Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure of betweenness uniform graphs with low values of betweenness centrality

This work deals with undirected graphs that have the same betweenness centrality for each vertex, so-called betweenness uniform graphs (or BUGs). The class of these graphs is not trivial and its classification is still an open problem. Recently, Gago, Coroni\v{c}ov\'a-Hurajov\'a and Madaras conjectured that for every rational $\alpha\ge 3/4$ there exists a BUG having betweenness centrality~$\alpha$. We disprove this conjecture, and provide an alternative view of the structure of betweenness-uniform graphs from the point of view of their complement. This allows us to characterise all the BUGs with betweennes centrality at most 9/10, and show that their betweenness centrality is equal to $\frac{\ell}{\ell+1}$ for some integer $\ell\le 9$. We conjecture that this characterization extends to all the BUGs with betweenness centrality smaller than~1.
Multi-spatial Multi-temporal Air Quality Forecasting with Integrated Monitoring and Reanalysis Data

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. Spatially, there is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To address these limitations, we present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU(MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of the 24h/48h/72h are as follows: PM2.5: (7.72%, 6.67%, 10.45%); PM10: (6.43%, 5.68%, 7.73%); NO2: (5.07%, 7.76%, 16.60%); O3: (6.46%, 6.86%, 9.79%). Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study.
Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing

Large Language Models (LLMs), particularly those similar to ChatGPT, have significantly influenced the field of Natural Language Processing (NLP). While these models excel in general language tasks, their performance in domain-specific downstream tasks such as biomedical and clinical Named Entity Recognition (NER), Relation Extraction (RE), and Medical Natural Language Inference (NLI) is still evolving. In this context, our study investigates the potential of instruction tuning for biomedical language processing, applying this technique to two general LLMs of substantial scale. We present a comprehensive, instruction-based model trained on a dataset that consists of approximately $200,000$ instruction-focused samples. This dataset represents a carefully curated compilation of existing data, meticulously adapted and reformatted to align with the specific requirements of our instruction-based tasks. This initiative represents an important step in utilising such models to achieve results on par with specialised encoder-only models like BioBERT and BioClinicalBERT for various classical biomedical NLP tasks. Our work includes an analysis of the dataset's composition and its impact on model performance, providing insights into the intricacies of instruction tuning. By sharing our codes, models, and the distinctively assembled instruction-based dataset, we seek to encourage ongoing research and development in this area.
Finiteness conjecture for 3-manifolds obtained from handlebodies by attaching 2-handles

We study a generalized Witten's finiteness conjecture for the skein modules of oriented compact 3-manifolds with boundary. We formulate an equivalent version of the generalized finiteness conjecture using handlebodies and 2-handles, and prove the conjecture for some classes with the handlebodies of genus 2 and 3 using the equivalent version.
QAOA on Hamiltonian Cycle problem

I use QAOA to solve the Hamiltonian Circle problem. First, inspired by Lucas, I define the QUBO form of Hamiltonian Cycle and transform it to a quantum circuit by embedding the problem of $n$ vertices to an encoding of $(n-1)^2$ qubits. Then, I calcluate the spectrum of the cost hamiltonian for both triangle case and square case and justify my definition. I also write a python program to generate the cost hamiltonian automatically for finding the hamiltonian cycle in an arbitrary graph. I test the correctess of the hamailtonian by analyze their energy spectrums. Since the $(n-1)^2$ embedding limit my simulation of graph size to be less than $5$, I decide to test the correctness, only for small and simple graph in this project. I implement the QAOA algorithm using qiskit and run the simulation for the triangle case and the square case, which are easy to test the correctness, both with and without noise. A very interesting result I got is that for the square case, the QAOA get much better result on a noisy simulator than a noiseless simulator. The explanation for this phenomena require further investigation, perhaps quantum noise can actually be helpful, rather than harmful in the annealing algorithms. I also use two different kinds of mixer, $R_x$ mixer and $R_y$ circuit to run the simulation. It turns out that $R_x$ mixer performs much better than $R_y$ mixer in this problem.
Electric Double Layer from Phase Demixing Reinforced by Strong Coupling Electrostatics

Ionic liquids (ILs) are appealing electrolytes for their favorable physicochemical properties. However, despite their longstanding use, understanding the capacitive behavior of ILs remains challenging. This is largely due to the formation of a non-conventional electric double layer (EDL) at the electrode-electrolyte interface. This study shows that the short-range Yukawa interactions, representing the large anisotropically charged ILs, demix IL to create a spontaneous surface charge separation, which is reinforced by the strongly coupled charge interaction. The properties of the condensed layer, the onset of charge separation, and the rise of overscreening and crowding critically depend on the asymmetry of Yukawa interactions.
Leveraging Partial Symmetry for Multi-Agent Reinforcement Learning

Incorporating symmetry as an inductive bias into multi-agent reinforcement learning (MARL) has led to improvements in generalization, data efficiency, and physical consistency. While prior research has succeeded in using perfect symmetry prior, the realm of partial symmetry in the multi-agent domain remains unexplored. To fill in this gap, we introduce the partially symmetric Markov game, a new subclass of the Markov game. We then theoretically show that the performance error introduced by utilizing symmetry in MARL is bounded, implying that the symmetry prior can still be useful in MARL even in partial symmetry situations. Motivated by this insight, we propose the Partial Symmetry Exploitation (PSE) framework that is able to adaptively incorporate symmetry prior in MARL under different symmetry-breaking conditions. Specifically, by adaptively adjusting the exploitation of symmetry, our framework is able to achieve superior sample efficiency and overall performance of MARL algorithms. Extensive experiments are conducted to demonstrate the superior performance of the proposed framework over baselines. Finally, we implement the proposed framework in real-world multi-robot testbed to show its superiority.
Quotients of the braid group that are extensions of the symmetric group

We consider normal subgroups $N$ of the braid group $B_n$ such that the quotient $B_n/N$ is an extension of the symmetric group by an abelian group. We show that, if $n\geq 4$, then there are exactly 8 commensurability classes of such subgroups. We define a Specht subgroup to be a subgroup of this form that is maximal in its commensurability class. We give descriptions of the Specht subgroups in terms of winding numbers and in terms of infinite generating sets. The quotient of the pure braid group by a Specht subgroup is a module over the symmetric group. We show that the modules arising this way are closely related to Specht modules for the partitions $(n-1,1)$ and $(n-2,2)$, working over the integers. We compute the second cohomology of the symmetric group with coefficients in both of these Specht modules, working over an arbitrary commutative ring. Finally, we determine which of the extensions of the symmetric group arising from Specht subgroups are split extensions.
Universality of critical dynamics on a complex network

We investigate the role of the spectral dimension $d_s$ in determining the universality of phase transitions on a complex network. Due to its structural heterogeneity, a complex network generally acts as a disordered system. Specifically, we study the synchronization and entrainment transitions in the nonequilibrium dynamics of the Kuramoto model and the phase transition of the equilibrium dynamics of the classical $XY$ model, thereby covering a broad spectrum from nonlinear dynamics to statistical and condensed matter physics. Using linear theory, we obtain a general relationship between the dynamics occurring on the network and the underlying network properties. This yields the lower critical spectral dimension of the phase synchronization and entrainment transitions in the Kuramoto model as $d_s=4$ and $d_s=2$ respectively, whereas for the phase transition in the $XY$ model it is $d_s=2$. To test our theoretical hypotheses, we employ a network where any two nodes on the network are connected with a probability proportional to a power law of the distance between the nodes; this realizes any desired $d_s\in [1, \infty)$. Our detailed numerical study agrees well with the prediction of linear theory for the phase synchronization transition in the Kuramoto model. However, it shows a clear entrainment transition in the Kuramoto model and phase transition in the $XY$ model at $d_s \gtrsim 3$, not $d_s=2$ as predicted by linear theory. Our study indicates that network disorder in the region $2 \leq d_s \lesssim 3$ introduces strong finite-size fluctuations, which makes it extremely difficult to probe the existence of the ordered phase as predicted, affecting the dynamics profoundly.
Valuation of Heritage Tourism Sites in West Bengal (India): An Application of Travel Cost Approach

This study focuses on assessing the values of heritage sites in West Bengal, India, with a particular emphasis on the post-lockdown period in the wake of the COVID-19 pandemic. The heritage temple town of Bishnupur in the Bankura district was purposively chosen as the primary research site. Both primary and secondary data sources were utilized. Secondary data were collected from official publications of the Ministry of Tourism, Government of India, the Department of Tourism, Government of West Bengal and other pertinent grey literature sources. Primary data were gathered through structured interviews with a sample of tourists. Analytical tools, including descriptive statistics, semi-log regression models, ANOVA, zonal travel cost method and contingent valuation method, were employed for data analysis. Findings reveal that Bishnupur’s heritage temples primarily attract tourists from nearby areas, particularly within the same district, during the post-lockdown period. Notably, tourists expressed a high level of satisfaction with their experiences, and this satisfaction was found to be significantly associated with their willingness to contribute financially for further improvements and conservation efforts in the heritage sites. This study provides valuable insights into the recreational values of selected tourist sites and tourists’ willingness to pay for their continued enhancement, preservation and management. These insights are particularly pertinent in the context of the post-lockdown period caused by the COVID-19 pandemic. They offer guidance for decision-makers and stakeholders involved in the development and preservation of heritage tourism in West Bengal, India, serving as a valuable resource for informed policy and strategy formulation.
Identification of nonsense-mediated decay inhibitors that alter the tumor immune landscape

Despite exciting developments in cancer immunotherapy, its broad application is limited by the paucity of targetable antigens on the tumor cell surface. As an intrinsic cellular pathway, nonsense- mediated decay (NMD) conceals neoantigens through the destruction of the RNA products from genes harboring truncating mutations. We developed and conducted a high throughput screen, based on the ratiometric analysis of transcripts, to identify critical mediators of NMD. This screen revealed disruption of kinase SMG1’s phosphorylation of UPF1 as a potent disruptor of NMD. This led us to design a novel SMG1 inhibitor, KVS0001, that elevates the expression of transcripts and proteins resulting from truncating mutations in vivo and in vitro. Most importantly, KVS0001 concomitantly increased the presentation of immune-targetable HLA class I-associated peptides from NMD-downregulated proteins on the surface of cancer cells. KVS0001 provides new opportunities for studying NMD and the diseases in which NMD plays a role, including cancer and inherited diseases. One Sentence Summary Disruption of the nonsense-mediated decay pathway with a newly developed SMG1 inhibitor with in-vivo activity increases the expression of T-cell targetable cancer neoantigens resulting from truncating mutations.
Discovery and Optimization of Narrow Spectrum Inhibitors of Tousled Like Kinase 2 (TLK2) Using Quantitative Structure Activity Relationships

The oxindole scaffold has been the center of several kinase drug discovery programs, some of which have led to approved medicines. A series of two oxindole matched pairs from the literature were identified where TLK2 was a potent off-target kinase. The oxindole as long been considered a promiscuous inhibitor template, but across these 4 specific literature oxindoles TLK2 activity was consistent, while the kinome profile was radically different from narrow to broad spectrum coverage. We synthesized a large series of analogues and through quantitative structure-activity relationship (QSAR) analysis, water mapping of the kinase ATP binding sites, small-molecule x-ray structural analysis and kinome profiling, narrow spectrum, sub-family selective, chemical tool compounds were identified to enable elucidation of TLK2 biology.
Hyperoxia Induced Chromatin Remodeling in Mesenchymal Stromal Cells

Chromatin is a highly dynamic entity of the eukaryotic cell nucleus. New evidences are emerging with a support of the notion that chromatin can locally and globally rearrange itself to adapt with the cellular microenvironmental changes. Such changes include oxidative stress such as supraphysiological oxygen level, found in hyperoxia. Although it is known that hyperoxia can cause DNA damage and alterations in cell function, it is not well understood how the chromatin architecture changes under such condition and what is the functional significance of such change. Characterization and quantification of chromatin remodeling is therefore a first step to understand the chromatin dynamics for understanding complex subnuclear behavior under single or multiple environmental stresses. Towards that end, in this work, human bone marrow derived mesenchymal stromal cells were used to characterize such chromatin level alterations under the changing oxidative stress on cells. Hyperoxia was applied using hydrogen peroxide treatment. Chromatin remodeling was hindered by the inhibition of methyltransferase EZH2, which regulates ARID1A activity and chromatin compaction. Epigenetic modifications and DNA damage under hyperoxia was investigated, which was found affected by the pharmacological inhibition of chromatin remodeling. The developed techniques and findings might provide us with new mechanism and intervention strategies to target genotoxic hyper-oxidative stress, which is common in degenerative diseases and aging, and also for cell therapy in regenerative medicine.
A STUDY ON LOCAL IDENTITY OF LOW CARBON TOURISM BASED ON SOCIAL EXCHANGE THEORY-A CASE STUDY OF TAIWAN’S CHARACTERISTIC HOT SPRING AREA

Hot spring resources are the characteristics and advantages of Taiwan’s development of tourism industry. However, due to the fragile geology and the environmental disasters over the years caused by over-exploitation and natural disasters, it is imperative to develop the tourism industry of “symbiosis with hot springs”, and thus the promotion of low-carbon concept in urban areas plays an important role. The scope of this research covers the Guanziling Hot Spring Area, which is a mud hot spring rarely seen in the world. Guanziling Hot Spring won the Champion of Taiwan’s Top 10 Hot Spring awarded by Taiwan Tourism Bureau in 2018. Based on Social Exchange Theory (SET), this paper explores the changes of local residents’ environmental awareness and tourism interests derived from the promotion of low-carbon policies by con-firmative factor analysis and testing of relations between constructs. The results show that residents highly agree that the hot spring tourism industry is a local competitive industry, and generally hold a high positive evaluation of the tourism effect and environmental conservation driven by low-carbon campaigns. In terms of the construct of social exchange factors, “tourism value affirmation” and “fairness and reciprocity of tourism interests” are the main factors affecting the promotion of low-carbon tourism. The empirical research results can provide reference for relevant units to focus on promoting low-carbon cities and low-carbon tourism.
Mobilizing Research and Regulatory Action on Dark Patterns and Deceptive Design Practices

Deceptive, manipulative, and coercive practices are deeply embedded in our digital experiences, impacting our ability to make informed choices and undermining our agency and autonomy. These design practices—collectively known as “dark patterns” or “decep-tive patterns”—are increasingly under legal scrutiny and sanctions, largely due to the efforts of human-computer interaction scholars that have conducted pioneering research relating to dark patterns types, definitions, and harms. In this workshop, we continue building this scholarly community with a focus on organizing for action. Our aims include: (i) building capacity around specific research questions relating to methodologies for detection; (ii) characterization of harms; and (iii) creating effective countermeasures. Through the outcomes of the workshop, we will connect our scholarship to the legal, design, and regulatory communities to inform further legislative and legal action.
MRI-Based Radiomics Approach Predicts Tumor Recurrence in ER+/HER2- Early Breast Cancer Patients

Oncotype Dx is a genetic assay providing a recurrence score (RS) correlated with the risk of cancer recurrence and adjuvant treatment response in breast carcinoma. We investigated the ability of an MRI-based radiomics approach to predict the risk of tumor recurrence in breast cancer. A total of 62 patients with biopsy-proved ER+/HER2- early breast cancer who underwent pre-treatment MRI and Oncotype Dx were included. An RS>25 was considered discriminant between low-intermediate and high risk of tumor recurrence. Two readers segmented each tumor. Radiomics features were extracted from the tumor and the peritumoral tissues. Partial least square (PLS) regression was used as the multivariate machine-learning algorithm. PLS β-weights of radiomics features included the 5% features with the largest β-weights in magnitude (top 5%). The diagnostic performance of the radiomics model was assessed through receiver operating characteristic (ROC) analysis. A null hypothesis probability threshold of 5% was chosen (p < 0.05). The predictive model delivered an AUC of 0.76. Of the 47 features included in the top 5%, 33 were texture-related and derived from the tumor and the peritumoral tissues. After a prospective evaluation in more extensive clinical trials, this approach may identify non-invasively patients who are more likely to benefit from adjuvant therapy.
Graph Balancing : A Special Case of Scheduling Unrelated Parallel Machines

We design a 1.75-approximation algorithm for a special case of scheduling parallel machines to minimize the makespan, namely the case where each job can be assigned to at most two machines, with the same processing time on either machine. (This is a special case of so-called restricted assignment, where the set of eligible machines can be arbitrary for each job.) We also show that even for this special case it is NP -hard to compute better than 1.5 approximation. This is the first improvement of the approximation ratio 2 of Lenstra, Shmoys, and Tardos [Approximation algorithms for scheduling unrelated parallel machines, Math. Program. , 46:259–271, 1990], for any special case with unbounded number of machines. Our lower bound yields the same ratio as their lower bound which works for restricted assignment, and which is still the state-ofthe-art lower bound even for the most general case.
Sources of uncertainty in the calculation of design weather conditions

In this research we examine the uncertainty associated with the design weather conditions (DWC) as found in the ASHRAE Handbookfundamentals. The work is organized around three main objectives. The first objective is to determine the error associated with typical data limitations, such as nonhomogeneous data, incomplete data, and insufficient data. The second objective is to develop methods to estimate the percentiles of hourly dry-bulb temperature from daily data and to estimate humidity-related design weather conditions when no humidity data are available. The final objective is to determine the frequency and duration of episodes during which the ASHRAE DWC are met or exceeded. We find for dry-bulb-related DWC that various data limitations are generally minimal (uncertainty less than 1 °C) for a station if at least tenyears of data are available. For DWC involving psychrometric variables, the probable error was shown to vary with measuring system and ambient conditions. Methods developed for estimating hourly DWC from daily values proved reliable (mean absolute error less than 1°C). The estimation of wet-bulb D WC when only dry-bulb temperatures are available is challenging, but a useful method was developed (mean absolute error near 1°C). Episodic behavior of the frequency/duration of DWC was examined and in some sites is consistent with known trends in urban warming. There was no evidence of episodic behavior of DWC except for areas undergoing rapid urbanization.
Effect of Cerebrospinal Fluid Area Mask Correction on 123I-FP-CIT SPECT Images in Idiopathic Normal Pressure Hydrocephalus

Makoto Ohba (  m-ohba@med.id.yamagata-u.ac.jp ) Yamagata University Hospital Ryota Kobayashi Yamagata University School of Medicine Chifumi Iseki Yamagata University School of Medicine Kazukuni Kirii Yamagata University School of Medicine Daichi Morioka Yamagata University School of Medicine Koichi Otani Yamagata University School of Medicine Yasuyuki Ohta Yamagata University School of Medicine Yukihiko Sonoda Yamagata University School of Medicine Koji Suzuki Yamagata University Hospital Masafumi Kanoto Yamagata University School of Medicine