30 minutes [6]. In 2014, FLASH-RT was introduced as a radiotherapy method that reduced radiation effects in normal tissue without compromising on its anti-cancer effects [7]. Unlike conventional radiation therapy, FLASH-RT utilizes an ultra-high dose rate (> 100 Gy/s), delivering the same amount of dose in milliseconds rather than minutes. RARAF’s FLASH irradiator, is able to achieve much higher FLASH dose rates, locally termed as ’superFLASH’ [1]. With instantaneous dose rates up to 30 MGy/second (0.2 - 150 Gy per pulse and 360 pulses per second), many real-time traditional dosimetric methods (like the Advanced Markus ion chamber) fail in this mode, due to signal oversaturation. This report will investigate the feasibility of a fiber optic Cherenkov radiation (FOCR) system as an alternative real-time beam monitor. After a total of five trial runs, it is observed that the FOCR system does work as intended, when certain conditions in the accelerator hall line up. A dual-series trend is observed over four out of five trials, where the remaining trial data successfully and completely correlated with the actual dose observed. The consistency of outlier measurements implies an unknown, systematic variable being overlooked, most likely due to the variance of the Clinac beam width. Though further development of this system is needed, the experiments conducted provide a satisfactory starting point for a real-time electron beam monitor at ultra high dose rates.
Magnetism in the Early Universe

Abstract Blazar observations point toward the possible presence of magnetic fields over intergalactic scales of the order of up to ∼1 Mpc, with strengths of at least ∼10−16 G. Understanding the origin of these large-scale magnetic fields is a challenge for modern astrophysics. Here we discuss the cosmological scenario, focussing on the following questions: (i) How and when was this magnetic field generated? (ii) How does it evolve during the expansion of the universe? (iii) Are the amplitude and statistical properties of this field such that they can explain the strengths and correlation lengths of observed magnetic fields? We also discuss the possibility of observing primordial turbulence through direct detection of stochastic gravitational waves in the mHz range accessible to LISA.
How to talk so AI will learn: Instructions, descriptions, and pragmatics

Humans intuitively use language to express our beliefs and desires, but today we lack computational models explaining such abstract language use. To address this challenge, we consider social learning in a linear bandit setting and ask how a human might communicate preferences over behaviors (i.e. the reward function). We study two distinct types of language: instructions , which specify partial policies, and descriptions , which provide information about the reward function. To explain how humans use such language, we suggest they reason about both known present and unknown future states: instructions optimize for the present, while descriptions optimize for the future. We formalize this choice by extending reward design to consider a distribution over states. We then define a pragmatic listener agent that infers the speaker’s reward function by reasoning about how the speaker expresses themselves. Simulations suggest that (1) descriptions afford stronger learning than instructions; and (2) maintaining uncertainty over the speaker’s pedagogical intent allows for robust reward inference. We hope these insights facilitate a shift from developing agents that obey language to agents that learn from it.
Evaluation of Adjuvant Activity and Bio-Distribution of Archaeosomes Prepared Using Microfluidic Technology

Archaeosomes, composed of sulfated lactosyl archaeol (SLA) glycolipids, have been proven to be an effective vaccine adjuvant in multiple preclinical models of infectious disease or cancer. They have classically been prepared using a thin-film hydration method with an average particle size of 100–200 nm. In this study, we developed methods to generate SLA archaeosomes at different sizes, i.e., 30 nm and 100 nm, via microfluidic mixing technology and evaluated their physicochemical characteristics, as well as adjuvant activity and in vivo biodistribution in mice. Archaeosomes, prepared using thin-film and microfluidic mixing techniques, had similar nanostructures and physicochemical characteristics, with both appearing stable during the course of this study when stored at 4 °C or 37 °C. They also demonstrated similar adjuvant activity when admixed with ovalbumin antigen and used to immunize mice, generating equivalent antigen-specific immune responses. Archaeosomes, labeled with CellVueTM NIR815, had an equivalent biodistribution with both sizes, namely the highest signal at the injection site at 24 h post injection, followed by liver, spleen and inguinal lymph node. The presence of SLA archaeosomes of either size helped to retain OVA antigen (OVA-Cy5.5) longer at the injection site than unadjuvanted OVA. Overall, archaeosomes of two sizes (30 nm and 100 nm) prepared using microfluidic mixing maintained similar physicochemical properties, adjuvant activity and biodistribution of antigen, in comparison to those compared by the conventional thin film hydration method. This suggests that microfluidics based approaches could be applied to generate consistently sized archaeosomes for use as a vaccine adjuvant.
Prediction

—Predicting the stocks price is both intriguing and difficult due to the nonlinear and noisy stock’s data. Stock investors’ investment risk can reduce and their return on investment can be significantly increased by properly predicting the change in stock prices. It is challenging for researchers to forecast stock prices using an individual stock forecasting model. Therefore, in this research we proposes MGRU-M1dCNN, a hybrid model to predict the stock opening price of the next day. This model is consisted of multiple gated recurrent unit (MGRU) and Multiple single dimensional convolutional neural networks (M1dCNN). MGRU employs to extract the valuable feature patterns from opening price of stocks. M1dCNN is used to extract the spatial features of the inputs received from MGRU to predict the stock prices. The outcomes demonstrate that this technique performs best, with the least Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) which are 0.854 and 0.456. When compared to other GRU based models (GRU-CNN, GRU-DNN, and GRU), the MGRU-M1dCNN model is better suited for stock price forecasting and for providing investors a trustworthy means to choose which stocks to buy.
Unified theory for Goos-Hanchen and imbert-fedorov effects

A unified theory is advanced to describe both the lateral Goos-Haenchen (GH) effect and the transverse Imbert-Fedorov (IF) effect, through representing the vector angular spectrum of a three-dimensional light beam in terms of a two-form angular spectrum consisting of its two orthogonal polarized components. From this theory, the quantization characteristics of the GH and IF displacements are obtained, and the Artmann formula for the GH displacement is derived. It is found that the eigenstates of the GH displacement are the two orthogonal linear polarizations in this two-form representation, and the eigenstates of the IF displacement are the two orthogonal circular polarizations. The theoretical predictions are found to be in agreement with recent experimental results.
Galaxies at z ~ 6: The UV Luminosity Function and Luminosity Density from 506 HUDF, HUDF Parallel ACS Field, and GOODS i-Dropouts

We have detected 506 i-dropouts (z ~ 6 galaxies) in deep, wide-area HST ACS fields: HUDF, enhanced GOODS, and HUDF parallel ACS fields (HUDF-Ps). The contamination levels are ≲8% (i.e., ≳92% are at z ~ 6). With these samples, we present the most comprehensive, quantitative analyses of z ~ 6 galaxies yet and provide optimal measures of the UV luminosity function (LF) and luminosity density at z ~ 6, and their evolution to z ~ 3. We redetermine the size and color evolution from z ~ 6 to z ~ 3. Field-to-field variations (cosmic variance), completeness, flux, and contamination corrections are modeled systematically and quantitatively. After corrections, we derive a rest-frame continuum UV (~1350 Å) LF at z ~ 6 that extends to M1350,AB ~ -17.5 (0.04L). There is strong evidence for evolution of the LF between z ~ 6 and z ~ 3, most likely through a brightening (0.6 ± 0.2 mag) of M* (at 99.7% confidence), although the degree depends on the faint-end slope. As expected from hierarchical models, the most luminous galaxies are deficient at z ~ 6. Density evolution (ϕ*) is ruled out at >99.99% confidence. Despite large changes in the LF, the luminosity density at z ~ 6 is similar to (0.82 ± 0.21 times) that at z ~ 3. Changes in the mean UV color of galaxies from z ~ 6 to z ~ 3 suggest an evolution in dust content, indicating that the true evolution is substantially larger: at z ~ 6 the star formation rate density is just ~30% of the z ~ 3 value. Our UV LF is consistent with z ~ 6 galaxies providing the necessary UV flux to reionize the universe.
Peahen: fast and precise static deadlock detection via context reduction

Deadlocks still severely inflict reliability and security issues upon software systems of the modern age. Worse still, as we note, in prior static deadlock detectors, good precision does not go hand-in-hand with high scalability --- their approaches are either context-insensitive, thereby engendering many false positives, or suffer from the calling context explosion to reach context-sensitive, thus compromising good efficiency. In this paper, we advocate Peahen, geared towards precise yet also scalable static deadlock detection. At its crux, Peahen decomposes the computational effort for embracing high precision into two cooperative analysis stages: (i) context-insensitive lock-graph construction, which selectively encodes the essential lock-acquisition information on each edge, and (ii) three precise yet lazy refinements, which incorporate such edge information into progressively refining the deadlock cycles in the lock graph only for a few interesting calling contexts. Our extensive experiments yield promising results: Peahen dramatically out-performs the state-of-the-art tools on accuracy without losing scalability; it can efficiently check million-line systems at a low false positive rate; and it has uncovered many confirmed deadlocks in dozens of mature open-source systems.
Discovering the rules of plant biogeography using a trait-based approach

11 Lineage-specific traits determine how plants interact with their surrounding environment. As 12 different species may find similar phenotypic solutions through evolution to tolerate, persist in 13 and invade environments with certain characteristics, some traits may become more common in 14 certain types of habitats. These general patterns of geographical trait distribution point towards 15 the existence of some rules in how plants diversify in space over time. Trait-environment 16 correlation analyses are ways to discover general rules in plant biogeography by quantifying to 17 what extent unrelated lineages have similar evolutionary responses to a given type of habitat. In 18 this synthesis, I give a short historical overview on trait-environment correlation analyses, from 19 some key observations from classic naturalists to modern approaches using trait evolution 20 models, large phylogenies, and massive datasets of traits and distributions. I discuss some 21 limitations of modern approaches, including the need for more realistic models, the lack of data 22 from tropical areas, and the necessary focus on trait scoring that goes beyond macro- 23 morphology. Overcoming these limitations will allow the field to explore new questions related 24 to trait lability and niche evolution and to better set apart rules and exceptions in how plants 25 diversify in space over time.
“I know how you feel”

In this article, we aim to evaluate the role of robots’ personality-driven behavioural patterns on users’ intention to use in an entertainment scenario. Toward such a goal, we designed two personalities: one introverted with an empathic and self-comparative interaction style, and the other extroverted with a provocative and other-comparative interaction style. To evaluate the proposed technology acceptance model, we conducted an experiment (N = 209) at a public venue where users were requested to play a game with the support of the TIAGo robot. Our findings show that the robot personality affects the acceptance model and three relevant drivers: perceived enjoyment, perceived usefulness, and social influence. The extroverted robot was perceived as more useful than the introverted, and participants who interacted with it were faster at solving the game. On the other hand, the introverted robot was perceived as more enjoyable but less useful than the extroverted, and participants who interacted with it made fewer mistakes. Taken together, these findings support the importance of designing proper robot personalities in influencing users’ acceptance, featuring that a given style can elicit a different driver of acceptance.
Electron-positron pair production in slow collisions of heavy nuclei beyond the monopole approximation

Electron-positron pair production in low-energy collisions of heavy nuclei is considered beyond the monopole approximation. The calculation method is based on the numerical solving of the time-dependent Dirac equation with the full two-center potential. Bound-free and free-free pair-production probabilities as well as the energy spectra of the emitted positrons are calculated for the collisions of bare uranium nuclei. The calculations are performed for collision energy near the Coulomb barrier for different values of the impact parameter. The obtained results are compared with the corresponding values calculated in the monopole approximation.
Genetic Diversity and Population Genetic Structure Analysis of Plasmodium knowlesi Thrombospondin-Related Apical Merozoite Protein (TRAMP) in Clinical Samples

The simian malaria parasite Plasmodium knowlesi causes a high number of zoonotic infections in Malaysia. The thrombospondin-related apical merozoite protein (TRAMP) is an essential ligand for binding to the erythrocyte cell surface, whereby it facilitates the invasion. This study is the first attempt to determine the genetic diversity, phylogeography, natural selection and population structure from 97 full-length PkTRAMP gene sequences originating from Malaysia. We found low levels of nucleotide diversity (π~0.0065) for the full-length gene despite samples originating from geographically separated regions (i.e., Peninsular Malaysia and Malaysian Borneo). The rate of synonymous substitutions was significantly higher than that of non-synonymous substitutions, indicating a purifying selection for the full-length gene within the clinical samples. The population genetic analysis revealed that the parasite population is undergoing a significant population expansion. The analysis of the amino acid sequence alignment of 97 PkTRAMP sequences identified 15 haplotypes, of which a major shared haplotype was noted Hap 1 (n = 68, Sarawak; n = 34, Sabah; n = 12, Peninsular Malaysia; n = 22). The phylogenetic analysis using DNA sequences identified two clusters that separated due to geographical distance and three mixed clusters with samples from both Peninsular Malaysia and Malaysian Borneo. Population structure analyses indicated two distinct sub-populations (K = 2). Our findings point to the potential for independent parasite evolution, which could make zoonotic malaria control and elimination even more challenging.
Multitype Λ-coalescents

Consider a multitype coalescent process in which each block has a colour in { 1 ,. ,d } . Individual blocks may change colour, and some number of blocks of various colours may merge to form a new block of some colour. We show that if the law of a multitype coalescent process is invariant under permutations of blocks of the same colour, has consistent Markovian projections, and has asynchronous mergers, then it is a multitype Λ -coalescent: a process in which single blocks may change colour, two blocks of like colour may merge to form a single block of that colour, or large mergers across various colours happen at rates governed by a d -tuple of measures on [0 , 1] d . We go on to identify when such processes come down from infinity. Our framework generalises Pitman’s celebrated classification theorem for singletype coalescent processes, and provides a unifying setting for numerous examples that have appeared in the literature, including the seed-bank model, the island model, and the coalescent structure of continuous-state branching processes.
Replicating the facilitatory effects of transcranial random noise stimulation on motion processing: A registered report

Non-invasive brain stimulation (NIBS) techniques have the potential to demonstrate the causal impact of targeted brain regions on specific behaviors, and to regulate or facilitate behavior in clinical applications. Transcranial random noise stimulation (tRNS) is one form of transcranial electric stimulation (tES) in which an alternating current is passed between electrodes at random frequencies. High-frequency tRNS (hf-tRNS) is thought to enhance excitability and has been reported to have facilitatory effects on behavior in healthy and clinical populations. Due to the potential application of tRNS, clear demonstrations of the efficacy and replicability of stimulation are critical. Here, we propose to replicate the facilitatory effect of hf-tRNS over the human middle temporal complex (hMT+) on contralateral motion processing, initially demonstrated by Ghin et al. (2018). The improvement in performance was specific to global motion processing in the visual field contralateral to stimulation. The motivation to replicate this effect is reinforced by the well-supported hypothesis that hMT+ is critical for contralateral global motion processing. We hypothesize that we will replicate the facilitatory effect of hf-tRNS to hMT+ on contralateral global motion processing in comparison to sham stimulation. Furthermore, we extend the original findings with the addition of a within-subjects comparison between stimulation to target region hMT+ and an active control, the forehead. We expect to find a significant contralateral stimulation effect for hMT+ only.
INdAM-DP-COFUND 2015 fellowship

During my fellowship period at my host institution, Department of Mathematics Università degli Studi di Roma Tre, under a supervision of Prof. Luca De Feo, we studied mainly some applications of algebraic geometry to cryptography, in particular to post-quantum cryptography (PQC). One of the main reasons to draw attention toward PQC is the algorithm of Shor [22]. Due to this algorithm, all the public key cryptosystems based on the difficulty of the discrete logarithm or integer factorization problems became vulnerable to polynomial-time attacks by quantum computers. Moreover, a competition organized by the United States government agency National Institute of Standards and Technology (NIST) for new post-quantum cryptographic algorithms [17] and its recent third selection round highlight the important of postquantum cryptography. Participants of the competition can be classified into latticebased, code-based, multivariate, hash-based, [11] and isogeny based cryptography. Most of these cryptographic constructions use techniques from algebraic geometry, thereby proving its efficacy in post-quantum cryptography. Our study was concentrated on two parts. The first part is related to the isogeny problems and the second part is devoted to new key exchange and signature protocols based on hard problems occurred in Veronese variety and secant variety of the Grassmannian. Isogeny based cryptography is appealing in the area of post-quantum cryptography because of its relatively small key size and underlying beautiful mathematical theory. Many variants of the isogeny problems are used in many existing primitives [12, 1, 4, 10, 9]. There are two main hard problems in isogeny based schemes: finding an isogeny between two elliptic curves and computing the endomorphism ring of an elliptic curve. These two problems are related to each other [14] and there is no sub-exponential algorithm to solve them for supersingular elliptic curves [20]. The computation of a single endomorphism of an elliptic curve E is also closely related to the problem of computing the endomorphism ring of E [13].
Axions, Instantons, and the Lattice

If the QCD axion is a significant component of dark matter, and if the universe was once hotter than a few hundred MeV, the axion relic abundance depends on the function $\chi(T)$, the temperature-dependent topological susceptibility. Uncertainties in this quantity induce uncertainties in the axion mass as a function of the relic density, or vice versa. At high temperatures, theoretical uncertainties enter through the dilute instanton gas computation, while in the intermediate and strong coupling regime, only lattice QCD can determine $\chi(T)$ precisely. We reassess the uncertainty on the instanton contribution, arguing that it amounts to less than $20\%$ in the effective action, or a factor of 20 in $\chi$ at $T=1.5$ GeV. We then combine the instanton uncertainty with a range of models for $\chi(T)$ at intermediate temperatures and determine the impact on the axion relic density. We find that for a given relic density and initial misalignment angle, the combined uncertainty amounts to a factor of 2-3 in the zero-temperature axion mass.
Compression of femtosecond pulses in a wide wavelength range using a large-mode-area tapered fiber

We report the design of a tapered fiber that can be used for the compression of pulses at different central wavelengths. The proposed fiber is a three-layer W-type large-mode-area fiber, which has been tapered to transform the mode area from 1700 µm2 to 900 µm2. We determine the exact length of the maximum pulse compression and numerically demonstrate the compression of a 250 fs, 100 kW peak power input pulse to 15 fs, 850 kW at 1.55 µm wavelength and compression of 250 fs, 120 kW peak power input pulses to 28 fs, 700 kW and to 46 fs, 500 kW at 1.8 µm and 2 µm wavelengths, respectively. Such a fiber can find wide ranging applications, including in communication, spectroscopy and medicine.
Impact of AI and Digital Twins on IIoT

We discuss the role and impact of AI on the Industrial Internet of Things (IIoT) as envisioned by the European ﬂagship project on 6G, Hexa-X. The envisioned ecosystem of trustworthy collaborative digital twins (DTs) lays the foundation for emergent intelligence (EI) and utilization of AI for industrial scenarios. One important building block for utilization of AI in IIoT is the inclusion of the human: we therefore provide insights on AI at the intersection between DTs and human-machine interfaces (HMIs).
Dropout Disagreement: A Recipe for Group Robustness with Fewer Annotations

Empirical risk minimization (ERM) of neural networks can cause over-reliance on spurious correlations and poor generalization on minority groups. Deep feature reweighting (DFR) [8] improves group robustness via last-layer retraining, but it requires full group and class annotations for the reweighting dataset. To eliminate this impractical requirement, we propose a one-shot active learning method which constructs the reweighting dataset with the disagreement points between the ERM model with and without dropout activated. Our experiments show our approach achieves 94% of DFR performance on the Waterbirds and CelebA datasets despite using no group annotations and up to 21 × fewer class annotations.