Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?

With the rise of increasingly powerful and user-facing NLP systems, there is growing interest in assessing whether they have a good _representation of uncertainty_ by evaluating the quality of their predictive distribution over outcomes. We identify two main perspectives that drive starkly different evaluation protocols. The first treats predictive probability as an indication of model confidence; the second as an indication of human label variation. We discuss their merits and limitations, and take the position that both are crucial for trustworthy and fair NLP systems, but that exploiting a single predictive distribution is limiting. We recommend tools and highlight exciting directions towards models with disentangled representations of uncertainty about predictions and uncertainty about human labels.
OAG-Bench: A Human-Curated Benchmark for Academic Graph Mining

With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often fall short in multi-aspect and fine-grained annotations, are constrained to specific task types and domains, or lack underlying real academic graphs. In this paper, we present OAG-Bench, a comprehensive, multi-aspect, and fine-grained human-curated benchmark based on the Open Academic Graph (OAG). OAG-Bench covers 10 tasks, 20 datasets, 70+ baselines, and 120+ experimental results to date. We propose new data annotation strategies for certain tasks and offer a suite of data pre-processing codes, algorithm implementations, and standardized evaluation protocols to facilitate academic graph mining. Extensive experiments reveal that even advanced algorithms like large language models (LLMs) encounter difficulties in addressing key challenges in certain tasks, such as paper source tracing and scholar profiling. We also introduce the Open Academic Graph Challenge (OAG-Challenge) to encourage community input and sharing. We envisage that OAG-Bench can serve as a common ground for the community to evaluate and compare algorithms in academic graph mining, thereby accelerating algorithm development and advancement in this field. OAG-Bench is accessible at https://www.aminer. cn/data/.
Achieving $\tilde{O}(1/\epsilon)$ Sample Complexity for Constrained Markov Decision Process

We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making. In this problem, we are given finite resources and a MDP with unknown transition probabilities. At each stage, we take an action, collecting a reward and consuming some resources, all assumed to be unknown and need to be learned over time. In this work, we take the first step towards deriving optimal problem-dependent guarantees for the CMDP problems. We derive a logarithmic regret bound, which translates into a $O(\frac{\kappa}{\epsilon}\cdot\log^2(1/\epsilon))$ sample complexity bound, with $\kappa$ being a problem-dependent parameter, yet independent of $\epsilon$. Our sample complexity bound improves upon the state-of-art $O(1/\epsilon^2)$ sample complexity for CMDP problems established in the previous literature, in terms of the dependency on $\epsilon$. To achieve this advance, we develop a new framework for analyzing CMDP problems. To be specific, our algorithm operates in the primal space and we resolve the primal LP for the CMDP problem at each period in an online manner, with \textit{adaptive} remaining resource capacities. The key elements of our algorithm are: i). an eliminating procedure that characterizes one optimal basis of the primal LP, and; ii) a resolving procedure that is adaptive to the remaining resources and sticks to the characterized optimal basis.
Monasteries and places of power in pre-Viking England: trajectories, relationships and interactions

Recent archaeological studies conducted at different scales, from the level of site through to landscapes and regions, have focused critical attention on the connections and interactions existing between secular and religious realms of life in Anglo-Saxon England. Settlement archaeology has made an important contribution to this re-evaluation by drawing attention to a series of high-status residences of the seventh-ninth centuries AD whose trajectories and lifestyle blur the boundaries between monastic and secular aristocratic culture in pre-Viking England. Recent excavations in the Kentish village of Lyminge extend an appreciation of this theme into a region which has hitherto suffered from a deficit of Anglo-Saxon settlement archaeology. Originally conceived to improve archaeological understanding of a documented pre-Viking monastery, the Lyminge Project has subsequently gone on to uncover the remains of a separate and spatially distinct royal focus – a rare example of a seventh-century ‘great hall complex’ ─ grafted onto an earlier fifth-sixth-century settlement. A provisional interpretation of these results was published in 2013, but is now possible to offer a more nuanced and richly-textured account in the light of more recent findings and radiocarbon dating. This paper draws upon these new insights to reassess the settlement sequence and to revaluate Lyminge’s wider contribution to relevant debates in early medieval studies.
Card-Based Overwriting Protocol for Equality Function and Applications

Research in the area of secure multi-party computation with an unconventional method of using a physical deck of playing cards began in 1989 when den Boar proposed a protocol to compute the logical AND function using five cards. Since then, the area has gained interest from many researchers and several card-based protocols to compute various functions have been developed. In this paper, we propose a card-based protocol called the overwriting protocol that can securely compute the $k$-candidate $n$-variable equality function $f: \{0,1,\ldots ,k-1\}^n \rightarrow \{0,1\}$. We also apply the technique used in this protocol to compute other similar functions.
Scalable Density-based Clustering with Random Projections

We present sDBSCAN, a scalable density-based clustering algorithm in high dimensions with cosine distance. Utilizing the neighborhood-preserving property of random projections, sDBSCAN can quickly identify core points and their neighborhoods, the primary hurdle of density-based clustering. Theoretically, sDBSCAN outputs a clustering structure similar to DBSCAN under mild conditions with high probability. To further facilitate sDBSCAN, we present sOPTICS, a scalable OPTICS for interactive exploration of the intrinsic clustering structure. We also extend sDBSCAN and sOPTICS to L2, L1, $\chi^2$, and Jensen-Shannon distances via random kernel features. Empirically, sDBSCAN is significantly faster and provides higher accuracy than many other clustering algorithms on real-world million-point data sets. On these data sets, sDBSCAN and sOPTICS run in a few minutes, while the scikit-learn's counterparts demand several hours or cannot run due to memory constraints.
Ultrafast and precise distance measurement via real-time chirped pulse interferometry

Laser frequency combs, which are composed of a series of equally-spaced coherent frequency components, have triggered revolutionary progress for precision spectroscopy and optical metrology. Length/distance is of fundamental importance in both science and technology. In this work, we describe a ranging scheme based on chirped pulse interferometry. In contrast to the traditional spectral interferometry, the local oscillator is strongly chirped which is able to meet the measurement pulses at arbitrary distances, and therefore the dead zones can be removed. The distances can be precisely determined via two measurement steps based on time-of-flight method and synthetic wavelength interferometry, respectively. To overcome the speed limitation of the optical spectrum analyzer, the spectrograms are stretched and detected by a fast photodetector and oscilloscope, and consequently mapped into the time domain in real time. The experimental results indicate that the measurement uncertainty can be well within 2 $\upmu$m, compared with the reference distance meter. The Allan deviation can reach 0.4 $\upmu$m at averaging time of 4 ns, 25 nm at 1 $\upmu$s, and can achieve 2 nm at 100 $\upmu$s averaging time. We also measure a spinning disk with grooves of different depths to verify the measurement speed, and the results show that the grooves with about 150 m/s line speed can be clearly captured. Our method provides a unique combination of non-dead zones, ultrafast measurement speed, high precision and accuracy, large ambiguity range, and with only one single comb source. This system could offer a powerful solution for the field measurements in practical applications in future.
Reinforcement Learning-Based Approaches for Enhancing Security and Resilience in Smart Control: A Survey on Attack and Defense Methods

Reinforcement Learning (RL), one of the core paradigms in machine learning, learns to make decisions based on real-world experiences. This approach has significantly advanced AI applications across various domains, notably in smart grid optimization and smart home automation. However, the proliferation of RL in these critical sectors has also exposed them to sophisticated adversarial attacks that target the underlying neural network policies, compromising system integrity. Given the pivotal role of RL in enhancing the efficiency and sustainability of smart grids and the personalized convenience in smart homes, ensuring the security of these systems is paramount. This paper aims to bolster the resilience of RL frameworks within these specific contexts, addressing the unique challenges posed by the intricate and potentially adversarial environments of smart grids and smart homes. We provide a thorough review of the latest adversarial RL threats and outline effective defense strategies tailored to safeguard these applications. Our comparative analysis sheds light on the nuances of adversarial tactics against RL-driven smart systems and evaluates the defense mechanisms, focusing on their innovative contributions, limitations, and the compromises they entail. By concentrating on the smart grid and smart home scenarios, this survey equips ML developers and researchers with the insights needed to secure RL applications against emerging threats, ensuring their reliability and safety in our increasingly connected world.
Sizing the bets in a focused portfolio

The paper provides a mathematical model and a tool for the focused investing strategy as advocated by Buffett, Munger, and others from this investment community. The approach presented here assumes that the investor's role is to think about probabilities of different outcomes for a set of businesses. Based on these assumptions, the tool calculates the optimal allocation of capital for each of the investment candidates. The model is based on a generalized Kelly Criterion with options to provide constraints that ensure: no shorting, limited use of leverage, providing a maximum limit to the risk of permanent loss of capital, and maximum individual allocation. The software is applied to an example portfolio from which certain observations about excessive diversification are obtained. In addition, the software is made available for public use.
InstructEdit: Instruction-based Knowledge Editing for Large Language Models

Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance. However, the current approach encounters issues with limited generalizability across tasks, necessitating one distinct editor for each task, which significantly hinders the broader applications. To address this, we take the first step to analyze the multi-task generalization issue in knowledge editing. Specifically, we develop an instruction-based editing technique, termed InstructEdit, which facilitates the editor's adaptation to various task performances simultaneously using simple instructions. With only one unified editor for each LLM, we empirically demonstrate that InstructEdit can improve the editor's control, leading to an average 14.86% increase in Reliability in multi-task editing setting. Furthermore, experiments involving holdout unseen task illustrate that InstructEdit consistently surpass previous strong baselines. To further investigate the underlying mechanisms of instruction-based knowledge editing, we analyze the principal components of the editing gradient directions, which unveils that instructions can help control optimization direction with stronger OOD generalization. Code and datasets will be available in https://github.com/zjunlp/EasyEdit.
Towards a Multi-Protocol Microscopic IVC Simulation Environment for ADASs

Most of today’s ADASs rely on the data of local perception sensors. With the introduction of Vehicle-to-X (V2X) communication, the perception range of next generation vehicles will be extended even further. In addition to the introduction of local perception sensors in Veins, we present ArteryLTE, a holistic simulation environment for using the vehicle position data from the microscopic traffic simulator SUMO in combination with ETSI ITS G5 and LTE enabled vehicles, as well as a backend. We use the proposed simulation framework to present preliminary results for detecting lane-level traffic phenomena.
FORCE CONTROL OF A NEW SEMI-ACTIVE PIEOZOELECTRIC-BASED FRICTION DAMPER

of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science FORCE CONTROL OF A NEW SEMI-ACTIVE PIEZOELECTRIC-BASED FRICTION DAMPER By Memet Unsal August 2002 Chair: Dr. Christopher Niezrecki Department: Mechanical Engineering We developed a new way to perform vibration control on a single-degree-offreedom system using a piezoelectric friction damper. The damper consists of an actuator, which is based on a piezoelectric stack with a mechanical amplifying mechanism that provides symmetric forces within the isolator. The advantages of such an actuator are its high bandwidth, actuating response and its ability to operate in vacuum environments such as in space. The damper is constrained to move using an air bearing that produces a virtually ideal single-degree-of-freedom spring-mass system. Within this work, the actuating ability of the friction-based actuator is characterized. The relationship between the force generated by the actuator and the applied voltage was found to be linear. The maximum force generated by the actuator in this study is 85 N for the specific friction pads used. xiii CHAPTER
Weight and Cardinality Constraints in ASP

Weight constraints are a powerful programming construct that has proved very useful within the Answer Set Programming paradigm. In this paper, we argue that practical Answer Set Programming might take profit from introducing some forms of nested weight constraints. We define such empowered constraints (that we call “Nested Weight Constraints”) and discuss their semantics and their complexity.
In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Background Late-stage cancer immunotherapy trials strive to demonstrate the clinical efficacy of novel immunotherapies, which is leading to exceptional responses and long-term survival in subsets of patients. To establish the clinical efficacy of an immunotherapy, it is critical to adjust the trial's design to the expected immunotherapy-specific response patterns. Methods In silico cancer immunotherapy trials are virtual clinical trials that simulate the kinetics and outcome of immunotherapy depending on the type and treatment schedule. We used an ordinary differential equation model to simulate (1) cellular interactions within the tumor microenvironment, (2) translates these into disease courses in patients, and (3) assemble populations of virtual patients to simulate in silico late-stage immunotherapy, chemotherapy, or combination trials. We predict trial outcomes and investigate how therapy-specific response patterns affect the probability of their success. Results In silico cancer immunotherapy trials reveal that immunotherapy-derived survival kinetics -- such as delayed curve separation and plateauing curve of the treatment arm -- arise naturally due to biological interactions in the tumor microenvironment. In silico clinical trials are capable of translating these biological interactions into survival kinetics. Considering four aspects of clinical trial design -- sample size calculations, endpoint and randomization rate selection, and interim analysis planning -- we illustrate that failing to consider such distinctive response patterns can significantly reduce the power of novel immunotherapy trials. Conclusion In silico trials have three significant implications for immuno-oncology. First, they provide an economical approach to verify the robustness of biological assumptions underlying an immunotherapy trial and help to scrutinize its design. Second, the biological basis of these trials facilitates and encourages communication between biomedical researchers, doctors, and trialists. Third, its application as an educational tool can illustrate design principles to scientists in training, contributing to improved designs and higher success rates of future immunotherapy trials.
Forensic Issues and Techniques to Improve Security in SSD With Flex Capacity Feature

Over-provisioning technology is typically introduced as a means to improve the performance of storage systems, such as databases. The over-provisioning area is both hidden and difficult for normal users to access. This paper focuses on attack models for such hidden areas. Malicious hackers use advanced over-provisioning techniques that vary capacity according to workload, and as such, our focus is on attack models that use variable over-provisioning technology. According to these attack models, it is possible to scan for invalid blocks containing original data or malware code that is hidden in the over-provisioning area. In this paper, we outline the different forensic processes performed for each memory cell type of the over-provisioning area and disclose security enhancement techniques that increase immunity to these attack models. This leads to a discussion of forensic possibilities and countermeasures for SSDs that can change the over-provisioning area. We also present information-hiding attacks and information-exposing attacks on the invalidation area of the SSD. Our research provides a good foundation upon which the performance and security of SSD-based databases can be further improved.
Multiscale Characterization and Quantification of Arsenic Mobilization and Attenuation During Injection of Treated Coal Seam Gas Coproduced Water into Deep Aquifers

Coal seam gas production involves generation and management of large amounts of co‐produced water. One of the most suitable methods of management is injection into deep aquifers. Field injection trials may be used to support the predictions of anticipated hydrological and geochemical impacts of injection. The present work employs reactive transport modeling (RTM) for a comprehensive analysis of data collected from a trial where arsenic mobilization was observed. Arsenic sorption behavior was studied through laboratory experiments, accompanied by the development of a surface complexation model (SCM). A field‐scale RTM that incorporated the laboratory‐derived SCM was used to simulate the data collected during the field injection trial and then to predict the long‐term fate of arsenic. We propose a new practical procedure which integrates laboratory and field‐scale models using a Monte Carlo type uncertainty analysis and alleviates a significant proportion of the computational effort required for predictive uncertainty quantification. The results illustrate that both arsenic desorption under alkaline conditions and pyrite oxidation have likely contributed to the arsenic mobilization that was observed during the field trial. The predictive simulations show that arsenic concentrations would likely remain very low if the potential for pyrite oxidation is minimized through complete deoxygenation of the injectant. The proposed modeling and predictive uncertainty quantification method can be implemented for a wide range of groundwater studies that investigate the risks of metal(loid) or radionuclide contamination.
Pre-processing and Modelling using Caret Package in R

To Implement Machine learning algorithms there are a number of Tools like python, R, Apache Mahout, Cloud-based services etc. In some tools, there are different packages that help us in data preprocessing and implementing machine learning algorithms. So, in this paper, aim to discuss how can use caret package in R software to implement machine learning techniques.
Exploring Topic-based Language Models for Effective Web Information Retrieval

The main obstacle for providing focused search is the relative opaqueness of search request -- searchers tend to express their complex information needs in only a couple of keywords. Our overall aim is to find out if, and how, topic-based language models can lead to more effective web information retrieval. In this paper we explore retrieval performance of a topic-based model that combines topical models with other language models based on cross-entropy. We first define our topical categories and train our topical models on the . GOV2 corpus by building parsimonious language models. We then test the topic-based model on TREC8 small Web data collection for ad-hoc search.Our experimental results show that the topic-based model outperforms the standard language model and parsimonious model.
Denoising and Edge-Detecting by Nonlocal-Approximate Correlated Sparsity Term

1 l norm is a popular regularizer in various linear inverse problems including image processing, compressed sensing and machine learning. But the non-zero entries of the sparsity solution obtained by 1 l are independent with each other, which always leads to biased result to real solution. Actually, there always exist some different correlations among those non-zero entries in an image signal domain or various analysis domains. In this paper, based on a simple observation that the non-zero entries of the sparsity vector in various image analysis domains should be also approximate when the relevant signal values are proximate, we proposed a nonlocal-approximate sparsity regularizer in analysis domains by minimizing the sum of the 2 l norms of those vectors with the same nonzero pattern like signal vectors. This regularizer is applied to image denoising, edge detecting, inpainting and decomposition models successively. The numerical experiments demonstrate the effectiveness of our method in terms of PSNR, visual effect and edge preserving.