Miracle-dz/UniFAR-FTUs huggingface dataset:
TorchQL: A Programming Framework for Integrity Constraints in Machine Learning

Finding errors in machine learning applications requires a thorough exploration of their behavior over data. Existing approaches used by practitioners are often ad-hoc and lack the abstractions needed to scale this process. We present TorchQL, a programming framework to evaluate and improve the correctness of machine learning applications. TorchQL allows users to write queries to specify and check integrity constraints over machine learning models and datasets. It seamlessly integrates relational algebra with functional programming to allow for highly expressive queries using only eight intuitive operators. We evaluate TorchQL on diverse use-cases including finding critical temporal inconsistencies in objects detected across video frames in autonomous driving, finding data imputation errors in time-series medical records, finding data labeling errors in real-world images, and evaluating biases and constraining outputs of language models. Our experiments show that TorchQL enables up to 13x faster query executions than baselines like Pandas and MongoDB, and up to 40% shorter queries than native Python. We also conduct a user study and find that TorchQL is natural enough for developers familiar with Python to specify complex integrity constraints.
Bilingual Streaming ASR with Grapheme units and Auxiliary Monolingual Loss

We introduce a bilingual solution to support English as secondary locale for most primary locales in hybrid automatic speech recognition (ASR) settings. Our key developments constitute: (a) pronunciation lexicon with grapheme units instead of phone units, (b) a fully bilingual alignment model and subsequently bilingual streaming transformer model, (c) a parallel encoder structure with language identification (LID) loss, (d) parallel encoder with an auxiliary loss for monolingual projections. We conclude that in comparison to LID loss, our proposed auxiliary loss is superior in specializing the parallel encoders to respective monolingual locales, and that contributes to stronger bilingual learning. We evaluate our work on large-scale training and test tasks for bilingual Spanish (ES) and bilingual Italian (IT) applications. Our bilingual models demonstrate strong English code-mixing capability. In particular, the bilingual IT model improves the word error rate (WER) for a code-mix IT task from 46.5% to 13.8%, while also achieving a close parity (9.6%) with the monolingual IT model (9.5%) over IT tests.
Spontaneous crumpling of active spherical shells

The existence of a crumpled phase for self-avoiding elastic surfaces was postulated more than three decades ago using simple Flory-like scaling arguments. Despite much effort, its stability in a microscopic environment has been the subject of much debate. In this Letter we show how a crumpled phase develops reliably and consistently upon subjecting a thin spherical shell to active fluctuations. We find a master curve describing how the relative volume of a shell changes with the strength of the active forces, that applies for every shell independent of size and elastic constants. Furthermore, we extract a general expression for the onset active force beyond which a shell begins to crumple. Finally, we calculate how the size exponent varies along the crumpling curve.
S3C2 Summit 2023-06: Government Secure Supply Chain Summit

Recent years have shown increased cyber attacks targeting less secure elements in the software supply chain and causing fatal damage to businesses and organizations. Past well-known examples of software supply chain attacks are the SolarWinds or log4j incidents that have affected thousands of customers and businesses. The US government and industry are equally interested in enhancing software supply chain security. On June 7, 2023, researchers from the NSF-supported Secure Software Supply Chain Center (S3C2) conducted a Secure Software Supply Chain Summit with a diverse set of 17 practitioners from 13 government agencies. The goal of the Summit was two-fold: (1) to share our observations from our previous two summits with industry, and (2) to enable sharing between individuals at the government agencies regarding practical experiences and challenges with software supply chain security. For each discussion topic, we presented our observations and take-aways from the industry summits to spur conversation. We specifically focused on the Executive Order 14028, software bill of materials (SBOMs), choosing new dependencies, provenance and self-attestation, and large language models. The open discussions enabled mutual sharing and shed light on common challenges that government agencies see as impacting government and industry practitioners when securing their software supply chain. In this paper, we provide a summary of the Summit.
DIVAS: An LLM-based End-to-End Framework for SoC Security Analysis and Policy-based Protection

Securing critical assets in a bus-based System-On-Chip (SoC) is imperative to mitigate potential vulnerabilities and prevent unauthorized access, ensuring the integrity, availability, and confidentiality of the system. Ensuring security throughout the SoC design process is a formidable task owing to the inherent intricacies in SoC designs and the dispersion of assets across diverse IPs. Large Language Models (LLMs), exemplified by ChatGPT (OpenAI) and BARD (Google), have showcased remarkable proficiency across various domains, including security vulnerability detection and prevention in SoC designs. In this work, we propose DIVAS, a novel framework that leverages the knowledge base of LLMs to identify security vulnerabilities from user-defined SoC specifications, map them to the relevant Common Weakness Enumerations (CWEs), followed by the generation of equivalent assertions, and employ security measures through enforcement of security policies. The proposed framework is implemented using multiple ChatGPT and BARD models, and their performance was analyzed while generating relevant CWEs from the SoC specifications provided. The experimental results obtained from open-source SoC benchmarks demonstrate the efficacy of our proposed framework.
A simple construction of Entanglement Witnesses for arbitrary and different dimensions

We present a simple approach for generation of a diverse set of positive maps between spaces of different dimensions. The proposed method enables the construction of Entanglement Witnesses tailored for systems in $d_1 \times d_2$ dimensions. With this method, it is possible to construct Entanglement Witnesses that consist solely of a chosen set of desired measurements. We demonstrate the effectiveness and generality of our approach using concrete examples. It is also demonstrated in two examples, how an appropriate entanglement witness can be identified for witnessing the entanglement of a given state, including a case when the given state is a Positive Partial Transpose (PPT) entangled state.
3D Printing Magnetophoretic Displays

We present a pipeline for printing interactive and always-on magnetophoretic displays using affordable Fused Deposition Modeling (FDM) 3D printers. Using our pipeline, an end-user can convert the surface of a 3D shape into a matrix of voxels. The generated model can be sent to an FDM 3D printer equipped with an additional syringe-based injector. During the printing process, an oil and iron powder-based liquid mixture is injected into each voxel cell, allowing the appearance of the once-printed object to be editable with external magnetic sources. To achieve this, we made modifications to the 3D printer hardware and the firmware. We also developed a 3D editor to prepare printable models. We demonstrate our pipeline with a variety of examples, including a printed Stanford bunny with customizable appearances, a small espresso mug that can be used as a post-it note surface, a board game figurine with a computationally updated display, and a collection of flexible wearable accessories with editable visuals.
Polar discontinuities and interfacial electronic properties of Bi$_2$O$_2$Se on SrTiO$_3$

The layered oxychalcogenide semiconductor Bi$_2$O$_2$Se (BOS) hosts a multitude of unusual properties including high electron mobility. Owing to similar crystal symmetry and lattice constants, the perovskite oxide SrTiO$_3$ (STO) has been demonstrated to be an excellent substrate for wafer-scale growth of atomically thin BOS films. However, the structural and electronic properties of the BOS/STO interface remain poorly understood. Here, through first-principles study, we reveal that polar discontinuities and interfacial contact configurations have a strong impact on the electronic properties of ideal BOS/STO interfaces. The lowest-energy [Bi-TiO$_2$] contact type, which features the contact between a Bi$_2$O$_2$ layer of BOS with the TiO$_2$-terminated surface of STO, incurs significant interfacial charge transfer from BOS to STO, producing a BOS/STO-mixed, $n$-type metallic state at the interface. By contrast, the [Se-SrO] contact type, which is the most stable contact configuration between BOS and SrO-terminated STO substrate, has a much smaller interfacial charge transfer from STO to BOS and exhibits $p$-type electronic structure with much weaker interfacial hybridization between BOS and STO. These results indicate that BOS grown on TiO$_2$-terminated STO substrates could be a fruitful system for exploring emergent phenomena at the interface between an oxychalcogenide and an oxide, whereas BOS grown on SrO-terminated substrates may be more advantageous for preserving the excellent intrinsic transport properties of BOS.
Human Voice Pitch Estimation: A Convolutional Network with Auto-Labeled and Synthetic Data

In the domain of music and sound processing, pitch extraction plays a pivotal role. Our research presents a specialized convolutional neural network designed for pitch extraction, particularly from the human singing voice in acapella performances. Notably, our approach combines synthetic data with auto-labeled acapella sung audio, creating a robust training environment. Evaluation across datasets comprising synthetic sounds, opera recordings, and time-stretched vowels demonstrates its efficacy. This work paves the way for enhanced pitch extraction in both music and voice settings.
Wideband Spectrum Acquisition for UAV Swarm Using the Sparse Coding Fourier Transform

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition for the UAV swarms is studied by proposing a processing method that features lower power consumption, higher compression rates, and a lower signal-to-noise ratio. Our system is equipped with multiple UAVs, each with a different sub-sampling rate. That allows for frequency backetization and estimation based on sparse Fourier transform theory. Unlike other techniques, the collisions and iterations caused by non-sparsity environ-ments are considered. We introduce sparse coding Fourier transform to address these issues. The key is to code the entire spectrum and decode it through spectrum correlation in the code. Simulation results show that our proposed method performs well in acquiring both narrowband and wideband signals simultaneously, compared to the other methods.
Fault Tolerance in Euclidean Committee Selection

In the committee selection problem, the goal is to choose a subset of size $k$ from a set of candidates $C$ that collectively gives the best representation to a set of voters. We consider this problem in Euclidean $d$-space where each voter/candidate is a point and voters' preferences are implicitly represented by Euclidean distances to candidates. We explore fault-tolerance in committee selection and study the following three variants: (1) given a committee and a set of $f$ failing candidates, find their optimal replacement; (2) compute the worst-case replacement score for a given committee under failure of $f$ candidates; and (3) design a committee with the best replacement score under worst-case failures. The score of a committee is determined using the well-known (min-max) Chamberlin-Courant rule: minimize the maximum distance between any voter and its closest candidate in the committee. Our main results include the following: (1) in one dimension, all three problems can be solved in polynomial time; (2) in dimension $d \geq 2$, all three problems are NP-hard; and (3) all three problems admit a constant-factor approximation in any fixed dimension, and the optimal committee problem has an FPT bicriterion approximation.
Locally repairable convertible codes with optimal access costs

Modern large-scale distributed storage systems use erasure codes to protect against node failures with low storage overhead. In practice, the failure rate and other factors of storage devices in the system may vary significantly over time, and leads to changes of the ideal code parameters. To maintain the storage efficiency, this requires the system to adjust parameters of the currently used codes. The changing process of code parameters on encoded data is called code conversion. As an important class of storage codes, locally repairable codes (LRCs) can repair any codeword symbol using a small number of other symbols. This feature makes LRCs highly efficient for addressing single node failures in the storage systems. In this paper, we investigate the code conversions for locally repairable codes in the merge regime. We establish a lower bound on the access cost of code conversion for general LRCs and propose a general construction of LRCs that can perform code conversions with access cost matching this bound. This construction provides a family of LRCs together with optimal conversion process over the field of size linear in the code length.
Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos

To ensure the safe and efficient navigation of autonomous vehicles and advanced driving assistance systems in complex traffic scenarios, predicting the future bounding boxes of surrounding traffic agents is crucial. However, simultaneously predicting the future location and scale of target traffic agents from the egocentric view poses challenges because the vehicles’ egomotion causes considerable field-of-view changes. Moreover, in anomalous or risky situations, tracking loss or abrupt motion changes limit the available observation time, requiring learning of cues within a short time window. Existing methods typically use a simple concatenation operation to combine different cues, overlooking their dynamics over time. To address this, this paper introduces the Fusion-Gated Recurrent Unit (Fusion-GRU) network, a novel encoder-decoder architecture for future bounding box localization. Unlike traditional gated recurrent units (GRUs), Fusion-GRU accounts for mutual and complex interactions among input features. Moreover, an intermediary estimator coupled with a self-attention aggregation layer is also introduced to learn sequential dependencies for long-range prediction. Finally, a GRU decoder is employed to predict the future bounding boxes. The proposed method is evaluated on two publicly available datasets, Risky Object Localization and Honda Egocentric Vehicle Intersection. The experimental results showcase the promising performance of the Fusion-GRU, demonstrating its effectiveness in predicting future bounding boxes of traffic agents.
Bridging Offline-Online Evaluation with a Time-dependent and Popularity Bias-free Offline Metric for Recommenders

The evaluation of recommendation systems is a complex task. The offline and online evaluation metrics for recommender systems are ambiguous in their true objectives. The majority of recently published papers benchmark their methods using ill-posed offline evaluation methodology that often fails to predict true online performance. Because of this, the impact that academic research has on the industry is reduced. The aim of our research is to investigate and compare the online performance of offline evaluation metrics. We show that penalizing popular items and considering the time of transactions during the evaluation significantly improves our ability to choose the best recommendation model for a live recommender system. Our results, averaged over five large-size real-world live data procured from recommenders, aim to help the academic community to understand better offline evaluation and optimization criteria that are more relevant for real applications of recommender systems.
Boundary dents, the arctic circle and the arctic ellipse

The original motivation for this paper goes back to the mid-1990's, when James Propp was interested in natural situations when the number of domino tilings of a region increases if some of its unit squares are deleted. Guided in part by the intuition one gets from earlier work on parallels between the number of tilings of a region with holes and the 2D Coulomb energy of the corresponding system of electric charges, we consider Aztec diamond regions with unit square defects along two adjacent sides. We show that for large regions, if these defects are at fixed distances from a corner, the ratio between the number of domino tilings of the Aztec diamond with defects and the number of tilings of the entire Aztec diamond approaches a Delannoy number. When the locations of the defects are not fixed but instead approach given points on the boundary of the scaling limit $S$ (a square) of the Aztec diamonds, we prove that, provided the line segment connecting these points is outside the circle inscribed in $S$, this ratio has the same asymptotics as the Delannoy number corresponding to the locations of the defects; if the segment crosses the circle, the asymptotics is radically different. We use this to deduce (under the assumption that an arctic curve exists) that the arctic curve for domino tilings of Aztec diamonds is the circle inscribed in $S$. We also discuss counterparts of this phenomenon for lozenge tilings of hexagons.
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume

Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. However, this assumption does not hold in dynamic objects in scenarios, leading to errors during the view synthesis stage, such as feature mismatch and occlusion, which can significantly reduce the accuracy of the generated depth maps. To address this problem, we propose a novel dynamic cost volume that exploits residual optical flow to describe moving objects, improving incorrectly occluded regions in static cost volumes used in previous work. Nevertheless, the dynamic cost volume inevitably generates extra occlusions and noise, thus we alleviate this by designing a fusion module that makes static and dynamic cost volumes compensate for each other. In other words, occlusion from the static volume is refined by the dynamic volume, and incorrect information from the dynamic volume is eliminated by the static volume. Furthermore, we propose a pyramid distillation loss to reduce photometric error inaccuracy at low resolutions and an adaptive photometric error loss to alleviate the flow direction of the large gradient in the occlusion regions. We conducted extensive experiments on the KITTI and Cityscapes datasets, and the results demonstrate that our model outperforms previously published baselines for self-supervised monocular depth estimation.
Weighted Sparse Partial Least Squares for Joint Sample and Feature Selection

Sparse Partial Least Squares (sPLS) is a common dimensionality reduction technique for data fusion, which projects data samples from two views by seeking linear combinations with a small number of variables with the maximum variance. However, sPLS extracts the combinations between two data sets with all data samples so that it cannot detect latent subsets of samples. To extend the application of sPLS by identifying a specific subset of samples and remove outliers, we propose an $\ell_\infty/\ell_0$-norm constrained weighted sparse PLS ($\ell_\infty/\ell_0$-wsPLS) method for joint sample and feature selection, where the $\ell_\infty/\ell_0$-norm constrains are used to select a subset of samples. We prove that the $\ell_\infty/\ell_0$-norm constrains have the Kurdyka-\L{ojasiewicz}~property so that a globally convergent algorithm is developed to solve it. Moreover, multi-view data with a same set of samples can be available in various real problems. To this end, we extend the $\ell_\infty/\ell_0$-wsPLS model and propose two multi-view wsPLS models for multi-view data fusion. We develop an efficient iterative algorithm for each multi-view wsPLS model and show its convergence property. As well as numerical and biomedical data experiments demonstrate the efficiency of the proposed methods.
NORMAS SOCIALES Y ACTITUDES COMO PREDICTORES DEL USO DEL CONDÓN EN EL SEXO VAGINAL EN ESTUDIANTES UNIVERSITARIOS FEMENINOS: ¿VARÍA SU PESO EN FUNCIÓN DEL INDIVIDUALISMO-COLECTIVISMO?

La teoria de la conducta planificada (Ajzen, 1991) postula que una conducta social, como el uso del condon para sexo vaginal con la pareja estable, depende de 3 factores: 1) las normas sociales y 2) las actitudes y 3) del control conductual percibido. Triandis (1996) ha propuesto varias dimensiones de la cultura, una de ellas, la dimension individualismo-colectivismo. En las culturas mas individualistas, la conducta social tiende a estar regulada mas por las actitudes, puesto que se prefiere la competencia con los otros a la armonia. En las culturas mas colectivistas, la conducta social tiende a estar regulada mas por las normas sociales; y las actitudes se sacrifican en pro de la armonia con los otros. El objetivo es evaluar si el peso de normas sociales y actitudes para predecir el uso del condon en el sexo vaginal con la pareja estable en el ultimo encuentro sexual en sujetos femeninos varia en funcion del individualismo-colectivismo. Esto es, si en sujetos individualistas el mayor predictor son las actitudes y en sujetos colectivistas, las normas sociales. Palabras clave Uso Condon Individualismo colectivismo Estudiantes ABSTRACT SOCIAL NORMS AND ATTITUDES AS CONDOM USE PREDICTORS IN VAGINAL SEX IN FEMALE COLLEGE STUDENTS: DO THEIR WEIGHTS VARY AS A FUNCTION OF INDIVIDUALISM-COLLECTIVISM? The theory of planed behavior (Ajzen, 1991) asserts that a social behavior like condom use depends on 3 factors, 1) social norms, 2) attitudes, and 3) perceived behavioral control. Triandis (1996) has proposed several dimensions of culture - one of them - the dimension individualism-collectivism. In more individualistics cultures, social behavior tend to be regulated more by attitudes, because competition with others - rather than harmony - is preferred. In more collectivistics cultures, social behavior tend to be regulated more by social norms; and attitudes are relegated in favor of harmony with others. The target is to evaluate if the weights of social norms and attitudes to predict condom use in vaginal sex with the main partner in the last sexual encounter in feminine subjects varies as a function of individualism-collectivism, i.e., if in individualistics subjects the mayor predictor are attitudes and in collectivistics subjects the social norms are. Key words Condom Use Individualism-Collectivism Students