Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained optimal stopping under a regime-switching model

We investigate an optimal stopping problem for the expected value of a discounted payoﬀ on a regime-switching geometric Brownian motion under two constraints on the possible stopping times: only at exogenous random times and only during a speciﬁc regime. The main objectives are to show that an optimal stopping time exists as a threshold type under some boundary conditions and to derive expressions of the value functions and the optimal threshold. To this end, we solve the corresponding variational inequality and show that its solution coincides with the value functions. Some numerical results are also introduced. Furthermore, we investigate some asymptotic behaviors.
Theoretical and experimental investigation of 3D-structured surface generation by computer controlled ultra-precision polishing

i Abstract of thesis entitled ‘Theoretical and Experimental Investigation of 3D-Structured Surface Generation by Computer Controlled Ultra-Precision Polishing’ submitted by Ho Lai Ting for the degree of Doctor of Philosophy at The Hong Kong Polytechnic University Three-dimensional (3D) structured surfaces have been widely used in different applications such as self-adhesive sensors, compound lenses for holography in phonics products, bionic applications, bio-medical areas, etc. Although there have been some research studies on the manufacture and machining of 3D-structured surfaces, the research work is still far from complete. The current fabricating process, for instance, laser and etching, for 3D-structured surface generation are expensive and time-consuming. Computer Controlled Ultra-precision Polishing (CCUP) is capable of fabricating ultra-precision freeform surfaces with sub-micrometre form accuracy and surface roughness in the nanometre range. CCUP consists of Mechanical Polishing (MP) and Fluid Jet Polishing (FJP) processes. Although polishing is a historic machining process, research on the polishing mechanics and surface generation during the polishing process is still far from complete. Polishing is a multi-step process that involves a sequence of steps with different process and material parameters. “What parameters should be used for the next step” is always a question asked by operators. They make their decisions by measuring the surface after each polishing step. Moreover, the achievement of desirable polished surfaces still depends largely on the expensive and time-consuming trial-and-error approach when new materials, new designs of 3D-structured surfaces or new machine tools are used. An appropriate
Characterization of devices fabricated in films grown at low temperature by atmospheric pressure CVD

First electrical device results are reported for a novel low-temperature epitaxial deposition process, ultra-clean atmospheric pressure chemical vapor deposition (APCVD). APCVD has been used to grow blanket and selective epitaxial films of Si and Si/sub 1-x/Ge/sub x/ alloys without facets. Films have been grown with controllable n- and p-dopant profiles in reactors which include a commercial 125-200-mm single-wafer processor with integrated preclean capability, a high-temperature hydrogen preanneal. Large area (10/sup 5/ mu m/sup 2/) p-n junctions fabricated in epitaxial n- and p-type Si grown at a temperature as low as 625 degrees C show ideal behavior and low reverse leakage (1 fA/ mu m/sup 2/) at 5 V. MOS capacitors fabricated in epitaxial Si films show C-V and oxide breakdown characteristics comparable to those of devices fabricated in bulk substrates. These results have promising implications for the implementation of device-quality low-temperature epitaxy in a CVD reactor without vacuum pumps. <<ETX>>
Generalized b-Symbol Weights of Linear Codes and b-Symbol MDS Codes

Generalized pair weights of linear codes are generalizations of minimum symbol-pair weights, which were introduced by Liu and Pan (2022) recently. Generalized pair weights can be used to characterize the ability of protecting information in the symbol-pair read wire-tap channels of type II. In this paper, we introduce the notion of generalized <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-symbol weights of linear codes over finite fields, which is a generalization of generalized Hamming weights and generalized pair weights. We obtain some basic properties and bounds of generalized <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-symbol weights which are called Singleton-like bounds for generalized <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-symbol weights. As examples, we calculate the generalized weight matrices for simplex codes and Hamming codes. We provide a necessary and sufficient condition for a linear code to be a <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-symbol MDS code by using the generator matrix and the parity check matrix of this linear code. Finally, a necessary and sufficient condition of a linear isomorphism preserving <inline-formula> <tex-math notation="LaTeX">$b$ </tex-math></inline-formula>-symbol weights between two linear codes is obtained. As a corollary, we get the classical MacWilliams extension theorem when <inline-formula> <tex-math notation="LaTeX">$b=1$ </tex-math></inline-formula>.
IsoScore: Measuring the Uniformity of Embedding Space Utilization

The recent success of distributed word representations has led to an increased interest in analyzing the properties of their spatial distribution. Several studies have suggested that contextualized word embedding models do not isotropically project tokens into vector space. However, current methods designed to measure isotropy, such as average random cosine similarity and the partition score, have not been thoroughly analyzed and are not appropriate for measuring isotropy. We propose IsoScore: a novel tool that quantifies the degree to which a point cloud uniformly utilizes the ambient vector space. Using rigorously designed tests, we demonstrate that IsoScore is the only tool available in the literature that accurately measures how uniformly distributed variance is across dimensions in vector space. Additionally, we use IsoScore to challenge a number of recent conclusions in the NLP literature that have been derived using brittle metrics of isotropy. We caution future studies from using existing tools to measure isotropy in contextualized embedding space as resulting conclusions will be misleading or altogether inaccurate.
Learning From Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing

Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing. The evolving network architecture requires the orchestration of multiple network resources such as radio and cache resources. In recent years, machine learning (ML) techniques have been widely applied for network management. However, most existing works do not take advantage of the knowledge transfer capability in ML. In this paper, we propose a deep transfer reinforcement learning (DTRL) scheme for joint radio and cache resource allocation to serve 5G RAN slicing. We first define a hierarchical architecture for joint resource allocation. Then we propose two DTRL algorithms: Q-value-based deep transfer reinforcement learning (QDTRL) and action selection-based deep transfer reinforcement learning (ADTRL). In the proposed schemes, learner agents utilize expert agents’ knowledge to improve their performance on current tasks. The proposed algorithms are compared with both the model-free exploration bonus deep Q-learning (EB-DQN) and the model-based priority proportional fairness and time-to-live (PPF-TTL) algorithms. Compared with EB-DQN, our proposed DTRL-based method presents 21.4% lower delay for Ultra Reliable Low Latency Communications (URLLC) slice and 22.4% higher throughput for enhanced Mobile Broad Band (eMBB) slice, while achieving significantly faster convergence than EB-DQN. Moreover, 40.8% lower URLLC delay and 59.8% higher eMBB throughput are observed with respect to PPF-TTL.
Learning Adversarially Robust Policies in Multi-Agent Games

Robust decision-making in multiplayer games requires anticipating what reactions a player policy may elicit from other players. This is difﬁ-cult in games with three or more players: when one player ﬁxes a policy, it induces a subgame between the remaining players with many possible equilibria outcomes. Predicting the worst-case outcome of a policy is thus an equilibrium selection problem—one known to be generally NP-Hard. We show that worst-case coarse-correlated equilibria can be efﬁciently approximated in smooth games and propose a framework that uses the worst-case evaluation scheme to learn robust player policies. We further prove the framework can be extended to handle uncertainty about the bounded rationality of other players. In experiments, our framework learns robust policies in repeated N -player matrix games and, when applied to deep multi-agent reinforcement learning, can scale to complex spatiotemporal games. For example, it learns robust AI tax policies that improve welfare by up to 15%, even when taxpayers are boundedly rational.
First results in the development of a mobile robot with trajectory planning and object recognition capabilities

Abstract The use of mobile robots is becoming popular in many areas of service because they ensure safety and good performance while working in dangerous or unreachable locations. Areas of application of mobile robots differ from educational research to detection of bombs and their disposal. Based on the mission of the robot they have different configurations and abilities – some of them have additional arms, cranes and other tools, others use sensors and built-in image processing and object recognition systems to perform their missions. The robot that is described in this paper is mobile robot with a turret mounted on top of it. Different approaches have been tested while searching for best method suitable for image processing and template matching goals. Based on the information from image processing unit the system executes appropriate actions for planning motions and trajectory of the mobile robot.
Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED

DocRED is a widely used dataset for document-level relation extraction. In the large-scale annotation, a recommend-revise scheme is adopted to reduce the workload. Within this scheme, annotators are provided with candidate relation instances from distant supervision, and they then manually supplement and remove relational facts based on the recommendations. However, when comparing DocRED with a subset relabeled from scratch, we find that this scheme results in a considerable amount of false negative samples and an obvious bias towards popular entities and relations. Furthermore, we observe that the models trained on DocRED have low recall on our relabeled dataset and inherit the same bias in the training data. Through the analysis of annotators’ behaviors, we figure out the underlying reason for the problems above: the scheme actually discourages annotators from supplementing adequate instances in the revision phase. We appeal to future research to take into consideration the issues with the recommend-revise scheme when designing new models and annotation schemes. The relabeled dataset is released at https://github.com/AndrewZhe/Revisit-DocRED, to serve as a more reliable test set of document RE models.
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction

Existing leading methods for spectral reconstruction (SR) focus on designing deeper or wider convolutional neural networks (CNNs) to learn the end-to-end mapping from the RGB image to its hyperspectral image (HSI). These CNN-based methods achieve impressive restoration performance while showing limitations in capturing the long-range dependencies and self-similarity prior. To cope with this problem, we propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++), for efficient spectral reconstruction. In particular, we employ Spectral-wise Multi-head Self-attention (S-MSA) that is based on the HSI spatially sparse while spectrally self-similar nature to compose the basic unit, Spectral-wise Attention Block (SAB). Then SABs build up Single-stage Spectral-wise Transformer (SST) that exploits a U-shaped structure to extract multi-resolution contextual information. Finally, our MST++, cascaded by several SSTs, progressively improves the reconstruction quality from coarse to fine. Comprehensive experiments show that our MST++ significantly outperforms other state-of-the-art methods. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place. Code and pre-trained models are publicly available at https://github.com/caiyuanhao1998/MST-plus-plus.
Wound Severity Classification using Deep Neural Network

The classification of wound severity is a critical step in wound diagnosis. An effective classifier can help wound professionals categorize wound conditions more quickly and affordably, allowing them to choose the best treatment option. This study used wound photos to construct a deep neural network-based wound severity classifier that classified them into one of three classes: green, yellow, or red. The green class denotes wounds still in the early stages of healing and are most likely to recover with adequate care. Wounds in the yellow category require more attention and treatment than those in the green category. Finally, the red class denotes the most severe wounds that require prompt attention and treatment. A dataset containing different types of wound images is designed with the help of wound specialists. Nine deep learning models are used with applying the concept of transfer learning. Several stacked models are also developed by concatenating these transfer learning models. The maximum accuracy achieved on multi-class classification is 68.49%. In addition, we achieved 78.79%, 81.40%, and 77.57% accuracies on green vs. yellow, green vs. red, and yellow vs. red classifications for binary classifications. With a learning rate of 0.001 and an Adam optimizer, all models were trained for 250 epochs. Two callbacks were employed with the best validation accuracy and the optimal combination of validation and training accuracy saving. The " sparse_categorical_crossentropy " and " binary_crossentropy " loss functions were used for multi-class and binary classification, respectively.
Dynamic testing and continuous monitoring of an arch bridge built in 1940

Foz-Tua roadway bridge is a concrete arch bridge built in 1940 at the northeast part of Portugal. Owing to the present construction of a new hydroelectric power plant close to the bridge site, which involves blasting operations, the circulation of heavy trucks and a deepening of the river bed, the owner of the dam (EDP Produção) required the installation of a vibration based Structural Health Monitoring system in the bridge, in order to observe if the construction activities would interfere with the structural integrity of this structure. In this context, this paper describes the dynamic tests and the continuous monitoring activity recently developed by the Laboratory of Vibrations and Structural Monitoring (ViBest, www.fe.up.pt/vibest) of FEUP in order to achieve the main objective defined by the owner of the dam. The processing of the data continuously collected by the SHM system is performed with the DynaMo software and includes the online automatic identification of the bridge modal parameters using state of the art algorithms.
Geometrical Bounds on Irreversibility in Squeezed Thermal Bath

Irreversible entropy production (IEP) plays an important role in quantum thermodynamic processes. Here, we investigate the geometrical bounds of IEP in nonequilibrium thermodynamics by exemplifying a system coupled to a squeezed thermal bath subject to dissipation and dephasing, respectively. We find that the geometrical bounds of the IEP always shift in a contrary way under dissipation and dephasing, where the lower and upper bounds turning to be tighter occur in the situation of dephasing and dissipation, respectively. However, either under dissipation or under dephasing, we may reduce both the critical time of the IEP itself and the critical time of the bounds for reaching an equilibrium by harvesting the benefits of squeezing effects in which the values of the IEP, quantifying the degree of thermodynamic irreversibility, also become smaller. Therefore, due to the nonequilibrium nature of the squeezed thermal bath, the system–bath interaction energy has a prominent impact on the IEP, leading to tightness of its bounds. Our results are not contradictory with the second law of thermodynamics by involving squeezing of the bath as an available resource, which can improve the performance of quantum thermodynamic devices.
Deterministic Low-Diameter Decompositions for Weighted Graphs and Distributed and Parallel Applications

This paper presents new deterministic and distributed low-diameter decomposition algorithms for weighted graphs. In particular, we show that if one can efficiently compute approximate distances in a parallel or a distributed setting, one can also efficiently compute low-diameter decompositions. This consequently implies solutions to many fundamental distance based problems using a polylogarithmic number of approximate distance computations.Our low-diameter decomposition generalizes and extends the line of work starting from [RG20] to weighted graphs in a very model-independent manner. Moreover, our clustering results have additional useful properties, including strong-diameter guarantees, separation properties, restricting cluster centers to specified terminals, and more. Applications include:–The first near-linear work and polylogarithmic depth randomized and deterministic parallel algorithm for low-stretch spanning trees (LSST) with polylogarithmic stretch. Previously, the best parallel LSST algorithm required $m.n^{o(1)}$ work and $n^{o(1)}$ depth and was inherently randomized. No deterministic LSST algorithm with truly sub-quadratic work and sub-linear depth was known. –The first near-linear work and polylogarithmic depth deterministic algorithm for computing an $\ell_{1}-$embedding into polylogarithmic dimensional space with polylogarithmic distortion. The best prior deterministic algorithms for $\ell_{1}$-embeddings either require large polynomial work or are inherently sequential.Even when we apply our techniques to the classical problem of computing a ball-carving with strong-diameter $O(\log^{2}n)$ in an unweighted graph, our new clustering algorithm still leads to an improvement in round complexity from $O(\log^{10}n)$ rounds [CG21] to $O(\log^{4}n)$.
Iterative Complexity and Its Applications of Linearized Generalized Alternating Direction Method of Multipliers with Multi-block Case

We consider a multi-block separable convex optimization problem with the linear constraints, where the objective function is the sum of m individual convex functions without overlapping variables. The linearized version of the generalized alternating direction method of multipliers (L-GADMM) is particularly eﬃcient for the two-block separable convex programming problem and its convergence was proved when two blocks of variables are alternatively updated. However, the convergence and some practical applications of the extension ( m ≥ 3) of the L-GADMM is still in its infancy. In this paper, we extend this algorithm to the general case where the objective function consists of the sum of m -block convex functions. Theoretically, we prove global convergence of the new method and establish the worst-case convergence rate in the ergodic and nonergodic senses for the proposed algorithm. The eﬃciency of the new method is further demonstrated through numerical results on the calibration of the correlation matrices. correlation
Dec-AltProjGDmin: Fully-Decentralized Alternating Projected Gradient Descent for Low Rank Column-wise Compressive Sensing

This work develops a fully-decentralized alternating projected gradient descent algorithm, called Dec-AltProjGDmin, for solving the following low-rank (LR) matrix recovery problem: recover an LR matrix from independent columnwise linear projections (LR column-wise Compressive Sensing). We prove its correctness under simple assumptions and argue that Dec-AltProjGDmin is both faster and more communication-efficient than various other potential solution approaches, in addition to also having one of the best sample complexity guarantees. To our best knowledge, this work is the first attempt to develop a provably correct fully-decentralized algorithm for any problem involving the use of an alternating projected GD algorithm when the constraint set (the set to be projected onto) is non-convex.
Noether charge formalism for Weyl transverse gravity

Weyl transverse gravity (WTG) is a gravitational theory that is invariant under transverse diffeomorphisms and Weyl transformations. It is characterised by having the same classical solutions as general relativity while solving some of its issues with the cosmological constant. In this work, we first find the Noether currents and charges corresponding to local symmetries of WTG as well as a prescription for the symplectic form. We then employ these results to derive the first law of black hole mechanics in WTG (both in vacuum and in the presence of a perfect fluid), identifying the total energy, the total angular momentum, and the Wald entropy of black holes. We further obtain the first law and Smarr formula for Schwarzschild-anti-de Sitter and pure de Sitter spacetimes, discussing the contributions of the varying cosmological constant, which naturally appear in WTG. Lastly, we derive the first law of causal diamonds in vacuum.
Towards Unification of Discourse Annotation Frameworks

Discourse information is difficult to represent and annotate. Among the major frameworks for annotating discourse information, RST, PDTB and SDRT are widely discussed and used, each having its own theoretical foundation and focus. Corpora annotated under different frameworks vary considerably. To make better use of the existing discourse corpora and achieve the possible synergy of different frameworks, it is worthwhile to investigate the systematic relations between different frameworks and devise methods of unifying the frameworks. Although the issue of framework unification has been a topic of discussion for a long time, there is currently no comprehensive approach which considers unifying both discourse structure and discourse relations and evaluates the unified framework intrinsically and extrinsically. We plan to use automatic means for the unification task and evaluate the result with structural complexity and downstream tasks. We will also explore the application of the unified framework in multi-task learning and graphical models.