Miracle-dz/UniFAR-FTUs huggingface dataset:
A Greed(y) Training Strategy to Attract High School Girls to Undertake Studies in ICT

It has been observed in many studies that female students in general are unwilling to undertake a course of study in ICT. Recent literature has also pointed out that undermining the prejudices of girls with respect to these disciplines is very difficult in adolescence, suggesting that, to be effective, awareness programs on computer disciplines should be offered in pre-school or lower school age. On the other hand, even assuming that large-scale computer literacy programs can be immediately activated in lower schools and kindergartens, we can't wait for>15-20 years before we can appreciate the effectiveness of these programs. The scarcity of women in ICT has a tangible negative impact on countries' technological innovation, which requires immediate action. In this paper, we describe a strategy, and the details of a number of programs coordinated by the Engineering and Computer Science Departments at Sapienza University, to make high school girl students aware of the importance of new technologies and ICT. In addition to describing the theoretical approach, the paper offers some project examples.
The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment

Increased focus on the computational efficiency of NLP systems has motivated the design of efficient model architectures and improvements to underlying hardware accelerators. However, the resulting increases in computational throughput and reductions in floating point operations have not directly translated to improvements in wall-clock inference latency. We demonstrate that these discrepancies can be largely attributed to bottlenecks introduced by deep learning frameworks. We denote this phenomenon as the \textit{framework tax}, and observe that the disparity is growing as hardware speed increases over time. In this work, we examine this phenomenon through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency. Code is available at https://github.com/JaredFern/Framework-Tax.
Ordinary muon capture rates on $^{100}$Mo and $^{\rm nat}$Mo for astro-antineutrinos and double beta decays

\item[Background] The nuclear responses for antineutrinos associated with double beta decays (DBDs) and astro-antineutrino interactions are studied by measuring ordinary muon capture (OMC) rates. \item[Purpose]The experimental studies of absolute OMC rates and their mass number dependence for $^{100}$Mo and the natural Mo are currently of interest in astro-antineutrinos and DBDs. \item[Method]The OMC rates were obtained experimentally by measuring the time spectrum of the trapped muon's decay into electrons to obtain the half-lives of the trapped muons. \item[Results]The OMC rate for the enriched isotope of $^{100}$Mo is $\Lambda$($^{100}$Mo)=(7.07$\pm$0.32)$\times10^{6}$ s$^{-1}$, while that for the natural Mo is $\Lambda$($^{\rm nat}$Mo)=(9.66$\pm$0.44)$\times10^{6}$ s$^{-1}$, i.e., $\Lambda$($^{100}$Mo) is about 27$\%$ of $\Lambda$($^{\rm nat}$Mo), reflecting the blocking effect of the excess neutrons for the proton-to-neutron transformation in OMC. The present experimental observation is consistent with the predictions using Goulard-Primakoff's (GPs) and Primakoff's (Ps) empirical equations. \item[Conclusions] The absolute OMC rates for $^{100}$Mo and $^{\rm nat}$Mo were measured. The large neutron excess in $^{100}$Mo gives a much lower OMC rate than $^{\rm nat}$Mo. On both $^{100}$Mo and $^{\rm nat}$Mo, consistent OMC rates with the GP and P values are observed.
Two hard meta-problems at the core of the inﬁnite-domain CSP dichotomy conjecture

The inﬁnite-domain CSP dichotomy conjecture extends the ﬁnite-domain CSP dichotomy theorem to reducts of ﬁnitely bounded homogeneous structures. Every such structure is uniquely described by a particular sentence of the logic SNP. We show that the question whether a given SNP sentence describes a structure within the scope of the conjecture is undecidable even if the sentence comes from the connected Datalog fragment, and that the closely related problem of testing the amalgamation property for universal sentences is EXPSPACE-hard. We also discuss some philosophical implications of these results for the inﬁnite-domain CSP dichotomy conjecture
Self-supervised pseudo-colorizing of masked cells

Self-supervised learning, which is strikingly referred to as the dark matter of intelligence, is gaining more attention in biomedical applications of deep learning. In this work, we introduce a novel self-supervision objective for the analysis of cells in biomedical microscopy images. We propose training deep learning models to pseudo-colorize masked cells. We use a physics-informed pseudo-spectral colormap that is well suited for colorizing cell topology. Our experiments reveal that approximating semantic segmentation by pseudo-colorization is beneficial for subsequent fine-tuning on cell detection. Inspired by the recent success of masked image modeling, we additionally mask out cell parts and train to reconstruct these parts to further enrich the learned representations. We compare our pre-training method with self-supervised frameworks including contrastive learning (SimCLR), masked autoencoders (MAEs), and edge-based self-supervision. We build upon our previous work and train hybrid models for cell detection, which contain both convolutional and vision transformer modules. Our pre-training method can outperform SimCLR, MAE-like masked image modeling, and edge-based self-supervision when pre-training on a diverse set of six fluorescence microscopy datasets. Code is available at: https://github.com/roydenwa/pseudo-colorize-masked-cells.
Engineering a Preprocessor for Symmetry Detection

State-of-the-art solvers for symmetry detection in combinatorial objects are becoming increasingly sophisticated software libraries. Most of the solvers were initially designed with inputs from combinatorics in mind (nauty, bliss, Traces, dejavu). They excel at dealing with a complicated core of the input. Others focus on practical instances that exhibit sparsity. They excel at dealing with comparatively easy but extremely large substructures of the input (saucy). In practice, these differences manifest in significantly diverging performances on different types of graph classes. We engineer a preprocessor for symmetry detection. The result is a tool designed to shrink sparse, large substructures of the input graph. On most of the practical instances, the overall running time improves significantly for many of the state-of-the-art solvers. At the same time, our benchmarks show that the additional overhead is negligible. Overall we obtain single algorithms with competitive performance across all benchmark graphs. As such the preprocessor bridges the disparity between solvers that focus on combinatorial graphs and large practical graphs. In fact, on most of the practical instances the combined setup significantly outperforms previous state-of-the-art.
Task-Specific Skill Localization in Fine-tuned Language Models

Pre-trained language models can be fine-tuned to solve diverse NLP tasks, including in few-shot settings. Thus fine-tuning allows the model to quickly pick up task-specific ``skills,'' but there has been limited study of where these newly-learnt skills reside inside the massive model. This paper introduces the term skill localization for this problem and proposes a solution. Given the downstream task and a model fine-tuned on that task, a simple optimization is used to identify a very small subset of parameters ($\sim0.01$% of model parameters) responsible for ($>95$%) of the model's performance, in the sense that grafting the fine-tuned values for just this tiny subset onto the pre-trained model gives performance almost as well as the fine-tuned model. While reminiscent of recent works on parameter-efficient fine-tuning, the novel aspects here are that: (i) No further re-training is needed on the subset (unlike, say, with lottery tickets). (ii) Notable improvements are seen over vanilla fine-tuning with respect to calibration of predictions in-distribution ($40$-$90$% error reduction) as well as the quality of predictions out-of-distribution (OOD). In models trained on multiple tasks, a stronger notion of skill localization is observed, where the sparse regions corresponding to different tasks are almost disjoint, and their overlap (when it happens) is a proxy for task similarity. Experiments suggest that localization via grafting can assist certain forms of continual learning.
Optical Stern–Gerlach effect via a single traveling-wave light

In this paper, we propose a simplified model of the optical Stern–Gerlach effect based on coherent coupling between the clock transition of alkaline-earth single atoms and a traveling-wave light. It is demonstrated that spin–orbit coupling-induced chiral motion in atom deflection appears under strong atom–light interaction. The strong optical driving removes the perturbation from the Doppler effect and back-action effect to access the coherent system. In this process, the superposition of distant matter waves connected to the arbitrary distribution of the atom internal state could be predicted, which is important for the realization of atom interferometry and quantum-state operation. The influence of atom relaxation and atom–atom interactions is discussed. The basic conditions for the experimental design are given at the end of this work.
How to Use Dropout Correctly on Residual Networks with Batch Normalization

For the stable optimization of deep neural networks, regularization methods such as dropout and batch normalization have been used in various tasks. Nevertheless, the correct position to apply dropout has rarely been discussed, and different positions have been employed depending on the practitioners. In this study, we investigate the correct position to apply dropout. We demonstrate that for a residual network with batch normalization, applying dropout at certain positions increases the performance, whereas applying dropout at other positions decreases the performance. Based on theoretical analysis, we provide the following guideline for the correct position to apply dropout: apply one dropout after the last batch normalization but before the last weight layer in the residual branch. We provide detailed theoretical explanations to support this claim and demonstrate them through module tests. In addition, we investigate the correct position of dropout in the head that produces the final prediction. Although the current consensus is to apply dropout after global average pooling, we prove that applying dropout before global average pooling leads to a more stable output. The proposed guidelines are validated through experiments using different datasets and models.
Exploring Navigation Maps for Learning-Based Motion Prediction

The prediction of surrounding agents' motion is a key for safe autonomous driving. In this paper, we explore navigation maps as an alternative to the predominant High Definition (HD) maps for learning-based motion prediction. Navigation maps provide topological and geometrical information on road-level, HD maps additionally have centimeter-accurate lane-level information. As a result, HD maps are costly and time-consuming to obtain, while navigation maps with near-global coverage are freely available. We describe an approach to integrate navigation maps into learning-based motion prediction models. To exploit locally available HD maps during training, we additionally propose a model-agnostic method for knowledge distillation. In experiments on the publicly available Argoverse dataset with navigation maps obtained from OpenStreetMap, our approach shows a significant improvement over not using a map at all. Combined with our method for knowledge distillation, we achieve results that are close to the original HD map-reliant models. Our publicly available navigation map API for Argoverse enables researchers to develop and evaluate their own approaches using navigation maps4.
On the minimum modulus problem in number fields

The minimum modulus problem on covering systems was posed by Erd\H{o}s in 1950, who asked whether the minimum modulus of a covering system with distinct moduli is bounded. In 2007, Filaseta, Ford, Konyagin, Pomerance and Yu affirmed it if the reciprocal sum of the moduli of a covering system is bounded. Later in 2015, Hough resolved this problem by showing that the minimum modulus is at most $10^{16}$. In 2022, Balister, Bollob\'as, Morris, Sahasrabudhe and Tiba reduced this bound to $616,000$ by developing a versatile method called the distortion method. Recently, Klein, Koukoulopoulos and Lemieux generalized Hough's result by using this method. In this paper, we develop the distortion method by introducing the theory of probability measures associated to an inverse system. Following Klein et al.'s work, we derive an analogue of their theorem in the setting of number fields, which provides a solution to Erd\H{o}s' minimum modulus problem in number fields.
An Algebraic Discussion of Bisexual Populations with Wolbachia Infection I: Discrete Dynamical System Approach

This is the first paper in the sequel studying the Wolbachia-infection in bisexual populations. This paper considers the behavior of the population as a discrete dynamical system. The recurrence relation is obtained as a function of the initial infected male/female frequencies and the cytoplasmic incompatibility of the population. The experimental data from Wolbachia-infected terrestrial isopod populations and the model proposed in Wolbachia-infected mosquitoes from literature is compared with the discrete dynamical system achieved.
Cross-sectional profile of photocarrier mobility in thin-film solar cells via multimolecular recombination and charge extraction by linearly increasing voltage (cs-p-CELIV)

The ability to spatially resolve the carrier mobility profile along the cross section of micrometer-thin solar cells is vital, both for fundamental studies in photovoltaics and as quality control for reproducibly obtaining high conversion efficiencies in commercial solar cell modules. Presently, no technique capable of such an endeavor is available to the best of our knowledge. Here, we introduce a novel method capable of profiling the carrier mobility along the z axis in thin-film photovoltaics. Our setup is based on the integration of photogenerated charge extraction by linearly increasing voltage (p-CELIV) with a scanning confocal optical microscope (SCOM) toward a confocal and cross-sectional p-CELIV (cs-p-CELIV) system. As monomolecular recombination of excess carriers is the most frequent radiative pathway for electrons and holes in solar cells at low power density of illumination, while multimolecular recombination dominates at high power, enhanced multimolecular recombination occurs at the SCOM focal plane. Thus, the cs-p-CELIV signal provides enhanced information on the mobility of all of the cross-sectional layers except the focal plane. By scanning the focal plane along the z axis, the mobility profile can be derived. To demonstrate our technique, we use it to investigate the carrier mobility in three hydrogenated amorphous silicon (a-Si:H) solar cells. The mobility profiles obtained by cs-p-CELIV correlate well with well-known depletion layer effects and the H content profile in a-Si:H, which is measured independently. Our findings are in excellent agreement with models suggesting a critical role of Si–H bonding in locally determining the carrier mobility in a-Si:H.
Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD

This work considers the problem of finding a first-order stationary point of a non-convex function with potentially unbounded smoothness constant using a stochastic gradient oracle. We focus on the class of $(L_0,L_1)$-smooth functions proposed by Zhang et al. (ICLR'20). Empirical evidence suggests that these functions more closely captures practical machine learning problems as compared to the pervasive $L_0$-smoothness. This class is rich enough to include highly non-smooth functions, such as $\exp(L_1 x)$ which is $(0,\mathcal{O}(L_1))$-smooth. Despite the richness, an emerging line of works achieves the $\widetilde{\mathcal{O}}(\frac{1}{\sqrt{T}})$ rate of convergence when the noise of the stochastic gradients is deterministically and uniformly bounded. This noise restriction is not required in the $L_0$-smooth setting, and in many practical settings is either not satisfied, or results in weaker convergence rates with respect to the noise scaling of the convergence rate. We develop a technique that allows us to prove $\mathcal{O}(\frac{\mathrm{poly}\log(T)}{\sqrt{T}})$ convergence rates for $(L_0,L_1)$-smooth functions without assuming uniform bounds on the noise support. The key innovation behind our results is a carefully constructed stopping time $\tau$ which is simultaneously"large"on average, yet also allows us to treat the adaptive step sizes before $\tau$ as (roughly) independent of the gradients. For general $(L_0,L_1)$-smooth functions, our analysis requires the mild restriction that the multiplicative noise parameter $\sigma_1<1$. For a broad subclass of $(L_0,L_1)$-smooth functions, our convergence rate continues to hold when $\sigma_1 \geq 1$. By contrast, we prove that many algorithms analyzed by prior works on $(L_0,L_1)$-smooth optimization diverge with constant probability even for smooth and strongly-convex functions when $\sigma_1>1$.
A Theoretical Understanding of shallow Vision Transformers: Learning, Generalization, and Sample Complexity

Vision Transformers (ViTs) with self-attention modules have recently achieved great empirical success in many vision tasks. Due to non-convex interactions across layers, however, theoretical learning and generalization analysis is mostly elusive. Based on a data model characterizing both label-relevant and label-irrelevant tokens, this paper provides the first theoretical analysis of training a shallow ViT, i.e., one self-attention layer followed by a two-layer perceptron, for a classification task. We characterize the sample complexity to achieve a zero generalization error. Our sample complexity bound is positively correlated with the inverse of the fraction of label-relevant tokens, the token noise level, and the initial model error. We also prove that a training process using stochastic gradient descent (SGD) leads to a sparse attention map, which is a formal verification of the general intuition about the success of attention. Moreover, this paper indicates that a proper token sparsification can improve the test performance by removing label-irrelevant and/or noisy tokens, including spurious correlations. Empirical experiments on synthetic data and CIFAR-10 dataset justify our theoretical results and generalize to deeper ViTs.
Strong Bounds for 3-Progressions

We show that for some constant $\beta\gt0$, any subset A of integers $\{1, \ldots, N\}$ of size at least $2^{-O\left((\log N)^{\beta}\right)} \cdot N$ contains a non-trivial three-term arithmetic progression. Previously, three-term arithmetic progressions were known to exist only for sets of size at least $N /(\log N)^{1+c}$ for a constant $c\gt0$. Our approach is first to develop new analytic techniques for addressing some related questions in the finite-field setting and then to apply some analogous variants of these same techniques, suitably adapted for the more complicated setting of integers.
SelectionBias: An R Package for Bounding Selection Bias

Selection bias can occur when subjects are included or excluded in the analysis based upon some selection criteria for the study population. The bias can jeopardize the validity of the study and sensitivity analyses for assessing the effect of the selection are desired. One method of sensitivity analysis is to construct bounds for the bias. In this work, we present an R package that can be used to calculate two previously proposed bounds for selection bias for the causal relative risk and causal risk difference for both the total and the selected population. The first bound, derived by Smith and VanderWeele (SV), is based on values of sensitivity parameters that describe parts of the joint distribution of the outcome, treatment, selection indicator and unobserved variables. The second bound is based solely on the observed data, and is therefore referred to as an assumption free (AF) bound. In addition to a tutorial for the bounds and the R package, we derive additional properties for the SV bound. We show that the sensitivity parameters are variation independent and derive feasible regions for them. Furthermore, a bound is sharp if it is a priori known that the bias can be equal to the value of the bound, given the values of the selected sensitivity parameters. Conditions for the SV bound to be sharp in the selected subpopulation are provided based on the observed data. We illustrate both the R package and the properties of the bound with a simulated dataset that emulates a study where the effect of the zika virus on microcephaly in Brazil is investigated.
Confidence and Uncertainty Assessment for Distributional Random Forests

The Distributional Random Forest (DRF) is a recently introduced Random Forest algorithm to estimate multivariate conditional distributions. Due to its general estimation procedure, it can be employed to estimate a wide range of targets such as conditional average treatment effects, conditional quantiles, and conditional correlations. However, only results about the consistency and convergence rate of the DRF prediction are available so far. We characterize the asymptotic distribution of DRF and develop a bootstrap approximation of it. This allows us to derive inferential tools for quantifying standard errors and the construction of confidence regions that have asymptotic coverage guarantees. In simulation studies, we empirically validate the developed theory for inference of low-dimensional targets and for testing distributional differences between two populations.
Data-Driven Inverse Optimization for Offer Price Recovery

This paper presents a data-driven inverse optimization (IO) approach to recover the marginal offer prices of generators in a wholesale energy market. By leveraging underlying market-clearing pro-cesses, we establish a closed-form relationship between the unknown parameters and the publicly available market-clearing results. Based on this relationship, we formulate the data-driven IO problem as a computationally feasible single-level optimization problem. The solution of the data-driven model is based on the gradient descent method, which provides an error bound on the optimal solution and a sub-linear convergence rate. We also rigorously prove the existence and uniqueness of the global optimum to the proposed data-driven IO problem and analyze its robustness in two possible noisy settings. The effectiveness of the proposed method is demonstrated through simulations in both an illustrative IEEE 14-bus system and a realistic NYISO 1814-bus system.
Relaxed N-Pairs Loss for Context-Aware Recommendations of Television Content

This paper studies context-aware recommendations in the television domain by proposing a deep learning-based method for learning joint context-content embeddings (JCCE). The method builds on recent developments within recommendations using latent representations and deep metric learning, in order to effectively represent contextual settings of viewing situations as well as available content in a shared latent space. This embedding space is used for exploring relevant content in various viewing settings by applying an N-pairs loss objective as well as a relaxed variant proposed in this paper. Experiments confirm the recommendation ability of JCCE, achieving improvements when compared to state-of-the-art methods. Further experiments display useful structures in the learned embeddings that can be used for gaining valuable knowledge of underlying variables in the relationship between contextual settings and content properties.