Miracle-dz/UniFAR-FTUs huggingface dataset:
DEKGCI: A double-sided recommendation model for integrating knowledge graph and user-item interaction graph

Both knowledge graphs and user-item interaction graphs are frequently used in recommender systems due to their ability to provide rich information for modeling users and items. However, existing studies often focused on one of these sources (either the knowledge graph or the user-item interaction graph), resulting in underutilization of the benefits that can be obtained by integrating both sources of information. In this paper, we propose DEKGCI, a novel double-sided recommendation model. In DEKGCI, we use the high-order collaborative signals from the user-item interaction graph to enrich the user representations on the user side. Additionally, we utilize the high-order structural and semantic information from the knowledge graph to enrich the item representations on the item side. DEKGCI simultaneously learns the user and item representations to effectively capture the joint interactions between users and items. Three real-world datasets are adopted in the experiments to evaluate DEKGCI's performance, and experimental results demonstrate its high effectiveness compared to seven state-of-the-art baselines in terms of AUC and ACC.
Bounds on the moduli of eigenvalues of rational matrices

A rational matrix is a matrix-valued function $R(\lambda): \mathbb{C} \rightarrow M_p$ such that $R(\lambda) = \begin{bmatrix}r_{ij}(\lambda)\end{bmatrix} _{p\times p}$, where $r_{ij}(\lambda)$ are scalar complex rational functions in $\lambda$ for $i,j=1,2,\ldots,p$. The main aim of this paper is to obtain bounds on the moduli of eigenvalues of rational matrices in terms of the moduli of its poles. To a given rational matrix $R(\lambda)$ we associate a block matrix $\mathcal{C}_R$ whose blocks consists of the coefficient matrices of $R(\lambda)$, as well as a scalar real rational function $q(x)$ whose coefficients are composed of the norm of the coefficient matrices of $R(\lambda)$. We prove that a zero of the real rational function $q(x)$ which is greater than moduli of all the poles of $R(\lambda)$ serves as an upper bound on the moduli of eigenvalues of $R(\lambda)$. Moreover, by using a block matrix associated with $q(x)$, we establish bounds on the zeros of $q(x)$, which in turn yields bounds on the moduli of eigenvalues of $R(\lambda)$. Bounds on the zeros of an arbitrary scalar complex rational function are also obtained in this process.
Variable-Length Codes with Bursty Feedback

We study variable-length codes for point-to-point discrete memoryless channels with noiseless unlimited-rate feed-back that occurs in L bursts. We term such codes variable-length bursty-feedback (VLBF) codes. Unlike classical codes with feedback after each transmitted code symbol, bursty feedback fits better with protocols that employ sparse feedback after a packet is sent and also with half-duplex end devices that cannot transmit and listen to the channel at the same time. We present a novel non-asymptotic achievability bound for VLBF codes with L bursts of feedback over any discrete memoryless channel. We numerically evaluate the bound over the binary symmetric channel (BSC). We perform optimization over the time instances at which feedback occurs for both our own bound and Yavas et al.’s non-asymptotic achievability bound for variable-length stop-feedback (VLSF) codes, where only a single bit is sent at each feedback instance. Our results demonstrate the advantages of richer feedback: VLBF codes significantly outperform VLSF codes at short blocklengths, especially as the error probability ϵ decreases. Remarkably, for BSC(0.11) and error probability 10−10, our VLBF code with L = 5 and expected decoding time N ≤ 400 outperforms the achievability bound given by Polyanskiy et al. for VLSF codes with L = ∞, and our VLBF code with L = 3.
From relative Rota-Baxter operators and relative averaging operators on Lie algebras to relative Rota-Baxter operators on Leibniz algebras: a uniform approach

In this paper, first we construct two subcategories (using symmetric representations and antisymmetric representations) of the category of relative Rota-Baxter operators on Leibniz algebras, and establish the relations with the categories of relative Rota-Baxter operators and relative averaging operators on Lie algebras. Then we show that there is a short exact sequence describing the relation between the controlling algebra of relative Rota-Baxter operators on a Leibniz algebra with respect to a symmetric (resp. antisymmetric) representation and the controlling algebra of the induced relative Rota-Baxter operators (resp. averaging operators) on the canonical Lie algebra associated to the Leibniz algebra. Finally, we show that there is a long exact sequence describing the relation between the cohomology groups of a relative Rota-Baxter operator on a Leibniz algebra with respect to a symmetric (resp. antisymmetric) representation and the cohomology groups of the induced relative Rota-Baxter operator (resp. averaging operator) on the canonical Lie algebra.
Context-Encoded Code Change Representation for Automated Commit Message Generation

Changes in source code are an inevitable part of software development. They are the results of indispensable activities such as fixing bugs or improving functionality. Descriptions for code changes (commit messages) help people better understand the changes. However, due to a lack of motivation and time pressure, writing high-quality commit messages remains reluctantly considered. Several methods have been proposed with the aim of automated commit message generation. However, the existing methods are still limited because they only utilise either the changed code or the changed code combined with surrounding statements. This paper proposes a method to represent code changes by combining the changed code and the unchanged code which have program dependence on the changed code. This method overcomes the limitations of current representations while improving the performance of 5/6 of state-of-the-art commit message generation methods by up to 15% in METEOR, 14% in ROUGE-L, and 10% in BLEU-4.
Can An Old Fashioned Feature Extraction and A Light-weight Model Improve Vulnerability Type Identification Performance?

Recent advances in automated vulnerability detection have achieved potential results in helping developers determine vulnerable components. However, after detecting vulnerabilities, investigating to fix vulnerable code is a non-trivial task. In fact, the types of vulnerability, such as buffer overflow or memory corruption, could help developers quickly understand the nature of the weaknesses and localize vulnerabilities for security analysis. In this work, we investigate the problem of vulnerability type identification (VTI). The problem is modeled as the multi-label classification task, which could be effectively addressed by"pre-training, then fine-tuning"framework with deep pre-trained embedding models. We evaluate the performance of the well-known and advanced pre-trained models for VTI on a large set of vulnerabilities. Surprisingly, their performance is not much better than that of the classical baseline approach with an old-fashioned bag-of-word, TF-IDF. Meanwhile, these deep neural network approaches cost much more resources and require GPU. We also introduce a lightweight independent component to refine the predictions of the baseline approach. Our idea is that the types of vulnerabilities could strongly correlate to certain code tokens (distinguishing tokens) in several crucial parts of programs. The distinguishing tokens for each vulnerability type are statistically identified based on their prevalence in the type versus the others. Our results show that the baseline approach enhanced by our component can outperform the state-of-the-art deep pre-trained approaches while retaining very high efficiency. Furthermore, the proposed component could also improve the neural network approaches by up to 92.8% in macro-average F1.
On the distribution of sensitivities of symmetric Boolean functions

A Boolean function $f({\vec x})$ is sensitive to bit $x_i$ if there is at least one input vector $\vec x$ and one bit $x_i$ in $\vec x$, such that changing $x_i$ changes $f$. A function has sensitivity $s$ if among all input vectors, the largest number of bits to which $f$ is sensitive is $s$. We count the $n$-variable symmetric Boolean functions that have maximum sensitivity. We show that most such functions have the largest possible sensitivity, $n$. This suggests sensitivity is limited as a complexity measure for symmetric Boolean functions.
Recurring patterns in online social media interactions during highly engaging events

People nowadays express their opinions in online spaces, using different forms of interactions such as posting, sharing and discussing with one another. These digital traces allow to capture how people dynamically react to the myriad of events occurring in the world. By unfolding the structure of Reddit conversations, we describe how highly engaging events happening in the society affect user interactions and behaviour with respect to unperturbed discussion patterns. Conversations, defined as a post and the comments underneath, are analysed along their temporal and semantic dimensions. We disclose that changes in the pace and language used in conversations exhibit notable similarities across diverse events. Conversations tend to become repetitive with a more limited vocabulary, display different semantic structures and feature heightened emotions. As the event approaches, the shifts occurring in conversations are reflected in the users' dynamics. Users become more active and they exchange information with a growing audience, despite using a less rich vocabulary and repetitive messages. The peers of each user fill up more semantic space, shifting the dialogue and widening the exchange of information. The recurring patterns we discovered are persistent across several contexts, thus represent a fingerprint of human behavior, which could impact the modeling of online social networks interactions.
PWSHAP: A Path-Wise Explanation Model for Targeted Variables

Predictive black-box models can exhibit high accuracy but their opaque nature hinders their uptake in safety-critical deployment environments. Explanation methods (XAI) can provide confidence for decision-making through increased transparency. However, existing XAI methods are not tailored towards models in sensitive domains where one predictor is of special interest, such as a treatment effect in a clinical model, or ethnicity in policy models. We introduce Path-Wise Shapley effects (PWSHAP), a framework for assessing the targeted effect of a binary (e.g.~treatment) variable from a complex outcome model. Our approach augments the predictive model with a user-defined directed acyclic graph (DAG). The method then uses the graph alongside on-manifold Shapley values to identify effects along causal pathways whilst maintaining robustness to adversarial attacks. We establish error bounds for the identified path-wise Shapley effects and for Shapley values. We show PWSHAP can perform local bias and mediation analyses with faithfulness to the model. Further, if the targeted variable is randomised we can quantify local effect modification. We demonstrate the resolution, interpretability, and true locality of our approach on examples and a real-world experiment.
Push-MOG: Efficient Pushing to Consolidate Polygonal Objects for Multi-Object Grasping

Recently, robots have seen rapidly increasing use in homes and warehouses to declutter by collecting objects from a planar surface and placing them into a container. While current techniques grasp objects individually, Multi-Object Grasping (MOG) can improve efficiency by increasing the average number of objects grasped per trip (OpT). However, grasping multiple objects requires the objects to be aligned and in close proximity. In this work, we propose Push-MOG, an algorithm that computes “fork pushing” actions using a parallel-jaw gripper to create graspable object clusters. In physical decluttering experiments, we find that Push-MOG enables multi-object grasps, increasing the average OpT by 34%. Code and videos are available at https://sites.google. com/berkeley.edu/push-mog.
Gradient Descent Converges Linearly for Logistic Regression on Separable Data

We show that running gradient descent with variable learning rate guarantees loss $f(x) \leq 1.1 \cdot f(x^*) + \epsilon$ for the logistic regression objective, where the error $\epsilon$ decays exponentially with the number of iterations and polynomially with the magnitude of the entries of an arbitrary fixed solution $x^*$. This is in contrast to the common intuition that the absence of strong convexity precludes linear convergence of first-order methods, and highlights the importance of variable learning rates for gradient descent. We also apply our ideas to sparse logistic regression, where they lead to an exponential improvement of the sparsity-error tradeoff.
Deceptive AI Ecosystems: The Case of ChatGPT

ChatGPT, an AI chatbot, has gained popularity for its capability in generating human-like responses. However, this feature carries several risks, most notably due to its deceptive behaviour such as offering users misleading or fabricated information that could further cause ethical issues. To better understand the impact of ChatGPT on our social, cultural, economic, and political interactions, it is crucial to investigate how ChatGPT operates in the real world where various societal pressures influence its development and deployment. This paper emphasizes the need to study ChatGPT "in the wild", as part of the ecosystem it is embedded in, with a strong focus on user involvement. We examine the ethical challenges stemming from ChatGPT’s deceptive human-like interactions and propose a roadmap for developing more transparent and trustworthy chatbots. Central to our approach is the importance of proactive risk assessment and user participation in shaping the future of chatbot technology.
Pointwise-in-Time Explanation for Linenr Temporal Logic Rules

The new field of Explainable Planning (XAIP) has produced a variety of approaches to explain and describe the behavior of autonomous agents to human observers. Many summarize agent behavior in terms of the constraints, or “rules,” which the agent adheres to during its trajectories. In this work, we narrow the focus from summary to specific moments in individual trajectories, offering a “pointwise-in-time” view. Our novel framework, which we define on Linear Temporal Logic (LTL) rules, assigns an intuitive status to any rule in order to describe the trajectory progress at individual time steps; here, a rule is classified as active, satisfied, inactive, or violated. Given a trajectory, a user may query for status of specific LTL rules at individual trajectory time steps. In this paper, we present this novel framework, named Rule Status Assessment (RSA), and provide an example of its implementation. We find that pointwise-in-time status assessment is useful as a post-hoc diagnostic, enabling a user to systematically track the agent's behavior with respect to a set of rules.
Evaluating the overall sensitivity of saliency-based explanation methods

We address the need to generate faithful explanations of"black box"Deep Learning models. Several tests have been proposed to determine aspects of faithfulness of explanation methods, but they lack cross-domain applicability and a rigorous methodology. Hence, we select an existing test that is model agnostic and is well-suited for comparing one aspect of faithfulness (i.e., sensitivity) of multiple explanation methods, and extend it by specifying formal thresh-olds and building criteria to determine the over-all sensitivity of the explanation method. We present examples of how multiple explanation methods for Convolutional Neural Networks can be compared using this extended methodology. Finally, we discuss the relationship between sensitivity and faithfulness and consider how the test can be adapted to assess different explanation methods in other domains.
Latent Factor Analysis in Short Panels

We develop inferential tools for latent factor analysis in short panels. The pseudo maximum likelihood setting under a large cross-sectional dimension $n$ and a fixed time series dimension $T$ relies on a diagonal $T \times T$ covariance matrix of the errors without imposing sphericity or Gaussianity. We outline the asymptotic distributions of the latent factor and error covariance estimates as well as of an asymptotically uniformly most powerful invariant (AUMPI) test based on the likelihood ratio statistic for tests of the number of factors. We derive the AUMPI characterization from inequalities ensuring the monotone likelihood ratio property for positive definite quadratic forms in normal variables. An empirical application to a large panel of monthly U.S. stock returns separates date after date systematic and idiosyncratic risks in short subperiods of bear vs. bull market based on the selected number of factors. We observe an uptrend in idiosyncratic volatility while the systematic risk explains a large part of the cross-sectional total variance in bear markets but is not driven by a single factor. We also find that observed factors, scaled or not, struggle spanning latent factors. Rank tests reveal that observed factors struggle spanning latent factors with a discrepancy between the dimension of the two factor spaces decreasing over time.
Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks

As we deal with the effects of climate change and the increase of global atmospheric temperatures, the accurate tracking and prediction of ice layers within polar ice sheets grows in importance. Studying these ice layers reveals climate trends, how snowfall has changed over time, and the trajectory of future climate and precipitation. In this paper, we propose a machine learning model that uses adaptive, recurrent graph convolutional networks to, when given the amount of snow accumulation in recent years gathered through airborne radar data, predict historic snow accumulation by way of the thickness of deep ice layers. We found that our model performs better and with greater consistency than our previous model as well as equivalent non-temporal, non-geometric, and non-adaptive models.
Approximation Algorithm for Unrooted Prize-Collecting Forest with Multiple Components and Its Application on Prize-Collecting Sweep Coverage

In this paper, we introduce a polynomial-time 2-approximation algorithm for the Unrooted Prize-Collecting Forest with $K$ Components (URPCF$_K$) problem. URPCF$_K$ aims to find a forest with exactly $K$ connected components while minimizing both the forest's weight and the penalties incurred by unspanned vertices. Unlike the rooted version RPCF$_K$, where a 2-approximation algorithm exists, solving the unrooted version by guessing roots leads to exponential time complexity for non-constant $K$. To address this challenge, we propose a rootless growing and rootless pruning algorithm. We also apply this algorithm to improve the approximation ratio for the Prize-Collecting Min-Sensor Sweep Cover problem (PCMinSSC) from 8 to 5. Keywords: approximation algorithm, prize-collecting Steiner forest, sweep cover.
ContentCTR: Frame-level Live Streaming Click-Through Rate Prediction with Multimodal Transformer

In recent years, live streaming platforms have gained immense popularity as they allow users to broadcast their videos and interact in real-time with hosts and peers. Due to the dynamic changes of live content, accurate recommendation models are crucial for enhancing user experience. However, most previous works treat the live as a whole item and explore the Click-through-Rate (CTR) prediction framework on item-level, neglecting that the dynamic changes that occur even within the same live room. In this paper, we proposed a ContentCTR model that leverages multimodal transformer for frame-level CTR prediction. First, we present an end-to-end framework that can make full use of multimodal information, including visual frames, audio, and comments, to identify the most attractive live frames. Second, to prevent the model from collapsing into a mediocre solution, a novel pairwise loss function with first-order difference constraints is proposed to utilize the contrastive information existing in the highlight and non-highlight frames. Additionally, we design a temporal text-video alignment module based on Dynamic Time Warping to eliminate noise caused by the ambiguity and non-sequential alignment of visual and textual information. We conduct extensive experiments on both real-world scenarios and public datasets, and our ContentCTR model outperforms traditional recommendation models in capturing real-time content changes. Moreover, we deploy the proposed method on our company platform, and the results of online A/B testing further validate its practical significance.
Dispersion analyses of FDTD schemes associated with dispersive media

Researchers have been extending the finite difference time domain algorithm to account for frequency-dependent media. These algorithms have been devised for cold plasma (isotropic and anisotropic), Debye, Lorentz media, etc. Essentially, the methodologies are either convolution based, which discretizes the constitutive convolution integral, or direct integration based, which discretizes an auxiliary set of equations. Although all schemes have been shown to yield good results, for a certain selection of numerical discretization parameters, it is not clear which method leads to more accurate solutions, all other things being equal. We present various analyses that highlight the dispersive and dissipation errors created by each of the discretization schemes, namely, those of Luebbers et al. (1990, 1992), Joseph et al. (1991) and Young (see IEEE Trans. Ant. and Prop., Nov. 1994). This is accomplished by considering the dispersive relationship of each method with Debye and Lorentz media in mind.
Sensor Selection for Remote State Estimation with QoS Requirement Constraints

In this paper, we study the sensor selection problem for remote state estimation under the Quality-of-Service (QoS) requirement constraints. Multiple sensors are employed to observe a linear time-invariant system, and their measurements should be transmitted to a remote estimator for state estimation. However, due to the limited communication resources and the QoS requirement constraints, only some of the sensors can be allowed to transmit their measurements. To estimate the system state as accurately as possible, it is essential to select sensors for transmission appropriately. We formulate the sensor selection problem as a non-convex optimization problem. It is difficult to solve such a problem and even to find a feasible solution. To obtain a solution which can achieve good estimation performance, we first reformulate and relax the formulated problem. Then, we propose an algorithm based on successive convex approximation (SCA) to solve the relaxed problem. By utilizing the solution of the relaxed problem, we propose a heuristic sensor selection algorithm which can provide a good suboptimal solution. Simulation results are presented to show the effectiveness of the proposed heuristic.