Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory-Sample Lower Bounds for Learning Parity with Noise

In this work, we show, for the well-studied problem of learning parity under noise, where a learner tries to learn $x=(x_1,\ldots,x_n) \in \{0,1\}^n$ from a stream of random linear equations over $\mathrm{F}_2$ that are correct with probability $\frac{1}{2}+\varepsilon$ and flipped with probability $\frac{1}{2}-\varepsilon$, that any learning algorithm requires either a memory of size $\Omega(n^2/\varepsilon)$ or an exponential number of samples. In fact, we study memory-sample lower bounds for a large class of learning problems, as characterized by [GRT'18], when the samples are noisy. A matrix $M: A \times X \rightarrow \{-1,1\}$ corresponds to the following learning problem with error parameter $\varepsilon$: an unknown element $x \in X$ is chosen uniformly at random. A learner tries to learn $x$ from a stream of samples, $(a_1, b_1), (a_2, b_2) \ldots$, where for every $i$, $a_i \in A$ is chosen uniformly at random and $b_i = M(a_i,x)$ with probability $1/2+\varepsilon$ and $b_i = -M(a_i,x)$ with probability $1/2-\varepsilon$ ($0<\varepsilon<\frac{1}{2}$). Assume that $k,\ell, r$ are such that any submatrix of $M$ of at least $2^{-k} \cdot |A|$ rows and at least $2^{-\ell} \cdot |X|$ columns, has a bias of at most $2^{-r}$. We show that any learning algorithm for the learning problem corresponding to $M$, with error, requires either a memory of size at least $\Omega\left(\frac{k \cdot \ell}{\varepsilon} \right)$, or at least $2^{\Omega(r)}$ samples. In particular, this shows that for a large class of learning problems, same as those in [GRT'18], any learning algorithm requires either a memory of size at least $\Omega\left(\frac{(\log |X|) \cdot (\log |A|)}{\varepsilon}\right)$ or an exponential number of noisy samples. Our proof is based on adapting the arguments in [Raz'17,GRT'18] to the noisy case.
Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence

With the arrival of next generation wireless communication, a growing number of new applications like internet of things, autonomous driving systems, and drone are crowding the unlicensed spectrum. Licensed network such as the long-term evolution (LTE) also comes to the unlicensed spectrum for better providing high-capacity contents with low cost. However, LTE was not designed to share resources with others. Previous solutions usually work on fixed scenarios. This work features a Nonparametric Bayesian reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE licensed assisted access (LTE-LAA) agents in 5 GHz unlicensed spectrum. The coexistence problem is modeled as a decentralized partially-observable Markov decision process (Dec-POMDP) and Bayesian inference is adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure is introduced in the reward function to encourage fair sharing between agents. Variational inference for posterior model approximation is considered to make the algorithm computationally efficient. Simulation results demonstrate that this algorithm can reach high value with compact policy representations in few learning iterations.
Accessing Plasmonic Hotspots Using Nanoparticle-on-Foil Constructs

Metal–insulator–metal (MIM) nanogaps in the canonical nanoparticle-on-mirror geometry (NPoM) provide deep-subwavelength confinement of light with mode volumes smaller than V/Vλ < 10–6. However, access to these hotspots is limited by the impendence mismatch between the high in-plane k∥ of trapped light and free-space plane-waves, making the in- and out-coupling of light difficult. Here, by constructing a nanoparticle-on-foil (NPoF) system with thin metal films, we show the mixing of insulator–metal–insulator (IMI) modes and MIM gap modes results in MIMI modes. This mixing provides multichannel access to the plasmonic nanocavity through light incident from both sides of the metal film. The red-tuning and near-field strength of MIMI modes for thinner foils is measured experimentally with white-light scattering and surface-enhanced Raman scattering from individual NPoFs. We discuss further the utility of NPoF systems, since the geometry allows tightly confined light to be accessed simply through different ports.
Independent Encoder for Deep Hierarchical Unsupervised Image-to-Image Translation

The main challenges of image-to-image (I2I) translation are to make the translated image realistic and retain as much information from the source domain as possible. To address this issue, we propose a novel architecture, termed as IEGAN, which removes the encoder of each network and introduces an encoder that is independent of other networks. Compared with previous models, it embodies three advantages of our model: Firstly, it is more directly and comprehensively to grasp image information since the encoder no longer receives loss from generator and discriminator. Secondly, the independent encoder allows each network to focus more on its own goal which makes the translated image more realistic. Thirdly, the reduction in the number of encoders performs more unified image representation. However, when the independent encoder applies two down-sampling blocks, it's hard to extract semantic information. To tackle this problem, we propose deep and shallow information space containing characteristic and semantic information, which can guide the model to translate high-quality images under the task with significant shape or texture change. We compare IEGAN with other previous models, and conduct researches on semantic information consistency and component ablation at the same time. These experiments show the superiority and effectiveness of our architecture. Our code is published on: https://github.com/Elvinky/IEGAN.
Unsupervised learning of MRI tissue properties using MRI physics models

In neuroimaging, MRI tissue properties characterize underlying neurobiology, provide quantitative biomarkers for neurological disease detection and analysis, and can be used to synthesize arbitrary MRI contrasts. Estimating tissue properties from a single scan session using a protocol available on all clinical scanners promises to reduce scan time and cost, enable quantitative analysis in routine clinical scans and provide scan-independent biomarkers of disease. However, existing tissue properties estimation methods - most often $\mathbf{T_1}$ relaxation, $\mathbf{T_2^*}$ relaxation, and proton density ($\mathbf{PD}$) - require data from multiple scan sessions and cannot estimate all properties from a single clinically available MRI protocol such as the multiecho MRI scan. In addition, the widespread use of non-standard acquisition parameters across clinical imaging sites require estimation methods that can generalize across varying scanner parameters. However, existing learning methods are acquisition protocol specific and cannot estimate from heterogenous clinical data from different imaging sites. In this work we propose an unsupervised deep-learning strategy that employs MRI physics to estimate all three tissue properties from a single multiecho MRI scan session, and generalizes across varying acquisition parameters. The proposed strategy optimizes accurate synthesis of new MRI contrasts from estimated latent tissue properties, enabling unsupervised training, we also employ random acquisition parameters during training to achieve acquisition generalization. We provide the first demonstration of estimating all tissue properties from a single multiecho scan session. We demonstrate improved accuracy and generalizability for tissue property estimation and MRI synthesis.
Departure Time Choice Models in Urban Transportation Systems Based on Mean Field Games

Departure time choice models play a crucial role in determining the traffic load in transportation systems. Most studies that consider departure time user equilibrium (DTUE) problems make assumptions on the user characteristics (e.g., distribution of desired arrival time and trip length) or dynamic traffic model (e.g., classic bathtub or point queue models) in order to analyze the problem. This paper relaxes these assumptions and introduces a new framework to model and analyze the DTUE problem based on the so-called mean field games (MFGs) theory. MFGs allow us to define players at the microscopic level similar to classical game theory models, translating the effect of players’ decisions to macroscopic models. In this paper, we first present a continuous departure time choice model and investigate the equilibria of the system. Specifically, we demonstrate the existence of the equilibrium and characterize the DTUE. Then, a discrete approximation of the system is provided based on deterministic differential game models to numerically obtain the equilibrium of the system. To examine the efficiency of the proposed model, we compare it with the departure time choice models in the literature. We apply our framework to a standard test case and observe that the solutions obtained based on our model are 5.6% better in terms of relative cost compared with the solutions determined based on previous studies. Moreover, our proposed model converges with fewer iterations than the reference solution method in the literature. Finally, the model is scaled up to the real test case corresponding to the whole Lyon metropolis with a real demand pattern. The results show that the proposed framework is able to tackle a much larger test case than usual to include multiple preferred travel times and heterogeneous trip lengths more accurately than existing models.
Detecting mid-infrared light by molecular frequency upconversion in dual-wavelength nanoantennas

Description Optomechanical upconversion Molecules have rich signatures in their spectra at infrared wavelengths and are typically accessed with dedicated spectroscopic instrumentation. Chen et al. and Xomalis et al. report optomechanical frequency upconversion from the mid-infrared to the visible domain using molecular vibrations coupled to a plasmonic nanocavity at ambient conditions (see the Perspective by Gordon). Using different nanoantenna designs, one with a nanoparticle-on-resonator and the other with nanoparticle-in-groove, both approaches show the ability to upconvert the mid-infrared vibrations of the molecules in the nanocavity to visible light wavelengths. The effect could be used to simplify infrared spectroscopy, possibly with single-molecule sensitivity. —ISO Mid-infrared light is upconverted to visible wavelengths using vibrating molecules inside a plasmonic nanocavity. Coherent interconversion of signals between optical and mechanical domains is enabled by optomechanical interactions. Extreme light-matter coupling produced by confining light to nanoscale mode volumes can then access single mid-infrared (MIR) photon sensitivity. Here, we used the infrared absorption and Raman activity of molecular vibrations in plasmonic nanocavities to demonstrate frequency upconversion. We converted approximately 10-micrometer-wavelength incoming light to visible light by surface-enhanced Raman scattering (SERS) in doubly resonant antennas that enhanced upconversion by more than 1010. We showed 140% amplification of the SERS anti-Stokes emission when an MIR pump was tuned to a molecular vibrational frequency, obtaining lowest detectable powers of 1 to 10 microwatts per square micrometer at room temperature. These results have potential for low-cost and large-scale infrared detectors and spectroscopic techniques.
State-efficient QFA Algorithm for Quantum Computers

The study of quantum ﬁnite automata (QFA’s) is one of the possible approaches in exploring quantum computers with ﬁnite memory. Despite being one of the most restricted models, Moore-Crutchﬁeld quantum ﬁnite automaton (MCQFA) is proven to be exponentially more succinct than classical ﬁnite automata models in recognizing certain languages such as MOD p = { a j | j ≡ 0 mod p } , where p is a prime number. In this paper, we present a modiﬁed MCQFA algorithm for the language MOD p , the operators of which are selected based on the basis gates on the available real quantum computers. As a consequence, we obtain shorter quantum programs using less basis gates compared to the implementation of the original algorithm given in the literature.
On the Hardness of Compressing Weights

We investigate computational problems involving large weights through the lens of kernelization, which is a framework of polynomial-time preprocessing aimed at compressing the instance size. Our main focus is the weighted Clique problem, where we are given an edge-weighted graph and the goal is to detect a clique of total weight equal to a prescribed value. We show that the weighted variant, parameterized by the number of vertices $n$, is significantly harder than the unweighted problem by presenting an $O(n^{3 - \varepsilon})$ lower bound on the size of the kernel, under the assumption that NP $\not \subseteq$ coNP/poly. This lower bound is essentially tight: we show that we can reduce the problem to the case with weights bounded by $2^{O(n)}$, which yields a randomized kernel of $O(n^3)$ bits. We generalize these results to the weighted $d$-Uniform Hyperclique problem, Subset Sum, and weighted variants of Boolean Constraint Satisfaction Problems (CSPs). We also study weighted minimization problems and show that weight compression is easier when we only want to preserve the collection of optimal solutions. Namely, we show that for node-weighted Vertex Cover on bipartite graphs it is possible to maintain the set of optimal solutions using integer weights from the range $[1, n]$, but if we want to maintain the ordering of the weights of all inclusion-minimal solutions, then weights as large as $2^{\Omega(n)}$ are necessary.
Twin-width IV: ordered graphs and matrices

We establish a list of characterizations of bounded twin-width for hereditary classes of totally ordered graphs: as classes of at most exponential growth studied in enumerative combinatorics, as monadically NIP classes studied in model theory, as classes that do not transduce the class of all graphs studied in finite model theory, and as classes for which model checking first-order logic is fixed-parameter tractable studied in algorithmic graph theory. This has several consequences. First, it allows us to show that every hereditary class of ordered graphs either has at most exponential growth, or has at least factorial growth. This settles a question first asked by Balogh, Bollobás, and Morris [Eur. J. Comb. ’06] on the growth of hereditary classes of ordered graphs, generalizing the Stanley-Wilf conjecture/Marcus-Tardos theorem. Second, it gives a fixed-parameter approximation algorithm for twin-width on ordered graphs. Third, it yields a full classification of fixed-parameter tractable first-order model checking on hereditary classes of ordered binary structures. Fourth, it provides a model-theoretic characterization of classes with bounded twin-width. Finally, it settles our small conjecture [SODA ’21] in the case of ordered graphs.
Geometrical sets with forbidden configurations

Abstract Given finite configurations $P_1, \dots , P_n \subset \mathbb {R}^d$ , let us denote by $\mathbf {m}_{\mathbb {R}^d}(P_1, \dots , P_n)$ the maximum density a set $A \subseteq \mathbb {R}^d$ can have without containing congruent copies of any $P_i$ . We will initiate the study of this geometrical parameter, called the independence density of the considered configurations, and give several results we believe are interesting. For instance we show that, under suitable size and nondegeneracy conditions, $\mathbf {m}_{\mathbb {R}^d}(t_1 P_1, t_2 P_2, \dots , t_n P_n)$ progressively ‘untangles’ and tends to $\prod _{i=1}^n \mathbf {m}_{\mathbb {R}^d}(P_i)$ as the ratios $t_{i+1}/t_i$ between consecutive dilation parameters grow large; this shows an exponential decay on the density when forbidding multiple dilates of a given configuration, and gives a common generalization of theorems by Bourgain and by Bukh in geometric Ramsey theory. We also consider the analogous parameter $\mathbf {m}_{\mathbb {S}^d}(P_1, \dots , P_n)$ in the more complicated framework of sets on the unit sphere $\mathbb {S}^d$ , obtaining the corresponding results in this setting.
Generalized Outer Bounds on the Finite Geometric Sum of Ellipsoids

General results on convex bodies are reviewed and used to derive an exact closed-form parametric formula for the boundary of the geometric (Minkowski) sum of $k$ ellipsoids in $n$-dimensional Euclidean space. Previously this was done through iterative algorithms in which each new ellipsoid was added to an ellipsoid approximation of the sum of the previous ellipsoids. Here we provide one shot formulas to add $k$ ellipsoids directly with no intermediate approximations required. This allows us to observe a new degree of freedom in the family of ellipsoidal bounds on the geometric sum. We demonstrate an application of these tools to compute the reachable set of a discrete-time dynamical system.
DSTATCOM with LCL Filter to Improve Voltage Sags and Current Harmonics in Power Distribution System

An increasing demand for high quality, reliable electrical power and increasing number of distorting loads may leads to an increased awareness of power quality both by customers and utilities. The most common power quality problems today are voltage sags, harmonic distortion and low power factor. This paper presents the improvement of voltage sags, harmonic distortion and low power factor using Distribution Static Compensator (D-STATCOM) with LCL Passive Filter in power distribution network. The model is based on the Voltage Source Converter (VSC) principle. The DSTATCOM injects a current into the system to mitigate the voltage sags. a LCL( inductor-capacitor-inductor ) Passive Filter with pulse width modulation (PWM) was then added to D-STATCOM to improve current harmonic distortion. The simulations were performed using MATLAB SIMULINK version R2009b.
Low Temperature Synthesis of Ultrafine Al2TiO5 Powders by Hydrolytic Sol-Gel Method

Aluminum titanate (AT) ultrafine powder with high productivity ratio was prepared via hydrolytic sol-gel (HSG) method at 1250 °C, using aluminum nitrate (Al (NO3)3·9H2O) and titanium tetrachloride (TiCl4) as precursors, ethanol as solvent through solvothermal treatment process. Water required for hydrolysis reaction was supplied by the crystal water of aluminum nitrate itself. The effect of process parameters including filling ratio, solvothermal temperature and soaking time on the synthesis of AT powder was investigated by means of X-ray diffraction (XRD) and scanning electron microscopy (SEM). The cause of low temperature synthesis of AT was investigated. The result showed that excessive or insufficient filling ratio, too high or low solvothermal temperature, too long soaking time was not beneficial for the preparation of AT. The optimized ultrafine AT powder appeared at the decomposition temperature of 1250 °C with average particle size less than 1μm at a relatively high productivity ratio of 98 % with filling ratios of 60 %, solvothermal temperatures of 130 °C and soaking time of 30 min. The refinement of particles in the AT xerogel through solvothermal treatment process, and increasing reactivity of precursor, plays the key role in the low temperature synthesis of AT.
International Journal of Instruction

The present paper describes the development and validation of a new instrument designed to explore English language teaching (ELT) student teachers’ beliefs about Intercultural Communicative Competence (ICC) in an expanding-circle context, Turkey. The process of instrument development and validation is described along with a concise literature review of ICC. Forty-eight items were prepared based on a comprehensive review of literature. 34‐item instrument was developed following two phases: First, an exploratory factor analysis of the responses of a large sample (N=399) of ELT student teachers, and then a subsequent confirmatory factor analysis with another large but different sample (N=378) were performed. The Kaiser-Meyer Olkin (KMO) coefficient was found as 0.90 and the Bartlett test result (ChiSquare 3613.58; p<0.01) was found significant. The results of the analysis indicated that the inventory has a perfect reliability value with . 925, resulting in 34 items. The four factors that emerged from the exploratory factor analysis are attitudes, knowledge, awareness and skills. The result of confirmatory factor analysis indicated that GFI = 0.93, AGFI = 0.73, CFI = 0.77, NNFI = 0.93, RMSEA (90% CI) = 0.076 (0.041-0.086) and SRMR = 0.046. Based on these findings, the study provides suggestions with regard to the potential uses of the Beliefs about Intercultural Communicative Competence Inventory (BICCI) in second language teacher education.
International Journal of Instruction

This quasi-experimental research aimed at investigating the effect of ProblemOriented Project-Based Learning (POPBL), Student Team Achievement Division (STAD), POPBLSTAD integration, and conventional learning on students' critical and creative thinking skills. Essay tests conducted to 129 students. The results of the test assessed using a relevant rubric. Posthoc analyses, namely LSD and effect size analyses, were used to analyze the data. The results of the ANCOVA analysis show that the learning model had a significant effect on students' critical thinking skills as well as on their creative thinking skills. The LSD analysis result showed that POPBLSTAD integration achieved a higher score of critical thinking compared to STAD, POPBL, and conventional learning (21.22%, 16.11%, and 39.38%, respectively). Likewise, related to creative thinking, the POPBLSTAD integration score was 25.91%, 18.57%, and 53.29% higher than those of STAD, POPBL, and conventional learning, respectively. Besides, all effect size analyses showed that POPBLSTAD obtained a higher effect-size score in critical thinking (high criteria) compared to STAD, POPBL, and conventional learning (0.68, 0.65, and 0.82, respectively). Similarly, the POPBLSTAD integration achieved a higher effect-size score of creative thinking (high criteria) compared to STAD, POPBL, and conventional learning (0.66, 0.55, and 0.83, respectively). Both analyses have demonstrated similar interpretations; thus, the two types of analyses should simultaneously be used to obtain complete research information.
International Journal of Instruction

Citation: Martínez-Valdivia, E. , Pegalajar-Palomino, Ma. C. , & Higueras-Rodríguez, M. L. (2021). Dialogic literary circles as a methodological strategy for the training of students in early childhood education degree. International Journal of Instruction, 14(3), 255-270. https://doi.org/10.29333/iji.2021. 14315a Article submission code: 20200710161356 Received: 10/07/2020 Revision: 11/12/2020 Accepted: 04/01/2021 OnlineFirst: 06/05/2021
Hot-water prehydrolysis of willow wood.

A pretreatment of various renewable lignocellulosic sources is very important step when producing pulp, bioethanol and other technically important chemicals. Hot water prehydrolysis of willow wood chips (Salix alba L., 15 years old) at the temperature of 180 °C in the time range of 30-240 min has been carried out. The maximum sugar concentration was found in the solution at 60 min. Concentration of acetic acid and 2furaldehyde in hydrolysates increased at first, but after 120 min decreased. Their concentrations were approximately twelve times higher than concentrations of methanol and propionic acid.