Miracle-dz/UniFAR-FTUs huggingface dataset:
Constitutional AI: Harmlessness from AI Feedback

As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.
Learning to repeatedly solve routing problems

In the last years, there has been a great interest in machine‐learning‐based heuristics for solving NP‐hard combinatorial optimization problems. The developed methods have shown potential on many optimization problems. In this paper, we present a learned heuristic for the reoptimization of a problem after a minor change in its data. We focus on the case of the capacited vehicle routing problem with static clients (i.e., same client locations) and changed demands. Given the edges of an original solution, the goal is to predict and fix the ones that have a high chance of remaining in an optimal solution after a change of client demands. This partial prediction of the solution reduces the complexity of the problem and speeds up its resolution, while yielding a good quality solution. The proposed approach resulted in solutions with an optimality gap ranging from 0% to 1.7% on different benchmark instances within a reasonable computing time.
A Principled, Flexible and Efficient Framework for Hypergraph Benchmarking

In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured benchmark models have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a ﬂexible and eﬃcient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows to specify general community structures and tune diﬀerent local statistics. We illustrate how to use our model to sample synthetic data with desired features (assortative or disassortative communities, mixed or hard community assignments, etc.), benchmark community detection algorithms, and generate hypergraphs structurally similar to real-world data. Overcoming previous limitations on the generation of synthetic hypergraphs, our work constitutes a substantial advancement in the statistical modeling of higher-order systems.
Evolutionary Algorithms for Multi‐Center Solutions

Large classes of multi‐center supergravity solutions have been constructed in the study of supersymmetric black holes and their microstates. Many smooth multi‐center solutions have the same charges as supersymmetric black holes, with all centers deep inside a long black‐hole‐like throat. These configurations are constrained by regularity, absence of closed timelike curves, and charge quantization. Due to these constraints, constructing explicit solutions with several centers in generic arrangements, and with all parameters in physically relevant ranges, is a hard task. In this work, an optimization algorithm, based on evolutionary algorithms and Bayesian optimization is presented, that systematically constructs numerical solutions satisfying all constraints. Explicit examples of novel five‐center and seven‐center machine‐precision solutions are exhibited.
Non-parametric Lagrangian biasing from the insights of neural nets

We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field. By fitting the mass-weighted halo field in the AbacusSummit simulations at z = 0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2–5% underestimation of the large-scale power and can cause the neural net to overfit. We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space. Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales. Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space.
Best-Answer Prediction in Q&A Sites Using User Information

Community Question Answering (CQA) sites have spread and mul-tiplied signiﬁcantly in recent years. Sites like Reddit, Quora, and Stack Exchange are becoming popular amongst people interested in ﬁnding answers to diverse questions. One practical way of ﬁnding such answers is automatically predicting the best candidate given existing answers and comments. Many studies were conducted on answer prediction in CQA but with limited focus on using the background information of the questionnaires. We address this limitation using a novel method for predicting the best answers using the questioner’s background information and other features, such as the textual content or the relationships with other participants. Our answer classiﬁcation model was trained using the Stack Exchange dataset and validated using the Area Under the Curve (AUC) metric. The experimental results show that the proposed method complements previous methods by pointing out the importance of the relationships between users, particularly throughout the level of involvement in diﬀerent communities on Stack Exchange. Furthermore, we point out that there is little overlap between user-relation information and the information represented by the shallow text features and the meta-features, such as time diﬀerences.
Experiments on Generalizability of BERTopic on Multi-Domain Short Text

Topic modeling is widely used for analytically evaluating large collections of textual data. One of the most popular topic techniques is Latent Dirichlet Allocation (LDA), which is flexible and adaptive, but not optimal for e.g. short texts from various domains. We explore how the state-of-the-art BERTopic algorithm performs on short multi-domain text and find that it generalizes better than LDA in terms of topic coherence and diversity. We further analyze the performance of the HDBSCAN clustering algorithm utilized by BERTopic and find that it classifies a majority of the documents as outliers. This crucial, yet overseen problem excludes too many documents from further analysis. When we replace HDBSCAN with k-Means, we achieve similar performance, but without outliers.
Symmetries of Hypergraphs and Some Invariant Subspaces of Matrices Associated with Hypergraphs

Here, the structural symmetries of a hypergraph are represented through equivalence relations on the vertex set of the hypergraph. A matrix associated with the hypergraph may not reflect a specific structural symmetry. In the context of a given symmetry within a hypergraph, we investigate a collection of matrices that encapsulate information about the symmetry. Our investigation reveals that certain structural symmetries in a hypergraph manifest observable effects on the eigenvalues and eigenvectors of designated matrices associated with the hypergraph. We identify invariant subspaces of these specific matrices as a consequence of symmetries present in the hypergraph. These invariant subspaces elucidate analogous behaviours observed in certain clusters of vertices during random walks and other dynamical processes on the hypergraph.
Influence of cumulative damage on synchronization of Kuramoto oscillators on networks

In this paper, we study the synchronization of identical Kuramoto phase oscillators under cumulative stochastic damage to the edges of networks. We analyze the capacity of coupled oscillators to reach a coherent state from initial random phases. The process of synchronization is a global function performed by a system that gradually changes when the damage weakens individual connections of the network. We explore diverse structures characterized by different topologies. Among these are deterministic networks as a wheel or the lattice formed by the movements of the knight on a chess board, and random networks generated with the Erdős–Rényi and Barabási–Albert algorithms. In addition, we study the synchronization times of 109 non-isomorphic graphs with six nodes. The synchronization times and other introduced quantities are sensitive to the impact of damage, allowing us to measure the reduction of the capacity of synchronization and classify the effect of damage in the systems under study. This approach is general and paves the way for the exploration of the effect of damage accumulation in diverse dynamical processes in complex systems.
Distributionally robust chance-constrained Markov decision processes

Markov decision process (MDP) is a decision making framework where a decision maker is interested in maximizing the expected discounted value of a stream of rewards received at future stages at various states which are visited according to a controlled Markov chain. Many algorithms including linear programming methods are available in the literature to compute an optimal policy when the rewards and transition probabilities are deterministic. In this paper, we consider an MDP problem where the transition probabilities are known and the reward vector is a random vector whose distribution is partially known. We formulate the MDP problem using distributionally robust chance-constrained optimization framework under various types of moments based uncertainty sets, and statistical-distance based uncertainty sets defined using phi-divergence and Wasserstein distance metric. For each type of uncertainty set, we consider the case where a random reward vector has either a full support or a nonnegative support. For the case of full support, we show that the distributionally robust chance-constrained Markov decision process is equivalent to a second-order cone programming problem for the moments and phi-divergence distance based uncertainty sets, and it is equivalent to a mixed-integer second-order cone programming problem for an Wasserstein distance based uncertainty set. For the case of nonnegative support, it is equivalent to a copositive optimization problem and a biconvex optimization problem for the moments based uncertainty sets and Wasserstein distance based uncertainty set, respectively. As an application, we study a machine replacement problem and illustrate numerical experiments on randomly generated instances.
LINEAR-TIME ALGORITHM FOR THE FUZZY WEIGHTED AVERAGE METHOD

ABSTRACT The fuzzy weighted average operations are common operations in risk and decision analysis. Dong and Wang [6] proposed a fuzzy weighted average method (FWA) for the performance evaluation under group decision making, which ends up with an exponential complexity. Liou and Wang [11] proved that indeed the FWA could be simplified to be a non-constrained linear fractional programming problem (called IFWA) with lower and upper bounds for each variable. For thiss implified problem, a steepest descend/ascend method was proposed and required an O(n2)-time, where n is the number of decision variables in the IFWA. Later on, based on Liou and Wang's simplified framework, Guh et al. [8] proposed a max-min paired elimination method (PFWA), which essentially was a heuristic one for lacking of the proof of convergence. Recently, Lee and Park [10] proposed an algorithm requiring an O(n logn)-time. In this paper, we shall propose a new method for solving IFWA. Our algorithm requires an O(n)-time. Proof of convergence and an example are provided, and the efficiency of our method is not worse than Robillard's in practice.
Is Maximal Lactate Accumulation Rate Promising for Improving 5000-m Prediction in Running?

Abstract Endurance running performance can be predicted by maximal oxygen uptake (V̇O 2 max), the fractional utilisation of oxygen uptake (%V̇O 2 max) and running economy at lactate threshold (RE OBLA ). This study aims to assess maximal lactate accumulation rate (ċLa max ) in terms of improving running performance prediction in trained athletes. Forty-four competitive female and male runners/triathletes performed an incremental step test, a 100-m sprint test and a ramp test to determine their metabolic profile. Stepwise linear regression was used to predict 5000-m time trial performance. Split times were recorded every 200-m to examine the ‘finishing kick’. Females had a slower t 5k and a lower V̇O 2 max, ċLa max , ‘finishing kick’ and RE OBLA . Augmenting Joyner’s model by means of ċLa max explained an additional 4.4% of variance in performance. When performing the same analysis exclusively for males, ċLa max was not included. ċLa max significantly correlated with %V̇O 2 max (r=-0.439, p=0.003) and the ‘finishing kick’ (r=0.389, p=0.010). ċLa max allows for significant (yet minor) improvements in 5000-m performance prediction in a mixed-sex group. This margin of improvement might differ in middle-distance events. Due to the relationship to the ‘finishing kick’, ċLa max might be related to individual pacing strategies, which should be assessed in future research.
BELIEF TRANSFORMS AND THE COMPARISON OF HYPOTHESES

In this paper, we propose a systematic approach to the comparison of any two specifications and then extend the comparison to collections of specifications. The comparison derives from the eigenstructure of the belief transform, which is the fundamental object of study in this paper. We describe the construction of the belief transform within a subjectivist framework, but the methodology can be similarly applied to compare any stochastic specifications. We build the belief transform over general inner product spaces to allow the possibility of comparing partial belief specifications. In Section 1, we explain how belief transforms can be constructed in full generality and describe various properties of the transforms.
Evaluation of a Real-time PCR in Combination with a Cultivation Method for the Detection of Brucella Abortus in Organs of Infected Cattle in Southern Italy

Abstract Introduction Brucellosis is a widespread zoonosis of great economic importance for livestock farming in many areas of the world. It is a highly infectious disease which is diagnosed using conventional serological and microbiological methods. The aim of this study was to assess the efficiency of a specific real-time PCR in combination with broth cultivation in detecting Brucella spp. in organs of infected cattle, in order to compare the sensitivity of the two approaches and the time needed in them until a correct diagnosis is made. Material and Methods We examined 67 organs collected from 10 cattle slaughtered following a brucellosis outbreak which occurred in February 2016 in southern Italy. The research was carried out by enrichment broth cultivations in combination with a real-time PCR every week for six weeks. Results Brucella strains were isolated by cultivation from 44 enrichment broths of organs. All isolates were later identified as Brucella abortus by real-time PCR. Using this method in combination with cultivation made it possible to identify the same percentage of infected animals faster than by cultivation alone. Moreover, the same diagnostic results were obtained, on average two weeks before they would have been using only cultivation. In almost all cases, Brucella was detected by real-time PCR after the first week of cultivation in pre-enrichment Brucella broth, while the bacterial growth was evident usually after 2 or 3 weeks. Conclusion Real-time PCR has allowed results to be obtained faster than in the classical microbiological method, reducing the response times to identify positive animals by half.
Screening for Coxiella Burnetii in Dairy Cattle Herds in Poland

Abstract Introduction The intracellular bacterium Coxiella burnetii is the aetiological agent of Q fever, a zoonosis affecting many animal species worldwide. Cattle and small ruminants are considered the major reservoirs of the bacteria and they shed it through multiple routes. Material and Methods A total of 2,180 sera samples from 801 cattle herds in all Polish voivodeships were tested by ELISA for the presence of specific antibodies. Milk samples were obtained from seropositive cows in 133 herds as part of a separate study. The milk samples were examined by ELISA and real-time PCR tests. Results Seroprevalence at the animal level was 7.06% and true positive seroprevalence was 6.0% (95% confidence interval (CI) 1.1–9.4). Seroprevalence at the herd level was estimated at 11.1% and true positive seroprevalence was 10.5% (95% CI 3.2–15.8). Shedding of the pathogen in milk was detected by real-time PCR in 33 out of 133 tested herds (24.81%, 95% CI 17.74–33.04%) and the presence of C. burnetii antibodies was confirmed in 85 of them (63.9%, 95% CI 55.13–72.05%). The highest level of conformity between ELISA and real-time PCR results was obtained for bulk tank milk samples. Conclusion Coxiella burnetii infections are quite common in cattle herds across the country, which emphasises the crucial roles of surveillance and adequate biosecurity measures in the prevention and limitation of Q fever spread in Poland.
Target video intelligent processing system based on Raspberry Pi

In order to effectively solve the problems of insufficient storage capacity, too late to shoot wonderful moments, and limited range of shooting space, a target video intelligent processing system based on Raspberry Pi is proposed. The system runs on the Raspberry Pi, and drives the camera to shoot a wider range of indoor environment video streams through the servo gimbal. For each frame of image in the video stream, image grayscale, filter denoising and histogram, equalization techniques are used for preprocessing. In the target detection and tracking stage, first use the OpenCV machine vision library to call the MobileNet lightweight convolutional neural network and SSD algorithm combined model (MobileNetSSD) for target detection, then it can alculate the relative position of the camera center focus and the target center, and finally drive the camera to track the target object. In terms of video processing, with the help of the results obtained during target detection, the automatic video editing process is completed by discarding the image frame without target object. Experiments show that the system can quickly and accurately track the target object to shoot, and effectively reduce the storage capacity of the video.
Speech emotion recognition based on SE-ResNet

Emotion recognition plays an important role in the field of human-computer interaction. It can help robots understand human needs more accurately. However, the impact of noise signal and model architecture on accuracy has not been fully explored. In addition, individual datasets are often used for algorithm testing, making it challenging to ensure algorithm generalization. To address these issues, we explore the impact of noise and algorithms on emotion recognition tasks based on two datasets. First, we use SE-ResNet as an emotion recognition network, which guarantees the effectiveness of the algorithm through the attention mechanism and residual structure. Experiments show that SE-ResNet performs better than other classical convolutional neural networks, and it validates the effectiveness of the attention mechanism. Second, we verify that noise can cause the algorithm to lose accuracy by setting up experiments with or without noise. Besides that, we analyze noise’s effect for each emotion class by the confusion matrix. The results show that noise has the most significant impact on the recognition accuracy of natural emotion.
Body size classification of the egg parasitoid Trichogramma pretiosum (Hymenoptera: Trichogrammatidae)

Abstract Parasitoid body size has been used as an indicator of fitness and its capability to parasitise hosts in the field, but studies that have investigated this relationship have disagreed. In the present study, we investigated the relationship between the two most common body parts used in body size classification and tried to select groups of small and large parasitoids by discriminant analysis. We found that groups with small or large individuals differ from each other only when the two body parts were used to classify them. We conclude that using just one body part to estimate the size of Trichogramma pretiosum Riley, 1879 (Hymenoptera: Trichogrammatidae) could be misleading and that caution is needed when defining small and large individuals.
Existence of Many Nonequivalent Nonradial Positive Solutions of Semilinear Elliptic Equations on Three-Dimensional Annuli

We consider a semilinear elliptic equation,Δu+up=0 onΩR≡{x∈Rn∣R−1 2. We prove that, when the space dimension n is three, the number of nonequivalent nonradial positive solutions of the equation goes to ∞ asR→∞. The same result has been known forn=2 andn⩾4; in those cases, the result was obtained by showing that the minimal energy solutions in various symmetry classes have different energy levels. As we will show in this paper, this is not true ifn=3. This makes the casen=3 highly exceptional, and explains why past attempts failed in this case. In this paper we will prove the above result by considering local—rather than global—minimizers in some symmetry classes.
Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series

ChowLin distributes a series, changing the frequency to a higher one while maintaining the sum over each period, using the Chow-Lin(1971) or related procedure. The newer procedure disaggregate.src is a better choice. Chow and Lin(1971), "Best Linear Unbiased Interpolation, Distribution and Extrapolation of Time Series by Related Series", Review of Economics and Statistics, vol 53, 372-375. Fernandez(1981), "A Methodological Note on the Estimation of Time Series", Review of Economics and Statistics, vol 63, 471-478. Litterman(1983), "A Random Walk, Markov Model for the Distribution of Time Series", JBES, vol 1, 169-173. (This abstract was borrowed from another version of this item.)