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Let $R$ be a commutative Noetherian ring, $\Phi$ a system of ideals of $R$ and $M,X$ two $R$-modules. In this paper, we study the Artinianness and cofiniteness of the module $H^i_{\Phi}(M,X)$ which is an extension of the generalized local cohomology modules of Herzog., Comment: 16 pages

Large language models (LLMs) have significantly advanced the field of natural language processing, with GPT models at the forefront. While their remarkable performance spans a range of tasks, adapting LLMs for real-world business scenarios still poses challenges warranting further investigation. This paper presents an empirical analysis aimed at bridging the gap in adapting LLMs to practical use cases. To do that, we select the question answering (QA) task of insurance as a case study due to its challenge of reasoning. Based on the task we design a new model relied on LLMs which are empowered by additional knowledge extracted from insurance policy rulebooks and DBpedia. The additional knowledge helps LLMs to understand new concepts of insurance for domain adaptation. Preliminary results on two QA datasets show that knowledge enhancement significantly improves the reasoning ability of GPT-3.5 (55.80% and 57.83% in terms of accuracy). The analysis also indicates that existing public knowledge bases, e.g., DBPedia is beneficial for knowledge enhancement. Our findings reveal that the inherent complexity of business scenarios often necessitates the incorporation of domain-specific knowledge and external resources for effective problem-solving., Comment: Ongoing work to adapt LLMs for business scenarios

Portfolio selection involves optimizing simultaneously financial goals such as risk, return and Sharpe ratio. This problem holds considerable importance in economics. However, little has been studied related to the nonconvexity of the objectives. This paper proposes a novel generalized approach to solve the challenging Portfolio Selection problem in an intuitionistic fuzzy environment where the objectives are soft pseudoconvex functions, and the constraint set is convex. Specifically, we utilize intuitionistic fuzzy theory and flexible optimization to transform the fuzzy pseudoconvex multicriteria vector into a pseudoconvex programming problem that can be solved by recent gradient descent methods. We demonstrate that our method can be applied broadly without special forms on membership and nonmembership functions as in previous works. Computational experiments on real-world scenarios are reported to show the effectiveness of our method., Comment: 10 pages

Aligning large language models (LLMs) with human preferences has proven to drastically improve usability and has driven rapid adoption as demonstrated by ChatGPT. Alignment techniques such as supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF) greatly reduce the required skill and domain knowledge to effectively harness the capabilities of LLMs, increasing their accessibility and utility across various domains. However, state-of-the-art alignment techniques like RLHF rely on high-quality human feedback data, which is expensive to create and often remains proprietary. In an effort to democratize research on large-scale alignment, we release OpenAssistant Conversations, a human-generated, human-annotated assistant-style conversation corpus consisting of 161,443 messages in 35 different languages, annotated with 461,292 quality ratings, resulting in over 10,000 complete and fully annotated conversation trees. The corpus is a product of a worldwide crowd-sourcing effort involving over 13,500 volunteers. Models trained on OpenAssistant Conversations show consistent improvements on standard benchmarks over respective base models. We release our code and data under a fully permissive licence., Comment: Published in NeurIPS 2023 Datasets and Benchmarks

The retrieval of 3D objects has gained significant importance in recent years due to its broad range of applications in computer vision, computer graphics, virtual reality, and augmented reality. However, the retrieval of 3D objects presents significant challenges due to the intricate nature of 3D models, which can vary in shape, size, and texture, and have numerous polygons and vertices. To this end, we introduce a novel SHREC challenge track that focuses on retrieving relevant 3D animal models from a dataset using sketch queries and expedites accessing 3D models through available sketches. Furthermore, a new dataset named ANIMAR was constructed in this study, comprising a collection of 711 unique 3D animal models and 140 corresponding sketch queries. Our contest requires participants to retrieve 3D models based on complex and detailed sketches. We receive satisfactory results from eight teams and 204 runs. Although further improvement is necessary, the proposed task has the potential to incentivize additional research in the domain of 3D object retrieval, potentially yielding benefits for a wide range of applications. We also provide insights into potential areas of future research, such as improving techniques for feature extraction and matching and creating more diverse datasets to evaluate retrieval performance. https://aichallenge.hcmus.edu.vn/sketchanimar, Comment: Accepted to Computers & Graphics (3DOR 2023, Journal track)

3D object retrieval is an important yet challenging task that has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe this task can potentially drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from fully solved. As such, we provide insights into potential areas for future research and improvements. We believe we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies. https://aichallenge.hcmus.edu.vn/textanimar, Comment: Accepted to Computers and Graphics (3DOR, Journal Track)

In order to satisfy diverse quality-of-service (QoS) requirements of complex real-time video applications, civilian and tactical use cases are employing software-defined hybrid edge-cloud systems. One of the primary QoS requirements of such applications is ultra-low end-to-end latency for video applications that necessitates rapid frame transfer between end-devices and edge servers using software-defined networking (SDN). Failing to guarantee such strict requirements leads to quality degradation of video applications and subsequently mission failure. In this paper, we show how a collaborative group of attackers can exploit SDN's control communications to launch Denial of Quality of Service (DQoS) attack that artificially increases end-to-end latency of video frames and yet evades detection. In particular, we show how Deep Neural Network (DNN) model training on all or partial network state information can help predict network packet drop rates with reasonable accuracy. We also show how such predictions can help design an attack model that can inflict just the right amount of added latency to the end-to-end video processing that is enough to cause considerable QoS degradation but not too much to raise suspicion. We use a realistic edge-cloud testbed on GENI platform for training data collection and demonstration of high model accuracy and attack success rate., Comment: WAMICON 2022

In this article, we study the existence of non-trivial weak solutions for the following boundary-value problem \begin{gather*} -\frac{\partial^2 u}{\partial x^2} -\left|x\right|^{2k}\frac{\partial^2 u}{\partial y^2}=f(x,y,u) \quad\text{ in }\Omega, \ u=0 \quad\text{ on }\partial\Omega, \end{gather*} where $\Omega$ is a bounded domain with smooth boundary in $\mathbb{R}^2, \Omega \cap \{x=0\}\ne \emptyset,$ $k >0,$ $f(x,y,0)=0. $, Comment: 14 pages

We present a cascaded diffusion model based on a part-level implicit 3D representation. Our model achieves state-of-the-art generation quality and also enables part-level shape editing and manipulation without any additional training in conditional setup. Diffusion models have demonstrated impressive capabilities in data generation as well as zero-shot completion and editing via a guided reverse process. Recent research on 3D diffusion models has focused on improving their generation capabilities with various data representations, while the absence of structural information has limited their capability in completion and editing tasks. We thus propose our novel diffusion model using a part-level implicit representation. To effectively learn diffusion with high-dimensional embedding vectors of parts, we propose a cascaded framework, learning diffusion first on a low-dimensional subspace encoding extrinsic parameters of parts and then on the other high-dimensional subspace encoding intrinsic attributes. In the experiments, we demonstrate the outperformance of our method compared with the previous ones both in generation and part-level completion and manipulation tasks., Comment: Project page: https://salad3d.github.io

The I-MMSE formula connects two important quantities in information theory and estimation theory: the mutual information and the minimum mean-squared error (MMSE). It states that in a scalar Gaussian channel, the derivative of the mutual information with respect to the signal-to-noise ratio (SNR) is one-half of the MMSE. Although any derivative at a fixed order can be computed in principle, a general formula for all the derivatives is still unknown. In this paper, we derive this general formula for vector Gaussian channels. The obtained result is remarkably similar to the classic cumulant-moment relation in statistical theory.

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety.

The article considers semi-supervised multitask learning on a Gaussian mixture model (GMM). Using methods from statistical physics, we compute the asymptotic Bayes risk of each task in the regime of large datasets in high dimension, from which we analyze the role of task similarity in learning and evaluate the performance gain when tasks are learned together rather than separately. In the supervised case, we derive a simple algorithm that attains the Bayes optimal performance., Comment: AISTATS 2023

Inference algorithms for probabilistic programming are complex imperative programs with many moving parts. Efficient inference often requires customising an algorithm to a particular probabilistic model or problem, sometimes called inference programming. Most inference frameworks are implemented in languages that lack a disciplined approach to side effects, which can result in monolithic implementations where the structure of the algorithms is obscured and inference programming is hard. Functional programming with typed effects offers a more structured and modular foundation for programmable inference, with monad transformers being the primary structuring mechanism explored to date. This paper presents an alternative approach to inference programming based on algebraic effects. Using effect signatures to specify the key operations of the algorithms, and effect handlers to modularly interpret those operations for specific variants, we develop two abstract algorithms, or inference patterns, representing two important classes of inference: Metropolis-Hastings and particle filtering. We show how our approach reveals the algorithms' high-level structure, and makes it easy to tailor and recombine their parts into new variants. We implement the two inference patterns as a Haskell library, and discuss the pros and cons of algebraic effects vis-a-vis monad transformers as a structuring mechanism for modular imperative algorithm design.

This study proposes a new distributed control method based on an adaptive fuzzy control for multiple collaborative autonomous underwater vehicles (AUVs) to track a desired formation shape within a fixed time. First, a formation control protocol based on a fixed-time backstepping sliding mode control is designed, in which the consensus cooperative tracking errors for each AUV will be formulated. Then, to compensate for the saturated control torques, an adaptive auxiliary variable is introduced. Finally, a fixed-time adaptive fuzzy logic control (FLC) is derived to approximate the unknown dynamics, in which the adaptive laws of the FLC is derived such that the adaptive signals and errors can be convergent within a fixed time. The fixed time convergence is desired in practice because it provides an exciting property that the global convergence of the whole system is independent with the initial states of the AUVs. The computer simulation results for a consensus formation control of four AUVs show that the proposed formation control can provide high tracking performance with lower and smoother control efforts

We report the first measurement of polarized thermal dust emission toward the entire early and massive Infrared Dark Cloud G11.11$-$0.12 taken by the polarimeter SOFIA/HAWC+ at 214 $\mu m$ wavelength. Magnetic fields (B-fields) obtained from the polarized emission tend to be perpendicular to the filament's spine. We produce a map of B-field strengths for the center region of the filament. The strengths vary in the range of 100-600 $\mu\rm{G}$ and are strongest along the filament's spine. The central region is sub-Alfv\'enic and mostly sub-critical meaning that B-fields dominate over turbulence and are strong enough to resist gravitational collapse. The alignment and properties of dust grains are studied in the filament using the RAdiative Torque (RAT) theory. We find the decrease of polarization degree $P$ with emission intensity $I$, i.e., depolarization effect, of the form $P\propto I^{-\alpha}$ with $\alpha\sim$0.8-0.9, implying a significant loss of grain alignment in the filament's spine. The depolarization can be explained by the decrease in RAT alignment efficiency toward the denser regions with lower dust temperature, and cannot be explained by the B-field tangling. We study the effect of the enhanced magnetic relaxation by embedded iron inclusions on RAT alignment and find that the high polarization fraction $P\sim$20-30\% in the outer layer of the filament is potential evidence for the enhanced RAT alignment by magnetic relaxation. This is the first time this effect is evaluated in a filament. Based on the polarization fraction and RAT alignment theory, we find evidence for grain growth in the filament.

In this paper, we study temporal graphs arising from mobility models, where vertices correspond to agents moving in space and edges appear each time two agents meet. We propose a rather natural one-dimensional model. If each pair of agents meets exactly once, we get a simple temporal clique where the edges are ordered according to meeting times. In order to characterize which temporal cliques can be obtained as such `mobility graphs', we introduce the notion of forbidden patterns in temporal graphs. Furthermore, using a classical result in combinatorics, we count the number of such mobility cliques for a given number of agents, and show that not every temporal clique resulting from the 1D model can be realized with agents moving with different constant speeds. For the analogous circular problem, where agents are moving along a circle, we provide a characterization via circular forbidden patterns. Our characterization in terms of forbidden patterns can be extended to the case where each edge appears at most once. We also study the problem where pairs of agents are allowed to cross each other several times, using an approach from automata theory. We observe that in this case, there is no finite set of forbidden patterns that characterize such temporal graphs and nevertheless give a linear-time algorithm to recognize temporal graphs arising from this model.

In high-power applications, multiple power MOSFETs are connected in parallel and treated as a single switch in order to handle much larger total currents. In this paper, a parallel power MOSFETs model from the turnoff state until they reach their steady state is introduced. The model represents the relationship between each power MOSFET's gate voltage and the current distribution among them. The study's key purpose is to use the model for dealing with the asymmetry in sharing current and power loss between these semiconductor devices during the steady state region., Comment: 10 pages, 7 figures, The 2023 INTERNATIONAL SYMPOSIUM ON ADVANCED ENGINEERING (ISAE2023)

We develop an improved stochastic formalism for the Bethe-Salpeter equation, based on an exact separation of the effective-interaction $W$ to two parts, $W=(W-v_W)+v_W$ where the latter is formally any translationally-invariant interaction $v_W(r-r')$. When optimizing the fit of $v_W$ exchange kernel to $W$, by using a stochastic sampling of $W$, the difference $W-v_W$ becomes quite small. Then, in the main BSE routine, this small difference is stochastically sampled. The number of stochastic samples needed for an accurate spectrum is then largely independent of system size. While the method is formally cubic in scaling, the scaling prefactor is small due to the constant number of stochastic orbitals needed for sampling $W$., Comment: 9 pages, 5 figures, 2 tables

Quantum Key Distribution (QKD) is considered the most immediate application to be widely implemented amongst a variety of potential quantum technologies. QKD enables sharing secret keys between distant users, using photons as information carriers. An ongoing endeavour is to implement these protocols in practice in a robust, and compact manner so as to be efficiently deployable in a range of real-world scenarios. Single Photon Sources (SPS) in solid-state materials are prime candidates in this respect. Here, we demonstrate a room temperature, discrete-variable quantum key distribution system using a bright single photon source in hexagonal-boron nitride, operating in free-space. Employing an easily interchangeable photon source system, we have generated keys with one million bits length, and demonstrated a secret key of approximately 70,000 bits, at a quantum bit error rate of 6%, with $\varepsilon$-security of $10^{-10}$. Our work demonstrates the first proof of concept finite-key BB84 QKD system realised with hBN defects.

The time-dependent Mandel Q parameter, Q(T), provides a measure of photon number variance for a light source as a function of integration time. Here, we use Q(T) to characterise single photon emission from a quantum emitter in hexagonal boron nitride (hBN). Under pulsed excitation a negative Q parameter was measured, indicating photon antibunching at an integration time of 100 ns. For larger integration times Q is positive and the photon statistics become super-Poissonian, and we show by comparison with a Monte Carlo simulation for a three-level emitter that this is consistent with the effect of a metastable shelving state. Looking towards technological applications for hBN single photon sources, we propose that Q(T) provides valuable information on the intensity stability of single photon emission. This is useful in addition to the commonly used $g^{(2)}({\tau})$ function for the complete characterisation of a hBN emitter., Comment: Main text: 9 pages, 5 figures. Supplemental document: 6 pages, 8 figures. Submitted to Optics Express

We consider a variant of the Seiberg-Witten equations for multiple-spinors. The moduli space of solutions to our generalized Seiberg-Witten equations in the setting of K\"ahler surfaces has a direct relation with ASD connections of holomorphic vector bundle. Also in K\"ahler setting, we construct a numerical invariant from the equations that detects a notion of $\phi-$stability of $SU(n)-$holomorphic vector bundles where $\phi$ is some prescribed non-trivial holomorphic section., Comment: An important technical mistake in the set-up of this variant of generalization to Seiberg-Witten equations was pointed out to the author. In particular, the equations are not elliptic as claimed. As a result, any statement about (or uses) regularity and transversality of the moduli space has to be disregarded. However, the moduli space is still compact

In this note, we present a proof of Donaldson's Diagonalization Theorem via an abelian gauge-theoretic variant of the Seiberg-Witten equations for multiple spinors. Like the other proof of Donaldson's theorem using the standard Seiberg-Witten theory, Elkies' theorem also plays a key role in our argument., Comment: The proof of the theorem in the notes relies on the results stated in our other posting arXiv:2301.09693. Specifically, one of the ingredients needed is transversality. It is pointed out to us that due to a technicality in our set-up, the system of equations that we consider is not elliptic. Hence, we should not expect transversality in this set-up

Social media networks commonly employ content moderation as a tool to limit the spread of harmful content. However, the efficacy of this strategy in limiting the delivery of harmful content to users is not well understood. In this paper, we create a framework to quantify the efficacy of content moderation and use our metrics to analyze content removal on Facebook within the U.S. news ecosystem. In a data set of over 2M posts with 1.6B user engagements collected from 2,551 U.S. news sources before and during the Capitol Riot on January 6, 2021, we identify 10,811 removed posts. We find that the active engagement life cycle of Facebook posts is very short, with 90% of all engagement occurring within the first 30 hours after posting. Thus, even relatively quick intervention allowed significant accrual of engagement before removal, and prevented only 21% of the predicted engagement potential during a baseline period before the U.S. Capitol attack. Nearly a week after the attack, Facebook began removing older content, but these removals occurred so late in these posts' engagement life cycles that they disrupted less than 1% of predicted future engagement, highlighting the limited impact of this intervention. Content moderation likely has limits in its ability to prevent engagement, especially in a crisis, and we recommend that other approaches such as slowing down the rate of content diffusion be investigated.

Screening of unknown particles, including viruses and nanoparticles, is key in medicine, industry and pollutant determination. However, existing techniques require sample a priori knowledge or modification (e.g. fluorescence). Here we introduce RYtov MIcroscopy for Nanoparticles Identification (RYMINI), a noninvasive and non-destructive optical approach that is combining holographic labelfree 3D tracking and high-sensitivity quantitative phase imaging into a compact optical setup. Dedicated to the characterization of nano-objects in solution, it is compatible with highly demanding environments such as level-3 biological laboratories. Metrological characterization has been performed at the level of each single object on both absorbing and transparent particles as well as on infectious HIV-1, SARS-CoV-2 and extracellular vesicles in solution. We demonstrate the capability of RYMINI to determine the nature, concentration, size, complex refractive index and mass of each single particle. We discuss the application of the method in unknown solution without requiring any knowledge or model of the particles' response. It paves the way to label-free nano-object identification in terra incognita.