Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Off-Policy Reinforcement Learning Algorithm for the Continuous-Time LQR Problem

In this paper, an off-policy reinforcement learning algorithm is designed to solve the continuous-time linear quadratic regulator (LQR) problem using only input-state data measured from the system. Different from other algorithms in the literature, we propose the use of a specific persistently ex-citing input as the exploration signal during the data collection step. We then show that, using this persistently excited data, the solution of the matrix equation in our algorithm is guaranteed to exist and to be unique at every iteration. Convergence of the algorithm to the optimal control input is also proven. Moreover, we formulate the policy evaluation step as the solution of a Sylvester-transpose equation, which increases the efficiency of its solution. A method to determine an initial stabilizing policy using only measured data is proposed. Finally, the advantages of the proposed method are tested via simulation.
A Closer Look at Parameter-Efficient Tuning in Diffusion Models

Large-scale diffusion models like Stable Diffusion are powerful and find various real-world applications while customizing such models by fine-tuning is both memory and time inefficient. Motivated by the recent progress in natural language processing, we investigate parameter-efficient tuning in large diffusion models by inserting small learnable modules (termed adapters). In particular, we decompose the design space of adapters into orthogonal factors -- the input position, the output position as well as the function form, and perform Analysis of Variance (ANOVA), a classical statistical approach for analyzing the correlation between discrete (design options) and continuous variables (evaluation metrics). Our analysis suggests that the input position of adapters is the critical factor influencing the performance of downstream tasks. Then, we carefully study the choice of the input position, and we find that putting the input position after the cross-attention block can lead to the best performance, validated by additional visualization analyses. Finally, we provide a recipe for parameter-efficient tuning in diffusion models, which is comparable if not superior to the fully fine-tuned baseline (e.g., DreamBooth) with only 0.75 \% extra parameters, across various customized tasks.
AceCoder: Utilizing Existing Code to Enhance Code Generation

Large Language Models (LLMs) have shown great success in code generation. LLMs take as the input a prompt and output the code. A key question is how to make prompts (i.e., Prompting Techniques). Existing prompting techniques are designed for natural language generation and have low accuracy in code generation. In this paper, we propose a new prompting technique named AceCoder. Our motivation is that code generation meets two unique challenges (i.e., requirement understanding and code implementation). AceCoder contains two novel mechanisms (i.e., guided code generation and example retrieval) to solve these challenges. (1) Guided code generation asks LLMs first to analyze requirements and output an intermediate preliminary (e.g., test cases). The preliminary is used to clarify requirements and tell LLMs"what to write". (2) Example retrieval selects similar programs as examples in prompts, which provide lots of relevant content (e.g., algorithms, APIs) and teach LLMs"how to write". We apply AceCoder to three LLMs (e.g., Codex) and evaluate it on three public benchmarks using the Pass@k. Results show that AceCoder can significantly improve the performance of LLMs on code generation. (1) In terms of Pass@1, AceCoder outperforms the state-of-the-art baseline by up to 56.4% in MBPP, 70.7% in MBJP, and 88.4% in MBJSP. (2) AceCoder is effective in LLMs with different sizes (i.e., 6B to 13B) and different languages (i.e., Python, Java, and JavaScript). (3) Human evaluation shows human developers prefer programs from AceCoder.
∞-Diff: Infinite Resolution Diffusion with Subsampled Mollified States

This paper introduces $\infty$-Diff, a generative diffusion model defined in an infinite-dimensional Hilbert space, which can model infinite resolution data. By training on randomly sampled subsets of coordinates and denoising content only at those locations, we learn a continuous function for arbitrary resolution sampling. Unlike prior neural field-based infinite-dimensional models, which use point-wise functions requiring latent compression, our method employs non-local integral operators to map between Hilbert spaces, allowing spatial context aggregation. This is achieved with an efficient multi-scale function-space architecture that operates directly on raw sparse coordinates, coupled with a mollified diffusion process that smooths out irregularities. Through experiments on high-resolution datasets, we found that even at an $8\times$ subsampling rate, our model retains high-quality diffusion. This leads to significant run-time and memory savings, delivers samples with lower FID scores, and scales beyond the training resolution while retaining detail.
A simple model for calculating proton induced SEU

A new semi-empirical model for proton induced SEU is presented. For estimating the energy deposited by the protons in the sensitive volumes of the devices the model uses the measured spectra of surface barrier detectors (SBD) with the same thicknesses of the sensitive volume and at the same proton energies. Fitting the SBD spectra by exponential functions and the heavy ion induced cross sections by simple formulas results in simple expressions for the proton cross sections. The model predictions are in good agreement with the experimental results.
Ideology-driven polarisation in online ratings: the review bombing of The Last of Us Part II

A review bomb is a large and quick surge in online reviews about a product, service, or business, coordinated by a group of people willing to manipulate public opinion about that entity. This study challenges the assumption that review bombing is solely a phenomenon of misinformation and connects motivations and substantial content of online reviews with the broader theory of judgement of facts and of value. These theories are verified in a quantitative analysis of the most prominent case of review bombing, which involves the video game The Last of Us Part II. It is discovered that ideology-driven ratings are followed by a grassroots counter-bombing, aimed at mitigating the effects of the negative ratings. The two factions of bombers, despite being politically polar opposites, are very similar in terms of other metrics. Evidence suggests the theoretical framework of political disinformation is insufficient to explain this case of review bombing. In light of the need to re-frame review bombing, recommendations are proposed for the preventive management of future cases.
Time-uniform central limit theory and asymptotic confidence sequences

Confidence intervals based on the central limit theorem (CLT) are a cornerstone of classical statistics. Despite being only asymptotically valid, they are ubiquitous because they permit statistical inference under weak assumptions and can often be applied to problems even when nonasymptotic inference is impossible. This paper introduces time-uniform analogues of such asymptotic confidence intervals, adding to the literature on confidence sequences (CS) -- sequences of confidence intervals that are uniformly valid over time -- which provide valid inference at arbitrary stopping times and incur no penalties for"peeking"at the data, unlike classical confidence intervals which require the sample size to be fixed in advance. Existing CSs in the literature are nonasymptotic, enjoying finite-sample guarantees but not the aforementioned broad applicability of asymptotic confidence intervals. This work provides a definition for"asymptotic CSs"and a general recipe for deriving them. Asymptotic CSs forgo nonasymptotic validity for CLT-like versatility and (asymptotic) time-uniform guarantees. While the CLT approximates the distribution of a sample average by that of a Gaussian for a fixed sample size, we use strong invariance principles (stemming from the seminal 1960s work of Strassen) to uniformly approximate the entire sample average process by an implicit Gaussian process. As an illustration, we derive asymptotic CSs for the average treatment effect in observational studies (for which nonasymptotic bounds are essentially impossible to derive even in the fixed-time regime) as well as randomized experiments, enabling causal inference in sequential environments.
MitoEVs: A new player in multiple disease pathology and treatment

Abstract Mitochondrial damage plays vital roles in the pathology of many diseases, such as cancers, neurodegenerative diseases, aging, metabolic diseases and many types of organ injury. However, the regulatory mechanism of mitochondrial functions among different cells or organs in vivo is still unclear, and efficient therapies for attenuating mitochondrial damage are urgently needed. Extracellular vesicles (EVs) are cell‐derived nanovesicles that can deliver bioactive cargoes among cells or organs. Interestingly, recent evidence shows that diverse mitochondrial contents are enriched in certain EV subpopulations, and such mitoEVs can deliver mitochondrial components to affect the functions of recipient cells under different conditions, which has emerged as a hot topic in this field. However, the overview and many essential questions with respect to this event remain elusive. In this review, we provide a global view of mitoEVs biology and mainly focus on the detailed sorting mechanisms, functional mitochondrial contents, and diverse biological effects of mitoEVs. We also discuss the pathogenic or therapeutic roles of mitoEVs in different diseases and highlight their potential as disease biomarkers or therapies in clinical translation. This review will provide insights into the pathology and drug development for various mitochondrial injury‐related diseases.
PUBLIC HEALTH ENDOCRINOLOGY Prevalence and correlates of peripheral neuropathy among type 2 diabetes mellitus patients – A community based epidemiological study in a town of Eastern India

Indian Journal of Endocrinology and Metabolism ¦ Volume 24 ¦ Issue 5 ¦ September-October 2020 453 removal of lesion causing symptoms. Description: Abott Libre Pro was applied to two MEN‐1 patients 3‐4 days before surgery and continued till 2‐3 days after discharge. Pre‐operatively and intra‐operatively these subjects are kept at fixed DNS infusion. Upward blood glucose spike was used to define dominant lesion enucleation. Once spike in blood glucose is observed, no further exploration was carried out and pancreatectomy avoided. Impact on current practise: Studies on type 1 DM have shown Abott Libre pro to be inferior in picking up hypoglycemia. However during insulinoma surgery, we looked for spike in blood glucose rather than hypoglycemia, especially the steepness of line. We have >20 months follow‐up of two patients without any recurrence. Intra‐op rt‐CGM should be widely adopted as handy tool by endocrinologists in India, as state of art localizing techniques like arterial calcium stimulation with hepatic venous sampling (ASVS) are not available. Conclusion: Further studies including long term follow up (>5 years) should be done to delineate perioperative and intraoperative rt‐CGM indices which predict clinical remission. This will help in better management of insulinoma especially those associated with MEN‐1 syndrome.
Magnetic activity in the young solar analog LQ hydrae. I. Active longitudes and cycles

We present the first evidence that a single active dwarf of solar type can show a long-lived, nonaxisymmetric spot distribution - active longitudes on opposite hemispheres, similar to evolved, rapidly rotating RS CVn-type binary stars. We analyse new as well as published photometric observations of the young active dwarf LQ Hya, spanning almost 20 years. We find that activity of the star has three activity cycles: a 5.2-yr "flip-flop" cycle, a 7.7-yr period in the amplitude modulation of the brightness and an approximately 15-yr period in variations of the mean brightness. The two shorter cycles are related to the alternating active longitudes and are similar to cycles observed in RS CVn-type stars. The 15-yr cycle reflects periodic changes of the mean spottedness of the star and resembles the solar 11-year cycle. The spot rotation period (about 1.6 days) changes during the 15-yr cycle, indicating the presence of small dierential rotation. The lengths of the three cycles are related as 3:2:1, with the repetition of the spot configuration after 15 years. We discuss the possibility that the observed spot cycles represent two dierent magnetic dynamo modes operating in LQ Hya: an axisymmetric mode, as in the Sun, and a nonaxisymmetric higher order mode with two cycles in spot patterns. Our results suggest that young stars exhibit their cycles in spot distribution, as seen in LQ Hya. This is in contrast to the conclusion based on the analysis of Caii H&K emission from plages. The results suggest also that the Vaughan-Preston gap represents a transition from a multiple-mode dynamo to a single-mode dynamo.
Management of failed stenting of the unprotected left main coronary artery

Percutaneous coronary intervention (PCI) is increasingly accepted as treatment for unprotected left main coronary artery (ULMCA) disease especially in those patients who are unsuitable for cardiac surgery. Treatment of any stent failure is associated with increased complexity and worse clinical outcomes when compared with de novo lesion revascularization. Intracoronary imaging has provided new insight into mechanisms of stent failure and treatment options have developed considerably over the last decade. There is paucity of evidence on the management strategy for stent failure in the specific setting of ULMCA. Treating any left main with PCI requires careful consideration and consequently treatment of failed stents in ULMCA is complex and provides unique challenges. Consequently, we provide an overview of ULMCA stent failure, proposing a tailored algorithm to guide best management and decision in daily clinical practice, with a special focus on intracoronary imaging characterization of causal mechanisms and specific technical and procedural considerations.
Development of a tool to support general practitioners to help adolescents with knee pain: an analysis using the Theoretical Domains Framework

Abstract Aim: Using the Theoretical Domains Framework (TDF) and COM-B model, this study aimed to determine the facilitators to a support tool for adolescent non-traumatic knee pain in general practice. Background: Many children and adolescents with non-traumatic knee pain consult their general practice. Currently, there are no tools to support general practitioners in the diagnosis and management of this group. There is a need to identify behavioural targets that would facilitate further development and implementation of such a tool. Methods: This study was designed as a qualitative study using focus group interviews with 12 medical doctors working in general practice. The semi-structured focus group interviews conducted online and followed an interview guide based on the TDF and COM-B model. Data were analysed via thematic text analysis. Findings: One of the biggest challenges from the general practitioner’s perspective was how to manage and guide adolescents with non-traumatic knee pain. The doctors had doubts in their capability to diagnose knee pain and saw opportunity to help structure the consultation. The doctors felt motivated to use a tool but considered access a potential barrier. Increasing opportunity and motivation by creating access in the community among general practitioners was considered important. We identified several barriers and facilitators for a support tool for the management of adolescent non-traumatic knee pain in general practice. To align with user needs, future tools should support diagnostic workup, structure the consultation and be easily available among doctors working in general practice.
The Regularity Lemma and applications to packings in graphs

In this thesis we investigate applications of Szemeredi’s Regularity Lemma [20]. This result was originally formulated to solve number-theoretical problems. However, we consider its applications in graph theory, in particular to packing results. We begin by introducing some of the basic notions that are needed to understand and use the Regularity Lemma. From this we give an outline of some tools which are useful in applications of the Regularity Lemma. For example, given a graph H we will see that the Key Lemma (see Section 3.2) can be applied to find almost perfect H-packings in graphs, whereas the Blowup Lemma (see Section 7.2) is useful for finding perfect H-packings in graphs. Furthermore we give several examples which use these results. For instance, in Chapter 4 we include proofs of the Erdős-Stone Theorem [6] and the AlonYuster Theorem on almost perfect packings [1]. We give an account of several results concerning H-packings in large dense graphs. For example, when considering graphs G with large minimum degree Komlos’ Theorem [11] tells us that the critical chromatic number of H is the parameter which governs whether G has a perfect H-packing. The Alon-Yuster Theorem on perfect packings [2] and a result by Kuhn and Osthus [16] determine the corresponding parameter for perfect H-packings. We prove this result in Chapter 7. We also investigate similar results involving so-called Ore-type degree conditions. In Chapter 5 we establish an analogue of Komlos’ Theorem for such degree conditions. Further, in Chapter 8 we prove an analogue of the Alon-Yuster Theorem on perfect packings. However, we also see that the characterisation of the parameter that governs whether a graph has a perfect H-packing is not the same when considering minimum and Ore-type degree conditions. Indeed, we provide an example that shows that an Ore-type analogue of the result by Kuhn and Osthus does not exist. This leads into the interesting question for which graphs we can improve the degree condition in the Ore-type analogue of the Alon-Yuster Theorem on perfect packings. We should make the preliminary remark that throughout this thesis floors and ceilings are ignored whenever this does not affect the argument given.
Photocatalytic activity of Cu@Ag BNCs synthesized by the green method: Photodegradation methyl orange and indigo carmine

Abstract In this research, four bimetallic nanocomposites (BNCs) of copper and silver with weight ratios (0.9:0.10, 0.92:0.08, 0.94:0.06, 0.96:0.04) formulated and specified as (Cu0.90@Ag0.10), (Cu0.92@Ag0.08), (Cu0.94@Ag0.06), and (Cu0.96@Ag0.04) were prepared via the green synthesis in the presence of Citrus paradisi extract as a low cost, simple and environmentally friendly method. The as-prepared Cu@Ag BNCs were identified by FTIR, XRD, EDX, FE-SEM, and EDX mapping analyses. The FTIR analysis was performed to determine the functional groups in Cu@Ag BNCs. EDX analysis confirms the existence of Cu and Ag in BNCs. These Cu@Ag BNCs were used as catalysts for the photodegradation process of methyl orange (MO) and indigo carmine (IC) as the anionic organic dye. The effect of parameters such as different weight percentages of BNCs constituent metals, the amounts of photocatalyst and hydrogen peroxide on the degradation rate of MO and IC under visible light was investigated and their optimal values were determined. The optimized conditions for removal of the dyes in the natural pH and 0.005 g of photocatalyst Cu0.90@Ag0.10 BNCs reached for degradation dyes MO and IC to 90.0% and 100% respectively.
Impact of decision and action outcomes on subsequent decision and action behaviours in humans

Speed–accuracy trade‐off adjustments in decision‐making have been mainly studied separately from those in motor control. In the wild, however, animals coordinate their decision and action, often deciding while acting. Recent behavioural studies support this view, indicating that animals, including humans, trade decision time for movement time to maximize their global rate of reward during experimental sessions. Besides, it is well established that choice outcomes impact subsequent decisions. Crucially though, whether and how a decision outcome also influences the subsequent motor performance, and whether and how the outcome of a movement influences the next decision, is unclear. Here, we address these questions by analysing trial‐to‐trial changes of choice and motor behaviours in healthy human participants instructed to perform successive perceptual decisions expressed with reaching movements whose duration was either weakly or strongly constrained in separate tasks. Results indicate that after a wrong decision, subjects who were weakly constrained in their action duration decided more slowly and more accurately. Interestingly, they also shortened their subsequent movement duration by moving faster. Conversely, we found that errors of constrained movements influenced not only the speed and the amplitude of the following movement but those of the decision too. If the movement had to be slowed down, the decision that precedes that movement was accelerated and vice versa. Together, these results indicate that from one trial to the next, humans seek to determine a behavioural duration as a whole instead of optimizing each of the decision and action speed–accuracy trade‐offs independently of each other.
Natural constraints explain working memory capacity limitations in sensory-cognitive models

The limited capacity of the brain to retain information in working memory has been well-known and studied for decades, yet the root of this limitation remains unclear. Here we built sensory-cognitive neural network models of working memory that perform tasks using raw visual stimuli. Contrary to intuitions that working memory capacity limitation stems from memory or cognitive constraints, we found that pre-training the sensory region of our models with natural images imposes sufficient constraints on models to exhibit a wide range of human-like behaviors in visual working memory tasks designed to probe capacity. Examining the neural mechanisms in our model reveals that capacity limitation mainly arises in a bottom-up manner. Our models offer a principled and functionally grounded explanation for the working memory capacity limitation without parameter fitting to behavioral data or much hyperparameter tuning. This work highlights the importance of developing models with realistic sensory processing even when investigating memory and other high-level cognitive phenomena.