Miracle-dz/UniFAR-FTUs huggingface dataset:
From STL Rulebooks to Rewards

The automatic synthesis of neural-network controllers for autonomous agents through reinforcement learning has to simultaneously optimize many, possibly conﬂicting, objectives of various importance. This multi-objective optimization task is reﬂected in the shape of the reward function, which is most often the result of an ad-hoc and crafty-like activity. In this paper we propose a principled approach to shaping rewards for reinforcement learning from multiple objectives that are given as a partially-ordered set of signal-temporal-logic (STL) rules. To this end, we ﬁrst equip STL with a novel quantitative semantics allowing to automatically evaluate individual requirements. We then develop a method for systematically combining evaluations of multiple requirements into a single reward that takes into account the priorities deﬁned by the partial order. We ﬁnally evaluate our approach on several case studies, demonstrating its practical applicability.
T-SNE Is Not Optimized to Reveal Clusters in Data

Cluster visualization is an essential task for nonlinear dimensionality reduction as a data analysis tool. It is often believed that Student t-Distributed Stochastic Neighbor Embedding (t-SNE) can show clusters for well clusterable data, with a smaller Kullback-Leibler divergence corresponding to a better quality. There was even theoretical proof for the guarantee of this property. However, we point out that this is not necessarily the case -- t-SNE may leave clustering patterns hidden despite strong signals present in the data. Extensive empirical evidence is provided to support our claim. First, several real-world counter-examples are presented, where t-SNE fails even if the input neighborhoods are well clusterable. Tuning hyperparameters in t-SNE or using better optimization algorithms does not help solve this issue because a better t-SNE learning objective can correspond to a worse cluster embedding. Second, we check the assumptions in the clustering guarantee of t-SNE and find they are often violated for real-world data sets.
DNN-Assisted Particle-Based Bayesian Joint Synchronization and Localization

In this work, we propose a Deep neural network-assisted Particle Filter-based (DePF) approach to address the Mobile User (MU) joint synchronization and localization (sync&loc) problem in ultra-dense networks. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the Access Points (APs), which, traditionally, provides us with information about the MUs’ clock offset and skew. However, information about the distance between an AP and an MU is also intrinsic to the propagation delay experienced by the exchanged time-stamps. In addition, to estimate the angle of arrival of the received synchronization packets, DePF draws on the multiple signal classification algorithm that is fed with the Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS. Finally, to perform joint sync&loc, DePF capitalizes on particle Gaussian mixtures that allow for a hybrid particle-based and parametric Bayesian Recursive Filtering (BRF) fusion of the aforementioned pieces of information and, thus, jointly estimates the position and clock parameters of the MUs. The simulation results verify the superiority of the proposed algorithm over the state-of-the-art schemes, especially that of the extended Kalman filter- and linearized BRF-based joint sync&loc. In particular, only drawing on the synchronization time-stamp exchange and CIRs from a single AP, for 90% of the cases, the absolute position and clock offset estimation error remain below 1 meter and 2 nanoseconds, respectively.
Semantically Stealthy Adversarial Attacks against Segmentation Models

Segmentation models have been found to be vulnerable to targeted and non-targeted adversarial attacks. However, the resulting segmentation outputs are often so damaged that it is easy to spot an attack. In this paper, we propose semantically stealthy adversarial attacks which can manipulate targeted labels while preserving non-targeted labels at the same time. One challenge is making semantically meaningful manipulations across datasets and models. Another challenge is avoiding damaging non-targeted labels. To solve these challenges, we consider each input image as prior knowledge to generate perturbations. We also design a special regularizer to help extract features. To evaluate our model’s performance, we design three basic attack types, namely ‘vanishing into the context,’ ‘embedding fake labels,’ and ‘displacing target objects. ’ Our experiments show that our stealthy adversarial model can attack segmentation models with a relatively high success rate on Cityscapes, Mapillary, and BDD100K. Our framework shows good empirical generalization across datasets and models.
DA3: Dynamic Additive Attention Adaption for Memory-Efficient On-Device Multi-Domain Learning

Nowadays, one practical limitation of deep neural network (DNN) is its high degree of specialization to a single task or domain (e.g., one visual domain). It motivates re-searchers to develop algorithms that can adapt DNN model to multiple domains sequentially, while still performing well on the past domains, which is known as multi-domain learning. Almost all conventional methods only focus on improving accuracy with minimal parameter update, while ignoring high computing and memory cost during training, which makes it difficult to deploy multi-domain learning into more and more widely used resource-limited edge devices, like mobile phone, IoT, embedded system, etc. During our study in multi-domain training process, we observe that large memory used for activation storage is the bottleneck that largely limits the training time and cost on edge devices. To reduce training memory usage, while keeping the domain adaption accuracy performance, we propose Dynamic Additive Attention Adaption (DA3), a novel memory-efficient on-device multi-domain learning method. DA3 learns a novel additive attention adaptor module, while freezing the weights of the pre-trained backbone model for each domain. Differentiating from prior works, our proposed DA3 module not only mitigates activation memory buffering for reducing memory usage during training, but also serves as dynamic gating mechanism to reduce the computation cost for fast inference. We validate DA3 on multiple dataset against state-of-the-art methods, which shows great improvement in both accuracy and training time. Moreover, we deploy DA3 into the popular NIVDIA Jetson Nano edge GPU, where the measured experimental results show our proposed DA3 reduces the on-device training memory consumption by 5-37×, and training time by 2×, in comparison to the baseline methods (e.g., standard fine-tuning, Parallel and Series Res. adaptor, Piggyback and TinyTL).
MediumVC: Any-to-any voice conversion using synthetic specific-speaker speeches as intermedium features

To realize any-to-any (A2A) voice conversion (VC), most methods are to perform symmetric self-supervised reconstruction tasks (Xi to Xi), which usually results in inefficient performances due to inadequate feature decoupling, especially for unseen speakers. We propose a two-stage reconstruction task (Xi to Yi to Xi) using synthetic specific-speaker speeches as intermedium features, where A2A VC is divided into two stages: any-to-one (A2O) and one-to-Any (O2A). In the A2O stage, we propose a new A2O method: SingleVC, by employing a noval data augment strategy(pitch-shifted and duration-remained, PSDR) to accomplish Xi to Yi. In the O2A stage, MediumVC is proposed based on pre-trained SingleVC to conduct Yi to Xi. Through such asymmetrical reconstruction tasks (Xi to Yi in SingleVC and Yi to Xi in MediumVC), the models are to capture robust disentangled features purposefully. Experiments indicate MediumVC can enhance the similarity of converted speeches while maintaining a high degree of naturalness.
Uncovering the Different Components of Contact Resistance to Atomically Thin Semiconductors

Achieving good electrical contacts is one of the major challenges in realizing devices based on atomically thin 2D semiconductors. Several studies have examined this hurdle, but a universal understanding of the contact resistance (Rc) and an underlying approach to its reduction are currently lacking. Here, the classical Rc transmission line model description of contacts to 2D materials is experimentally examined, and a modification based on an additional lateral resistance component, namely, the junction resistance (Rjun) is offered. A combination of transfer length method and contact‐end measurements to characterize contacts to monolayer MoS2 and separate the different Rc components is used. Technology computer‐aided design simulations are also used to study Rc in Fermi‐level pinned and unpinned contacts. This study finds that Rjun is the dominating component of Rc in atomically thin semiconductor devices, and is also responsible for most of the back‐gate bias and temperature dependence. The experimental results help understand the underlying physics of state‐of‐the‐art contact engineering in the context of minimizing Rjun.
3-manifolds of rank 3 have filling links

M. Freedman and V. Krushkal introduced the notion of a “filling” link in a 3manifold: a link L is filling in M if for any spine G of M disjoint from L, π1(G) injects into π1(M \ L). Freedman and Krushkal show that there exist links in the 3-torus T 3 that satisfy a weaker form of filling, but they leave open the question of whether T 3 contains an actual filling link. We answer this question affirmatively by proving in fact that every closed, orientable 3-manifold M with rank(π1(M)) = 3 contains a filling link.
Robustness modularity in complex networks

A basic question in network community detection is how modular a given network is. This is usually addressed by evaluating the quality of partitions detected in the network. The Girvan-Newman (GN) modularity function is the standard way to make this assessment, but it has a number of drawbacks. Most importantly, it is not clearly interpretable, given that the measure can take relatively large values on partitions of random networks without communities. Here we propose a measure based on the concept of robustness: modularity is the probability to find trivial partitions when the structure of the network is randomly perturbed. This concept can be implemented for any clustering algorithm capable of telling when a group structure is absent. Tests on artificial and real graphs reveal that robustness modularity can be used to assess and compare the strength of the community structure of different networks. We also introduce two other quality functions: modularity difference, a suitably normalized version of the GN modularity, and information modularity, a measure of distance based on information compression. Both measures are strongly correlated with robustness modularity, but have lower time complexity, so they could be used on networks whose size makes the calculation of robustness modularity too costly.
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation

Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image generation, text-to-shape generation remains a challenging problem due to the unavailability of paired text and shape data at a large scale. We present a simple yet effective method for zero-shot text-to-shape gener-ation that circumvents such data scarcity. Our proposed method, named CLIP-Forge, is based on a two-stage training process, which only depends on an unlabelled shape dataset and a pre-trained image-text network such as CLIP. Our method has the benefits of avoiding expensive inference time optimization, as well as the ability to generate multiple shapes for a given text. We not only demonstrate promising zero-shot generalization of the CLIP-Forge model qualitatively and quantitatively, but also provide extensive compar-ative evaluations to better understand its behavior.
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Unsupervised detection of anomaly points in time series is a challenging problem, which requires the model to derive a distinguishable criterion. Previous methods tackle the problem mainly through learning pointwise representation or pairwise association, however, neither is sufficient to reason about the intricate dynamics. Recently, Transformers have shown great power in unified modeling of pointwise representation and pairwise association, and we find that the self-attention weight distribution of each time point can embody rich association with the whole series. Our key observation is that due to the rarity of anomalies, it is extremely difficult to build nontrivial associations from abnormal points to the whole series, thereby, the anomalies' associations shall mainly concentrate on their adjacent time points. This adjacent-concentration bias implies an association-based criterion inherently distinguishable between normal and abnormal points, which we highlight through the \emph{Association Discrepancy}. Technically, we propose the \emph{Anomaly Transformer} with a new \emph{Anomaly-Attention} mechanism to compute the association discrepancy. A minimax strategy is devised to amplify the normal-abnormal distinguishability of the association discrepancy. The Anomaly Transformer achieves state-of-the-art results on six unsupervised time series anomaly detection benchmarks of three applications: service monitoring, space&earth exploration, and water treatment.
Disjunction and existence properties in modal arithmetic

We systematically study several versions of the disjunction and the existence properties in modal arithmetic. First, we newly introduce three classes $\mathrm{B}$, $\Delta(\mathrm{B})$, and $\Sigma(\mathrm{B})$ of formulas of modal arithmetic, and study basic properties of them. Then, we prove several implications between the properties. In particular, among other things, we prove that for any consistent recursively enumerable extension $T$ of $\mathbf{PA}(\mathbf{K})$ with $T \nvdash \Box \bot$, the $\Sigma(\mathrm{B})$-disjunction property, the $\Sigma(\mathrm{B})$-existence property, and the $\mathrm{B}$-existence property are pairwise equivalent. Moreover, we introduce the notion of the $\Sigma(\mathrm{B})$-soundness of theories, and prove that for any consistent recursively enumerable extension of $\mathbf{PA}(\mathbf{K4})$, the modal disjunction property is equivalent to the $\Sigma(\mathrm{B})$-soundness.
Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?

As a model-free optimization and decision-making method, deep reinforcement learning (DRL) has been widely applied to the field of energy management in energy Internet. While, some DRL-based energy management schemes also incorporate the prediction module used by the traditional model-based methods, which seems to be unnecessary and even adverse. In this work, we implement the standard energy management scheme with prediction using supervised learning and DRL, and the counterpart without prediction using end-to-end DRL. Then, these two schemes are compared in the unified energy management framework. The simulation results demonstrate that the energy management scheme without prediction is superior over the scheme with prediction. This work intends to rectify the misuse of DRL methods in the field of energy management.
Association between Plasma Homocysteine Concentrations and the First Ischemic Stroke in Hypertensive Patients with Obstructive Sleep Apnea: A 7-Year Retrospective Cohort Study from China

Purpose This study was aimed at investigating the association between baseline plasma homocysteine (Hcy) concentrations and the risk of the first ischemic stroke (IS) and at investigating any possible influential modifying factors in hypertensive patients with obstructive sleep apnea (OSA). Methods Cox proportional hazards regression was employed to investigate the relationship between plasma Hcy concentration and the first IS. A generalized additive model was applied to determine the nonlinear relationship. In addition, we conducted subgroup analysis. Results A total of 2350 hypertensive patients with OSA without a history of IS were enrolled in this study. At a median follow-up of 7.15 years, we identified 93 cases of the first IS. After adjusting for potential confounding, the findings revealed that plasma Hcy concentration was strongly and positively associated with the occurrence of the first IS (per SD increment; HR = 1.37, 95% CI: 1.30-1.44). A nonlinear relationship was found between plasma Hcy concentration and the risk of developing the first IS with inflection points for plasma Hcy of 5 μmol/L. In stratified analysis, a greater positive correlation was found between baseline plasma Hcy concentrations and new-onset IS in patients with DBP ≥ 90 mmHg (per SD increment; HR = 1.48, 95% CI: 1.33-1.65 vs. <90 mmHg: HR = 1.20, 95% CI: 1.02-1.42; P‐interaction = 0.04) and BMI ≥ 24 and <28 kg/m2 (per SD increment; HR = 1.46, 95% CI: 1.26-1.70 vs. <24 kg/m2: HR = 1.13, 95% CI: 0.95-1.33 vs. ≥28 kg/m2: HR = 1.46, 95% CI: 1.25-1.70; P‐interaction = 0.03). Conclusion Elevated plasma Hcy concentrations are independently associated with the risk of the first IS in hypertensive patients with OSA. Plasma Hcy concentrations ≥ 5 μmol/L surely increased the risk of the first IS in hypertensive patients with OSA.
The association between serum CD4 T lymphocyte counts and surgical outcomes in HIV/AIDS patients in Guangxi, China: a retrospective cohort study

Background HIV/AIDS is a chronic disease leading to complications in infected individuals that often require surgical intervention. These patients’ serum CD4 T lymphocyte (CD4) counts represent one of the most important indicators of their ability to tolerate surgical treatment. Previous studies have demonstrated that CD4 cell count (CD4-CC) < 200 cells/μl may increase the risk of surgical complications in these patients, limiting their ability to undergo surgery, which may negatively affect their quality of life. Further investigation into the surgical outcomes of patients with CD4-CC < 200 cells/μl should provide guidance in making appropriate clinical decisions for the optimal healthcare of this patient demographic. Methods All enrolled patients were selected from 14 prefecture-level general hospitals in Guangxi, China, and were referred to AIDS outpost hospitals for inpatient surgical therapy. A total cohort of 168 adult patients was retrospectively analyzed. Multifactorial and stratified analyses were performed to evaluate the in surgical outcome differences for patients with CD4-CC < 200 cells/μl (N = 43), using those with CD4-CC ≥ 200 cells/μl (N = 125) as controls. Results Poor incisional healing was used as the primary outcome indicator, and postoperative complications were used as the secondary outcome indicator. In the patient group with CD4-CC < 200 cells/μl, the risk of surgical complications was significantly increased (OR 2.379; 95% CI [1.049–5.394]) after adjustment. Adjusted stratified analysis of the CD4-CC < 200 cells/μl group revealed that individuals over 60 years (OR 27.504; 95% CI [2.297–329.317]) with erythrocyte counts below 4.00/ml for males or 3.50/ml for females (OR 3.353; 95% CI [1.079–10.419]) had a significantly higher risk of postoperative complications; this finding was statistically different from the control (CD4 ≥ 200 cells/μl) group. However, there was no significant difference between the two groups regarding the risk of poorly healed incision outcomes. Conclusions Preliminary findings suggest that a serum CD4-CC < 200 cells/μl is not a definitive contraindication for surgical therapy and that baseline and surgical characteristics may help predict surgical outcomes in these patients. Further studies are needed to confirm these findings.
Assessment of Risk Factors for Falls among Patients with Parkinson's Disease

Introduction The aim of this study was to assess the risk factors for falls in patients with Parkinson's disease. Materials and Methods The study comprised 53 participants (52.8% women and 47.2% men). The Hoehn and Yahr 5-point disability scale was used to assess the severity of Parkinson's disease. The Tinetti Balance and Gait Scale were used to evaluate the risk of falls. The Katz scale was used to test the independence of people with PD. The Falls Efficacy Scale-International Short Form (FES-I) was implemented to assess fear of falling. Results The majority of participants was at a high risk of falls, being at the same level for women and men. A significant relationship was noted between the risk of falls and subjective assessment of mobility (χ2 = 31.86, p < 0.001), number of falls (χ2 = 37.92, p < 0.001), independence of the subjects (χ2 = 19.28, p < 0.001), type of injury suffered during the fall (χ2 = 36.93, p < 0.001), external factors (χ2 = 33.36, p < 0.001), and the level of fear of falling (χ2 = 8.88, p < 0.001). A significant relationship also occurred between the number of falls and the fear of falling (χ2 = 33.49, p < 0.001) and between the number of falls and disease severity (χ2 = 45.34, p < 0.001). The applied physiotherapy did not reduce the risk of falls (χ2 = 3.18, p = 0.17). Conclusions Individuals who rated their mobility as good or excellent were at a low risk of falls. People who fell more times were at a high risk of falling. People more independent were at a low risk of falls. Previous injuries were the most associated with being at risk of falling. Uneven surfaces and obstacles on one's path are the external factors most associated with the risk of falling. People with low levels of fall anxiety were at a low risk of falls. Most people with low fall anxiety have never fallen. Additionally, the majority of patients with stage 1 of the disease have not fallen at all. The reason for the ineffectiveness of physiotherapy may be due to the exercise programs used and the lack of systematic implementation of them. PD is different for each patient; thus, it is important to select individually customized physiotherapy depending on motor and nonmotor symptoms, as well as general health of a patient.
Multiomics-based analyses of KPNA2 highlight its multiple potentials in hepatocellular carcinoma

Dysregulation and prognostic roles of Karyopherin α2 (KPNA2) were reported in many malignancies including hepatocellular carcinoma (HCC). A multi-omics analysis of KPNA2 is needed to gain a deeper understanding of its multilevel molecular characteristics and provide novel clues for HCC diagnosis, prognosis, and target therapy. Herein multi-omic alterations of KPNA2 were analyzed at genetic, epigenetic, transcript, and protein levels with evaluation of their relevance with clinicopathological features of HCC by integrative analyses. The significant correlations of KPNA2 expression with its gene copy number variation (CNV) and methylation status were shown through Spearman correlation analyses. With Cox regression, Kaplan-Meier survival, and receiver operating characteristic (ROC) analyses, based on the factors of KPNA2 CNV, methylation, expression, and tumor stage, risk models for HCC overall survival (OS) and disease-free survival (DFS) were constructed which could discriminate the 1-year, 3-year, and 5-year OS/DFS status effectively. With Microenvironment Cell Populations-counter (MCP-counter), the immune infiltrations of HCC samples were evaluated and their associations with KPNA2 were shown. KPNA2 expression in liver was found to be influenced by low fat diet and presented significant correlations with fatty acid metabolism and fatty acid synthase activity in HCC. KPNA2 was detected lowered in HCC patient’s plasma by enzyme linked immunosorbent assay (ELISA), consistent with its translocation to nuclei of HCC cells. In conclusion, KPNA2 multilevel dysregulation in HCC and its correlations with immune infiltration and the fatty acid metabolism pathway indicated its multiple roles in HCC. The clinicopathological significance of KPNA2 was highlighted through the in-depth analyses at multilevels.