Miracle-dz/UniFAR-FTUs huggingface dataset:
Information-Theoretic Text Hallucination Reduction for Video-grounded Dialogue

Video-grounded Dialogue (VGD) aims to decode an answer sentence to a question regarding a given video and dialogue context. Despite the recent success of multi-modal reasoning to generate answer sentences, existing dialogue systems still suffer from a text hallucination problem, which denotes indiscriminate text-copying from input texts without an understanding of the question. This is due to learning spurious correlations from the fact that answer sentences in the dataset usually include the words of input texts, thus the VGD system excessively relies on copying words from input texts by hoping those words to overlap with ground-truth texts. Hence, we design Text Hallucination Mitigating (THAM) framework, which incorporates Text Hallucination Regularization (THR) loss derived from the proposed information-theoretic text hallucination measurement approach. Applying THAM with current dialogue systems validates the effectiveness on VGD benchmarks (i.e., AVSD@DSTC7 and AVSD@DSTC8) and shows enhanced interpretability.
Walkability Optimization: Formulations, Algorithms, and a Case Study of Toronto

The concept of walkable urban development has gained increased attention due to its public health, economic, and environmental sustainability benefits. Unfortunately, land zoning and historic under-investment have resulted in spatial inequality in walkability and social inequality among residents. We tackle the problem of Walkability Optimization through the lens of combinatorial optimization. The task is to select locations in which additional amenities (e.g., grocery stores, schools, restaurants) can be allocated to improve resident access via walking while taking into account existing amenities and providing multiple options (e.g., for restaurants). To this end, we derive Mixed-Integer Linear Programming (MILP) and Constraint Programming (CP) models. Moreover, we show that the problem’s objective function is submodular in special cases, which motivates an efficient greedy heuristic. We conduct a case study on 31 underserved neighborhoods in the City of Toronto, Canada. MILP finds the best solutions in most scenarios but does not scale well with network size. The greedy algorithm scales well and finds high-quality solutions. Our empirical evaluation shows that neighbourhoods with low walkability have a great potential for transformation into pedestrian-friendly neighbourhoods by strategically placing new amenities. Allocating 3 additional grocery stores, schools, and restaurants can improve the “WalkScore” by more than 50 points (on a scale of 100) for 4 neighbourhoods and reduce the walking distances to amenities for 75% of all residential locations to 10 minutes for all amenity types. Our code and paper appendix are available at https://github.com/khalil-research/walkability.
Impedance-matched high-overtone bulk acoustic resonator

We demonstrated a nearly impedance-matched high-overtone bulk acoustic resonator (HBAR) operating at super high frequency ranges using an epitaxial AlN piezoelectric layer directly grown on a conductive SiC cavity substrate with no metal layer insertion. The small impedance mismatch was verified from the variation in the free spectral range (FSR); the experimentally obtained FSR spectra were greatly reproduced using the Mason model. Broadband phonon cavity modes up to the K-band (26.5 GHz) were achieved at an AlN layer thickness of 200 nm. The high figure of merit of [Formula: see text] at 10 GHz was also obtained. Our nearly impedance-matched high-quality HBAR will enable the development of microwave signal processing devices for 5G and future 6G communication systems, such as low-phase noise oscillators and acoustic filters, as well as research on high-frequency acoustic systems hybridized with electric, optical, and magnetic systems.
An Incremental Gradient Method for Optimization Problems With Variational Inequality Constraints

We consider minimizing a sum of agent-specific nondifferentiable merely convex functions over the solution set of a variational inequality (VI) problem in that each agent is associated with a local monotone mapping. This problem finds an application in computation of the best equilibrium in nonlinear complementarity problems arising in transportation networks. We develop an iteratively regularized incremental gradient method where at each iteration, agents communicate over a directed cycle graph to update their solution iterates using their local information about the objective and the mapping. The proposed method is single-timescale in the sense that it does not involve any excessive hard-to-project computation per iteration. We derive nonasymptotic agent-wise convergence rates for the suboptimality of the global objective function and infeasibility of the VI constraints measured by a suitably defined dual gap function. The proposed method appears to be the first fully iterative scheme equipped with iteration complexity that can address distributed optimization problems with VI constraints over cycle graphs.
Efficiency Matters: Speeding Up Automated Testing with GUI Rendering Inference

Due to the importance of Android app quality assurance, many automated GUI testing tools have been developed. Although the test algorithms have been improved, the impact of GUI rendering has been overlooked. On the one hand, setting a long waiting time to execute events on fully rendered GUIs slows down the testing process. On the other hand, setting a short waiting time will cause the events to execute on partially rendered GUIs, which negatively affects the testing effectiveness. An optimal waiting time should strike a balance between effectiveness and efficiency. We propose AdaT, a lightweight image-based approach to dynamically adjust the inter-event time based on GUI rendering state. Given the real-time streaming on the GUI, AdaT presents a deep learning model to infer the rendering state, and synchronizes with the testing tool to schedule the next event when the GUI is fully rendered. The evaluations demonstrate the accuracy, efficiency, and effectiveness of our approach. We also integrate our approach with the existing automated testing tool to demonstrate the usefulness of AdaT in covering more activities and executing more events on fully rendered GUIs.
Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package

Biodiversity and ecosystem function (BEF) studies aim to understand how ecosystems respond to a gradient of species diversity. Generalised Diversity‐Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is included implicitly in a DI model. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is broader than richness alone, and important patterns in the BEF relationship may remain hidden. Here, we introduce the DImodels R package for implementing DI models. We show how richness is mathematically equivalent to a simplified DI model under certain conditions, and illustrate how using the DI multidimensional definition of species diversity can provide deeper insight to the BEF relationship compared to traditional approaches. Using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can interpolate or extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference. The open‐source DImodels R package provides a user‐friendly way to implement this modelling approach.
Fighting Malicious Media Data: A Survey on Tampering Detection and Deepfake Detection

Online media data, in the forms of images and videos, are becoming mainstream communication channels. However, recent advances in deep learning, particularly deep generative models, open the doors for producing perceptually convincing images and videos at a low cost, which not only poses a serious threat to the trustworthiness of digital information but also has severe societal implications. This motivates a growing interest of research in media tampering detection, i.e., using deep learning techniques to examine whether media data have been maliciously manipulated. Depending on the content of the targeted images, media forgery could be divided into image tampering and Deepfake techniques. The former typically moves or erases the visual elements in ordinary images, while the latter manipulates the expressions and even the identity of human faces. Accordingly, the means of defense include image tampering detection and Deepfake detection, which share a wide variety of properties. In this paper, we provide a comprehensive review of the current media tampering detection approaches, and discuss the challenges and trends in this field for future research.
Starfruit Shape Defect Estimation Based on Concave and Convex Area of a Closed Planar Curve

Dalam kertas kerja ini, sebuah perwakilan bentuk berdasarkan kawasan cekung dan cembung di sepanjang keluk tertutup dipersembahkan. Teknik yang dicadangkan dalam kertas kerja ini melibatkan proses anggaran kekelukan daripada keluk masukan dan juga proses pencarian titik kritikal pada keluk masukan tersebut. Dengan mengasingkan titik kritikal tersebut kepada kategori cekung dan cembung, pengiraan kawasan cekung dan cembung dilakukan. Daripada ciri–ciri statistik ini, dua permasalahan berkaitan dengan bentuk disiasat. Di sini, teknik yang dicadangkan diuji terhadap proses penganggaran kerosakan bentuk dan pengenalan bentuk pada buah belimbing. Pada kes pertama, kerosakan bentuk belimbing diukur dengan mengira kuasa cekung di mana kuasa cekung ini adalah selanjar dengan kerosakan bentuk tersebut. Untuk kes pengenalpastian bentuk pula, tangkai belimbing adalah objek untuk dikenal pasti dan tangkai belimbing ini akan diasingkan daripada bentuk belimbing kerana ia akan menyumbang kepada kesalahan pengiraan kerosakan bentuk belimbing. Bagi kedua–dua kes, teknik yang dicadangkan diuji dengan tiga teknik penganggaran kekelukan untuk memastikan keberkesanan teknik yang dicadangkan. Kata kunci: Perwakilan bentuk, kekelukan, titik kritikal, kawasan cekung dan cembung In this paper, a shape representation based on concave and convex area along a closed curve is presented. The proposed technique involves the process of the curvature estimation from the input curve and search for its corresponding critical points. By splitting the critical points into concave and convex categories, the concave and convex area are computed. From these statistical features, two problems related to the shape (curve) are investigated. Here, the proposed technique is tested on shape defect estimation and shape recognition of starfruit. In the first case, defect is measured by computing concave energy, which is proportional to the defect. For shape recognition, starfruit’s stem is identified and removed from the starfruit’s shape, as it will contributes false computation of defect measurement. For both cases, the proposed technique is tested with three different curvature estimation techniques to validate the results. Key words: Shape representation, curvature, critical point, concave and convex area
Two-Motor Synchronous Decoupling Control Based on Improved Incremental Regularized Extreme Learning Machine

Neural network generalized inverse (NNGI) can realize two-motor synchronous decoupling control, but traditional neural network (NN) exists many shortcomings, Regular extreme learning machine (RELM) has fast learning and good generalization ability, which is an ideal approach to approximate inverse system. But it is difficult to accurately give the reasonable number of hidden neurons. Improved incremental RELM(IIRELM) is prospected on the basis of analyzing RELM learning algorithm, which can automatically determine optimal network structure through gradually adding new hidden-layer neurons, and prediction model based on IIRELM is applied in two-motor closed-loop control based on NNGI, the decoupling control between velocity and tension is realized. The experimental results proved that the system has excellent performance.
A Hierarchical Approach for Investigating Social Features of a City from Mobile Phone Call Detail Records

Cellphone service-providers continuously collect Call Detail Records (CDR) as a usage log containing spatio-temporal traces of phone users. We proposed a multi-layered hierarchical analytical model for large spatio-temporal datasets and applied that for the progressive exploration of social features of a city, e.g., social activities, relationships, and groups, from CDR. This approach utilizes CDR as the preliminary input for the initial layer, and analytical results from consecutive layers are added to the knowledge-base to be used in the subsequent layers to explore more detailed social features. Each subsequent layer uses the results from previous layers, facilitating the discovery of more in-depth social features not predictable in a single-layered approach using only raw CDR. This model starts with exploring aggregated overviews of the social features and gradually focuses on comprehensive details of social relationships and groups, which facilitates a novel approach for investigating CDR datasets for the progressive exploration of social features in a densely-populated city.
Detecting Code Injections in Noisy Environments Through EM Signal Analysis and SVD Denoising

The penetration of embedded devices in networks that support critical applications has rendered them a lucrative target for attackers and evildoers. However, traditional protection mechanisms may not be supported due to the memory and computational limitations of these systems. Recently, the analysis of electromagnetic (EM) emanations has gathered the interest of the research community. Thus, analo-gous protection systems have emerged as a viable solution e.g
Reverberation Mapping of IC 4329A

We present the results of a new reverberation mapping campaign for the broad-line active galactic nucleus (AGN) in the edge-on spiral IC 4329A. Monitoring of the optical continuum with V-band photometry and broad emission-line flux variability with moderate-resolution spectroscopy allowed emission-line light curves to be measured for Hβ, Hγ, and He ii λ4686. We find a time delay of 16.3−2.3+2.6 days for Hβ, a similar time delay of 16.0−2.6+4.8 days for Hγ, and an unresolved time delay of −0.6−3.9+3.9 days for He ii. The time delay for Hβ is consistent with the predicted value from the relationship between AGN luminosity and broad-line region radius, after correction for the ∼2.4 mag of intrinsic extinction at 5100 Å. Combining the measured time delay for Hβ with the broad emission-line width and an adopted value of 〈f〉 = 4.8, we find a central supermassive black hole mass of MBH=6.8−1.1+1.2×107 M ⊙. Velocity-resolved time delays were measured across the broad Hβ emission-line profile and may be consistent with an “M”-like shape. Modeling of the full reverberation response of Hβ was able to provide only modest constraints on some parameters, but does exhibit agreement with the black hole mass and average time delay. The models also suggest that the AGN structure is misaligned by a large amount from the edge-on galaxy disk. This is consistent with expectations from the unified model of AGNs, in which broad emission lines are expected to be visible only for AGNs that are viewed at relatively face-on inclinations.
Generation of nearly pure and highly directional magnetic light in fluorescence of rare-earth ions

A thorough analysis of the emission via the magnetic dipole (MD) transition, called magnetic light below, of trivalent rare-earth ions in or near dielectric homogeneous spheres has been performed. In the search for enhancement of fluorescence from magnetic light, one faces the difficult task of identifying the regions where the combined fluorescence due to multiple electric dipole (ED) transitions becomes negligible compared to the fluorescence of the MD transition. We have succeeded in identifying a number of configurations with dielectric sphere parameters and a radial position of a trivalent rare-earth emitter wherein the branching ratio of the MD transition approaches its limit of one, implying that transitions from a given initial level (e.g., $^5$D$_0$-level of Eu$^{3+}$) are completely dominated by the MD transition. The dimensionless directivity of the MD emission, the radiative decay rates, and the fluorescence of the magnetic light can be increased by a factor of more than $25$, $10^3$, and $10^4$, respectively.
Markerless Body Motion Capturing for 3D Character Animation based on Multi-view Cameras

This paper proposes a novel application system for the generation of three-dimensional (3D) character animation driven by markerless human body motion capturing. The entire pipeline of the system consists of five stages: 1) the capturing of motion data using multiple cameras, 2) detection of the two-dimensional (2D) human body joints, 3) estimation of the 3D joints, 4) calculation of bone transformation matrices, and 5) generation of character animation. The main objective of this study is to generate a 3D skeleton and animation for 3D characters using multi-view images captured by ordinary cameras. The computational complexity of the 3D skeleton reconstruction based on 3D vision has been reduced as needed to achieve frame-by-frame motion capturing. The experimental results reveal that our system can effectively and efficiently capture human actions and use them to animate 3D cartoon characters in real-time.
The entropy production of stationary diffusions

The entropy production rate is a central quantity in non-equilibrium statistical physics, scoring how far a stochastic process is from being time-reversible. In this paper, we compute the entropy production of diffusion processes at non-equilibrium steady-state under the condition that the time-reversal of the diffusion remains a diffusion. We start by characterising the entropy production of both discrete and continuous-time Markov processes. We investigate the time-reversal of time-homogeneous stationary diffusions and recall the most general conditions for the reversibility of the diffusion property, which includes hypoelliptic and degenerate diffusions, and locally Lipschitz vector fields. We decompose the drift into its time-reversible and irreversible parts, or equivalently, the generator into symmetric and antisymmetric operators. We show the equivalence with a decomposition of the backward Kolmogorov equation considered in hypocoercivity theory, and a decomposition of the Fokker-Planck equation in GENERIC form. The main result shows that when the time-irreversible part of the drift is in the range of the volatility matrix (almost everywhere) the forward and time-reversed path space measures of the process are mutually equivalent, and evaluates the entropy production. When this does not hold, the measures are mutually singular and the entropy production is infinite. We verify these results using exact numerical simulations of linear diffusions. We illustrate the discrepancy between the entropy production of non-linear diffusions and their numerical simulations in several examples and illustrate how the entropy production can be used for accurate numerical simulation. Finally, we discuss the relationship between time-irreversibility and sampling efficiency, and how we can modify the definition of entropy production to score how far a process is from being generalised reversible.
Realistic Modeling of Human Timings for Wearable Cognitive Assistance

Wearable Cognitive Assistance (WCA) applications present a challenge to benchmark and characterize due to their human-in-the-loop nature. Employing user testing to optimize system parameters is generally not feasible, given the scope of the problem and the number of observations needed to detect small but important effects in controlled experiments. Considering the intended mass-scale deployment of WCA applications in the future, there exists a need for tools enabling human-independent benchmarking. We present in this paper the first model for the complete end-to-end emulation of humans in WCA. We build this model through statistical analysis of data collected from previous work in this field, and demonstrate its utility by studying application task durations. Compared to first-order approximations, our model shows a ~36% larger gap between step execution times at high system impairment versus low. We further introduce a novel framework for stochastic optimization of resource consumption-responsiveness tradeoffs in WCA, and show that by combining this framework with our realistic model of human behavior, significant reductions of up to 50% in number processed frame samples and 20% in energy consumption can be achieved with respect to the state-of-the-art.
SMILE: Scaling Mixture-of-Experts with Efficient Bi-level Routing

The mixture of Expert (MoE) parallelism is a recent advancement that scales up the model size with constant computational cost. MoE selects different sets of parameters (i.e., experts) for each incoming token, resulting in a sparsely-activated model. Despite several successful applications of MoE, its training efficiency degrades significantly as the number of experts increases. The routing stage in MoE relies on the efficiency of the All2All communication collective, which suffers from network congestion and has poor scalability. To mitigate these issues, we introduce SMILE, which exploits heterogeneous network bandwidth and splits a single-step routing into bi-level routing. Our experimental results show that the proposed method obtains a 2.5x speedup over Switch Transformer in terms of pretraining throughput on the Colossal Clean Crawled Corpus without losing any convergence speed.