Miracle-dz/UniFAR-FTUs huggingface dataset:
Provable Reach-avoid Controllers Synthesis Based on Inner-approximating Controlled Reach-avoid Sets

In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a desired target set. The approach falls within the reachability analysis framework and is based on the computation of inner-approximations of controlled reach-avoid sets(CRSs). Given a target set and a safe set, the controlled reach-avoid set is the set of states such that starting from each of them, there exists at least one controller to ensure that the system can enter the target set while staying inside the safe set before the target hitting time. Therefore, the boundary of the controlled reach-avoid set acts as a barrier, which separating states capable of achieving the reach-avoid objective from those that are not, and thus the computed inner-approximation provides a viable space for the system to achieve the reach-avoid objective. Our approach for synthesizing reach-avoid controllers mainly consists of three steps. We first learn a safe set of states in the unknown environment from sensor measurements based on a support vector machine approach. Then, based on the learned safe set and target set, we compute an inner-approximation of the CRS. Finally, we synthesize controllers online to ensure that the system will reach the target set by evolving inside the computed inner-approximation. The proposed method is demonstrated on a Dubin's car system.
Enabling knowledge discovery in natural hazard engineering datasets on DesignSafe

Data-driven discoveries require identifying relevant data relationships from a sea of complex, unstructured, and heterogeneous scientific data. We propose a hybrid methodology that extracts metadata and leverages scientific domain knowledge to synthesize a new dataset from the original to construct knowledge graphs. We demonstrate our approach's effectiveness through a case study on the natural hazard engineering dataset on ``LEAP Liquefaction'' hosted on DesignSafe. Traditional lexical search on DesignSafe is limited in uncovering hidden relationships within the data. Our knowledge graph enables complex queries and fosters new scientific insights by accurately identifying relevant entities and establishing their relationships within the dataset. This innovative implementation can transform the landscape of data-driven discoveries across various scientific domains.
Morse inequalities for ordered eigenvalues of generic self-adjoint families

In many applied problems one seeks to identify and count the critical points of a particular eigenvalue of a smooth parametric family of self-adjoint matrices, with the parameter space often being known and simple, such as a torus. Among particular settings where such a question arises are the Floquet--Bloch decomposition of periodic Schroedinger operators, topology of potential energy surfaces in quantum chemistry, spectral optimization problems such as minimal spectral partitions of manifolds, as well as nodal statistics of graph eigenfunctions. In contrast to the classical Morse theory dealing with smooth functions, the eigenvalues of families of self-adjoint matrices are not smooth at the points corresponding to repeated eigenvalues (called, depending on the application and on the dimension of the parameter space, the diabolical/Dirac/Weyl points or the conical intersections). This work develops a procedure for associating a Morse polynomial to a point of eigenvalue multiplicity; it utilizes the assumptions of smoothness and self-adjointness of the family to provide concrete answers. In particular, we define the notions of non-degenerate topologically critical point and generalized Morse family, establish that generalized Morse families are generic in an appropriate sense, establish a differential first-order conditions for criticality, as well as compute the local contribution of a topologically critical point to the Morse polynomial. Remarkably, the non-smooth contribution to the Morse polynomial turns out to be universal: it depends only on the size of the eigenvalue multiplicity and the relative position of the eigenvalue of interest and not on the particulars of the operator family; it is expressed in terms of the homologies of Grassmannians.
Optimal Stopping with Trees: The Details

The purpose of this paper is two-fold, first, to review a recent method introduced by S. Becker, P. Cheridito, and P. Jentzen, for solving high-dimensional optimal stopping problems using deep Neural Networks, second, to propose an alternative algorithm replacing Neural Networks by CART-trees which allow for more interpretation of the estimated stopping rules. We in particular compare the performance of the two algorithms with respect to the Bermudan max-call benchmark example concluding that the Bermudan max-call may not be suitable to serve as a benchmark example for high-dimensional optimal stopping problems. We also show how our algorithm can be used to plot stopping boundaries.
VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval

The relations expressed in user queries are vital for cross-modal information retrieval. Relation-focused cross-modal retrieval aims to retrieve information that corresponds to these relations, enabling effective retrieval across different modalities. Pre-trained networks, such as Contrastive Language-Image Pre-training (CLIP), have gained significant attention and acclaim for their exceptional performance in various cross-modal learning tasks. However, the Vision Transformer (ViT) used in these networks is limited in its ability to focus on image region relations. Specifically, ViT is trained to match images with relevant descriptions at the global level, without considering the alignment between image regions and descriptions. This paper introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder. VITR is comprised of two key components. Firstly, it extends the capabilities of ViT-based cross-modal networks by enabling them to extract and reason with region relations present in images. Secondly, VITR incorporates a fusion module that combines the reasoned results with global knowledge to predict similarity scores between images and descriptions. The proposed VITR network was evaluated through experiments on the tasks of relation-focused cross-modal information retrieval. The results derived from the analysis of the RefCOCOg, CLEVR, and Flickr30K datasets demonstrated that the proposed VITR network consistently outperforms state-of-the-art networks in image-to-text and text-to-image retrieval.
Multi-crop Contrastive Learning for Unsupervised Image-to-Image Translation

 Abstract —Recently, image-to-image translation methods based on contrastive learning achieved state-of-the-art results in many tasks. However, the negatives are sampled from the input feature spaces in the previous work, which makes the negatives lack diversity. Moreover, in the latent space of the embedings,the previous methods ignore domain consistency between the generated image and the real images of target domain. In this paper, we propose a novel contrastive learning framework for unpaired image-to-image translation, called MCCUT. We utilize the multi-crop views to generate the negatives via the center-crop and the random-crop, which can improve the diversity of negatives and meanwhile increase the quality of negatives. To constrain the embedings in the deep feature space,, we formulate a new domain consistency loss function, which encourages the generated images to be close to the real images in the embedding space of same domain. Furthermore, we present a dual coordinate channel attention network by embedding positional information into SENet, which called DCSE module. We employ the DCSE module in the design of generator, which makes the generator pays more attention to channels with greater weight. In many image-to-image translation tasks, our method achieves state-of-the-art results, and the advantages of our method have been proved through extensive comparison experiments and ablation research.
Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting

Automated detection of software failures is an important but challenging software engineering task. It involves finding in a vast search space the failure-inducing test cases that contain an input triggering the software fault and an oracle asserting the incorrect execution. We are motivated to study how far this outstanding challenge can be solved by recent advances in large language models (LLMs) such as ChatGPT. However, our study reveals that ChatGPT has a relatively low success rate (28.8%) in finding correct failure-inducing test cases for buggy programs. A possible conjecture is that finding failure-inducing test cases requires analyzing the subtle differences (nuances) between the tokens of a program's correct version and those for its buggy version. When these two versions have similar sets of tokens and attentions, ChatGPT is weak in distinguishing their differences. We find that ChatGPT can successfully generate failure-inducing test cases when it is guided to focus on the nuances. Our solution is inspired by an interesting observation that ChatGPT could infer the intended functionality of buggy code if it is similar to the correct version. Driven by the inspiration, we develop a novel technique, called Differential Prompting, to effectively find failure-inducing test cases with the help of the compilable code synthesized by the inferred intention. Prompts are constructed based on the nuances between the given version and the synthesized code. We evaluate Differential Prompting on Quixbugs (a popular benchmark of buggy programs) and recent programs published at Codeforces (a popular programming contest portal, which is also an official benchmark of ChatGPT). We compare Differential Prompting with two baselines constructed using conventional ChatGPT prompting and Pynguin (the state-of-the-art unit test generation tool for Python programs). Our evaluation results show that for programs of Quixbugs, Differential Prompting can achieve a success rate of 75.0% in finding failure-inducing test cases, outperforming the best baseline by 2.6X. For programs of Codeforces, Differential Prompting's success rate is 66.7%, outperforming the best baseline by 4.0X.
ExCalibR: Expected Calibration of Recommendations

In many recommender systems and search problems, presenting a well balanced set of results can be an important goal in addition to serving highly relevant content. For example, in a movie recommendation system, it may be helpful to achieve a certain balance of different genres, likewise, it may be important to balance between highly popular versus highly personalized shows. Such balances could be thought across many categories and may be required for enhanced user experience, business considerations, fairness objectives etc. In this paper, we consider the problem of calibrating with respect to any given categories over items. We propose a way to balance a trade-off between relevance and calibration via a Linear Programming optimization problem where we learn a doubly stochastic matrix to achieve optimal balance in expectation. We then realize the learned policy using the Birkhoff-von Neumann decomposition of a doubly stochastic matrix. Several optimizations are considered over the proposed basic approach to make it fast. The experiments show that the proposed formulation can achieve a much better trade-off compared to many other baselines. This paper does not prescribe the exact categories to calibrate over (such as genres) universally for applications. This is likely dependent on the particular task or business objective. The main contribution of the paper is that it proposes a framework that can be applied to a variety of problems and demonstrates the efficacy of the proposed method using a few use-cases.
Improving Knowledge Distillation Via Transferring Learning Ability

Existing knowledge distillation methods generally use a teacher-student approach, where the student network solely learns from a well-trained teacher. However, this approach overlooks the inherent differences in learning abilities between the teacher and student networks, thus causing the capacity-gap problem. To address this limitation, we propose a novel method called SLKD.
Review of ensemble gradients for robust optimisation

In robust optimisation problems the objective function consists of an average over (an ensemble of) uncertain parameters. Ensemble optimisation (EnOpt) implements steepest descent by estimating the gradient using linear regression on Monte-Carlo simulations of (an ensemble of) control parameters. Applying EnOpt for robust optimisation is costly unless the evaluations over the two ensembles are combined, i.e. 'paired'. Here, we provide a new and more rigorous perspective on the stochastic simplex approximate gradient (StoSAG) used in EnOpt, explaining how it addresses detrimental cross-correlations arising from pairing by only capturing the variability due to the control vector, and not the vector of uncertain parameters. A few minor variants are derived from a generalised derivation, as well as a new approach using decorrelation. These variants are tested on linear and non-linear toy gradient estimation problems, where they achieve highly similar accuracy, but require a very large ensemble size to outperform the non-robust approach when accounting for variance and not just bias. Other original contributions include a discussion of the particular robust control objectives for which EnOpt is suited, illustrations, a variance reduction perspective, and a discussion on the centring in covariance and gradient estimation.
Development of a Trust-Aware User Simulator for Statistical Proactive Dialog Modeling in Human-AI Teams

The concept of a Human-AI team has gained increasing attention in recent years. For effective collaboration between humans and AI teammates, proactivity is crucial for close coordination and effective communication. However, the design of adequate proactivity for AI-based systems to support humans is still an open question and a challenging topic. In this paper, we present the development of a corpus-based user simulator for training and testing proactive dialog policies. The simulator incorporates informed knowledge about proactive dialog and its effect on user trust and simulates user behavior and personal information, including socio-demographic features and personality traits. Two different simulation approaches were compared, and a task-step-based approach yielded better overall results due to enhanced modeling of sequential dependencies. This research presents a promising avenue for exploring and evaluating appropriate proactive strategies in a dialog game setting for improving Human-AI teams.
The Estimation of Paclitaxel in Parenterals by RP-HPLC.

A simple, precise, rapid and accurate reverse phase HPLC method developed for the estimation of Paclitaxel in Parenterals dosage form. A Unison US C18, 250x4.6 mm i.d, 5 μm partical size, with mobile phase consisting of Methanol and 0.02 M potassium dihydrogen phosphate in water (pH 2.5 adjusted with o-phosphoric acid) in the ratio of 80:20 v/v was used. The flow rate was 1 ml/min and the effluents were monitored at 225 nm. The retention time was 4.978 min. The detector response was linear in the concentration of 15–180 mcg/ml. The respective linear regression equation being Y= 46411.83x+29.2578. The limit of detection and limit of quantification was 0.03 and 0.09 mcg/ml respectively. The percentage assay of Paclitaxel was 99.09%. The method was validated by determining its accuracy, precision and system suitability. The results of the study showed that the proposed RP-HPLC method is simple, rapid, precise and accurate, which is useful for the routine determination of Paclitaxel in bulk drug and in its pharmaceutical dosage form.
A Bulk Parameterization of the Ice Particle Size Distribution and the Optical Properties in Ice Clouds

Abstract A new parameterization has been developed that assumes that nonprecipitating particles obey the Heymsfield–Platt power-law (H–P particles) and that the precipitating particles obey the Marshall–Palmer distribution (M–P particles). The parameterization defines a critical ice content for the onset of precipitation particles and allows the number of ice crystals, the extinction coefficient, and the effective diameter of the crystals for the cloud layer in the model to be diagnosed. The implementation of the new parameterization in a model unifies the microphysical assumptions used to calculate the optical properties and precipitation. If it is assumed that the number of H–P particles at cloud top is much larger than the number of M–P particles in southeastern Australia frontal systems, then the observed number of ice crystals at cloud top agrees well with the diagnosed number of H–P particles at cloud top. A simulation of the passage of a cold front is used to test the parameterization. The modeled .
Negative group delay times in frustrated Gires-Tournois and Fabry-Perot interferometers

It is demonstrated in this paper that a Gires-Tournois interferometer illuminated with an angle of incidence greater than the critical angle for total internal reflection introduces a negative group delay time, whatever the orientation of the electric field vector of the wave with respect to the plane of incidence, when the evanescent wave in the dielectric layer is reflected by a dielectric substrate whose refractive index is between those of the incident medium and of the dielectric layer. When the evanescent wave in the dielectric layer is reflected by a nonabsorbing metal, the group delay time is negative when the electric field vector is in the plane of incidence and positive when the electric field vector is perpendicular to the plane of incidence. Similarly, a frustrated Fabry-Perot interferometer shows negative group delay times for angles of incidence greater than specific p-wave and s-wave critical angles.
The African Stopwords project: curating stopwords for African languages

Stopwords are fundamental in Natural Language Processing (NLP) techniques for information retrieval. One of the common tasks in preprocessing of text data is the removal of stopwords. Currently, while high-resource languages like English benefit from the availability of several stopwords, low-resource languages, such as those found in the African continent, have none that are standardized and available for use in NLP packages. Stopwords in the context of African languages are understudied and can reveal information about the crossover between languages. The \textit{African Stopwords} project aims to study and curate stopwords for African languages. In this paper, we present our current progress on ten African languages as well as future plans for the project.
Data-driven Knowledge Fusion for Deep Multi-instance Learning

Multi-instance learning (MIL) is a widely-applied technique in practical applications that involve complex data structures. MIL can be broadly categorized into two types: traditional methods and those based on deep learning. These approaches have yielded significant results, especially with regards to their problem-solving strategies and experimental validation, providing valuable insights for researchers in the MIL field. However, a considerable amount of knowledge is often trapped within the algorithm, leading to subsequent MIL algorithms that solely rely on the model's data fitting to predict unlabeled samples. This results in a significant loss of knowledge and impedes the development of more intelligent models. In this paper, we propose a novel data-driven knowledge fusion for deep multi-instance learning (DKMIL) algorithm. DKMIL adopts a completely different idea from existing deep MIL methods by analyzing the decision-making of key samples in the data set (referred to as the data-driven) and using the knowledge fusion module designed to extract valuable information from these samples to assist the model's training. In other words, this module serves as a new interface between data and the model, providing strong scalability and enabling the use of prior knowledge from existing algorithms to enhance the learning ability of the model. Furthermore, to adapt the downstream modules of the model to more knowledge-enriched features extracted from the data-driven knowledge fusion module, we propose a two-level attention module that gradually learns shallow- and deep-level features of the samples to achieve more effective classification. We will prove the scalability of the knowledge fusion module while also verifying the efficacy of the proposed architecture by conducting experiments on 38 data sets across 6 categories.
On polynomials associated to Voronoi diagrams of point sets and crossing numbers

Three polynomials are defined for given sets $S$ of $n$ points in general position in the plane: The Voronoi polynomial with coefficients the numbers of vertices of the order-$k$ Voronoi diagrams of~$S$, the circle polynomial with coefficients the numbers of circles through three points of $S$ enclosing $k$ points of $S$, and the $E_{\leq k}$ polynomial with coefficients the numbers of (at most $k$)-edges of~$S$. We present several formulas for the rectilinear crossing number of $S$ in terms of these polynomials and their roots. We also prove that the roots of the Voronoi polynomial lie on the unit circle if, and only if, $S$ is in convex position. Further, we present bounds on the location of the roots of these polynomials.
Preliminary results on the herdability of complex multiagent systems via local information

We present preliminary results on the problem of driving the dynamics of a group of agents, the herders, so as to steer the collective behaviour of another group of agents, the targets, interacting with them. We define this problem as the multiagent herding control problem and study the herdability of the target agents by relaxing some strong assumptions often made in the existing literature, namely that targets are cohesive in the absence of herders and that herders possess global information. We find that scaling laws exist linking crucial parameters such as the herders' sensing radius and the targets density with the herdability of the system. Our most surprising observation, supported by exhaustive numerical experiments, is that the crucial effect of limited sensing is that the minimum number of herders required to solve the problem significantly increases below a critical minimum density of the target agents.
Fast-speed and low-power-consumption optical phased array based on thin-film lithium niobate platform

Fast scanning-speed and low-power-consumption are becoming progressively more and more important in realizing high-performance chiplet optical phased arrays (OPAs). Here, we establish an integrated OPA based on thin-film lithium niobate-on-insulator (LNOI) platform to access these outstanding performances. Significantly, a lithium niobate (LN) OPA chip is implemented by 32/48 channels LN waveguides enabled by electro-optic modulations, which showcases the low power consumption (1.11nJ/{\pi}}) and fast operation speed (14.4 ns) promising the advantage of the LNOI platform for integrated OPAs. As results, we experimentally achieved a beam steering with a 62.2{\deg}*8.8{\deg} field of view (FOV) and a beam divergence of 2.4{\deg}*1.2{\deg}. Moreover, by employing sparse aperiodic arrays in waveguides design we obtained a significant reduction of lateral divergence to 0.33{\deg} for the radiation beam. This work demonstrate that remarkable advantage of LNOI platform for power-saving and scalable OPA chips for various applications.