Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Futility of Blind Search

This paper might have been subtitled An algorithmicist looks at no free lunch We use simple adversary arguments to redevelop and explore some of the no free lunch NFL theorems and perhaps extend them a lit tle A second goal is to clarify the relationship of NFL theorems to algorithm theory In particular we claim that NFL puts much weaker restrictions on the claims that an evolutionary algorithm can make than does acceptance of the conjectures of traditional complexity the ory And third we take a brief look at whether the notion of natural evolution relates to optimization and what if any the implications of evolution are for computing In this part we mostly try to raise questions concerning the validity of applying the genetic model to the problem of optimization This is an informal paper most of the information presented is not formally proven and is either common knowledge or formally proven elsewhere Some of the claims are intuitions based on experience with algorithms and in a more formal setting should be classi ed as conjectures The goal is not so much to develop theory as it is to perhaps persuade the reader to adopt a particular viewpoint
The Impact of Social Support on Social Identity Development and Well-Being in International Exchange Students

International exchanges have become increasingly common. Although it is an explicit goal of exchange programs that exchange students immerse in another culture and learn to feel like a local, there is no systematic research on the development of one’s social identity during the exchange year. A longitudinal study with German high school students who spend an exchange year in the United States investigates the trajectories of social identification, identity integration of primary and secondary cultural identities, and well-being longitudinally at three measurement times (before departure, N = 556, 3 months, N = 210, and 6 months after arrival, N = 178). As social support is suggested to be an important resource, multilevel analyses tested the impact of three subcomponents of social support (i.e., emotional, instrumental, and compatibility-informational support) on the individual trajectories. It was found that social identification and identity integration increased over time, whereas well-being was high across all times. Identity integration mediated the positive effect of social identification on well-being. Moreover, emotional support was positively related to well-being and compatibility-informational support was positively related to identity integration. Instrumental support fostered early identity integration but diminished its slope when applied at later times. The present data demonstrate that an exchange year stimulates the development of the social self-concept, which is relevant for well-being. Moreover, it shows that the right kind of social support at the right time can foster this development.
Social Class and Socialization Values in the United States and China

The psychological correlate of social class across cultures is a topic of debate. Some have argued cross-cultural similarities, while others have maintained culturally divergent manifestations of social class. Using the data from the World Value Survey 2017 to 2020 (Wave 7), the current study examined the associations between social class and socialization values among parents in the United States (n = 1,615) and China (n = 2,524). Results indicated that all social class indices (i.e., education level, income, subjective social status, and composite social class) were positively associated with self-oriented socialization values in the United States, whereas such associations were absent in China, except for education level. In addition, higher social class, in terms of higher income, higher subjective social status, and higher composite social class, was associated with greater other-oriented socialization values in China. However, such associations were absent (for income, education level, and composite social class) or even reversed (for subjective social status) in the United States. These findings extend the literature on social class variations in parenting and highlight cultural specificity in the psychological manifestations of social class.
Determining EMG embedding and fractal dimensions and its application

An electromyogram (EMG) records a myoelectric signal sampled from the surface of skeletal muscles, a complex nonlinear system. In order to study its dynamics, analysis methods based on phase space reconstruction and fractal theory are discussed. The methods focus on determination of the embedding dimension and fractal dimension of EMG. It is shown that embedding and fractal dimensions reflect the human muscle's potential in athletics. Furthermore, by comparison, we find that both dimensions seem to have consistent experimental results. It hints that this kind of analysis may be helpful in evaluation of athletes.
Laplacian eigenmodes for the three-sphere

The vector space of the eigenfunctions of the Laplacian on the three-sphere S3, corresponding to the same eigenvalue λk = −k(k + 2), has dimension (k + 1)2. After recalling the standard bases for , we introduce a new basis B3, constructed from the reductions to S3 of a peculiar homogeneous harmonic polynomial involving null vectors. We give the transformation laws between this basis and the usual hyper-spherical harmonics. Thanks to the quaternionic representations of S3 and SO(4), we are able to write explicitly the transformation properties of B3, and thus of any eigenmode, under an arbitrary rotation of SO(4). This offers the possibility of selecting those functions of which remain invariant under a chosen rotation of SO(4). When the rotation is a holonomy transformation of a spherical space S3/Γ, this gives a method for calculating the eigenmodes of S3/Γ, which remains an open problem in general. We illustrate our method by (re-)deriving the eigenmodes of lens and prism space. In a companion paper, we present the derivation for dodecahedral space.
A Borsuk-Ulam Lower Bound for Sign-Rank and Its Applications

We introduce a new topological argument based on the Borsuk-Ulam theorem to prove a lower bound on sign-rank. This result implies the strongest possible separation between randomized and unbounded-error communication complexity. More precisely, we show that for a particular range of parameters, the randomized communication complexity of the Gap Hamming Distance problem is O(1) while its unbounded-error communication complexity is Ω(log(n)). Previously, it was unknown whether the unbounded-error communication complexity could be asymptotically larger than the randomized communication In connection to learning theory, we prove that, despite its learnability properties, the class of large margin half-spaces in ℝd is genuinely high-dimensional, i.e., it cannot be embedded in d−1. This result is closely related to a recent conjecture of Alon, Hanneke, Holzman, and Moran (FOCS 2021) about the VC dimension of this class. Our final application is to the theory of dimension reductions. The Johnson-Lindenstrauss theorem implies that any set of N unit vectors is embeddable in dimension O(γ−2logN) without altering the signs of those pairwise inner products that have absolute values at least γ>0. Our result establishes the tightness of this bound, which answers a question of Linial, Mendelson, Schechtman, and Shraibman (Combinatorica, 27(2007)) in the case of partial functions.
On the Optimal Interpolation Weights for Hybrid Autoregressive Transducer Model

This paper explores rescoring strategies to improve a two-pass speech recognition system when the ﬁrst-pass is a hybrid autoregressive transducer model and the second-pass is a neural language model. The main focus is on the scores provided by each of these models, their quantitative analysis, how to improve them and the best way to combine them to achieve better recognition accuracy. Several analyses are presented to emphasize the importance of the choice of the integration weights for combining the ﬁrst-pass and the second-pass scores. A sequence level combination weight estimation model along with four training criteria are proposed which allows adaptive integration of the scores per acoustic sequence. The effectiveness of this algorithm is demonstrated by constructing and analyzing models on the Librispeech data set. It is shown that the proposed adaptive weight interpolation technique achieves 5 % relative gain over the baseline model with non-adaptive weights.
Air-coupled thickness measurements of stainless steel

A method of measuring the thickness of steel plates using through transmission of an acoustic pulse is demonstrated. This study has been done on a stainless steel plate with regions of thickness 10:0 mm, 9:8 mm, and 9:6 mm, using broadband pulses with energy in 200 kHz to 600 kHz band. Ultimately the goal is to perform similar air-coupled thickness measurements in a single sided pitch-catch measurement setup. The spectra of the transmitted pulses show the first and second harmonics of the compressional waves in the plate. When compared to a plane wave model of a fluid layer embedded in air, the second harmonic of the plate resonance fits well with the expected value. However, the first harmonic deviates such that the plate appears thicker at this resonance. This is believed to be caused by the finite aperture of the transmitting transducer, causing deviations from a plane wave. Thickness differences of 0:2 mm between the different regions of the plate were shown to be resolved. A third peak was found in the spectra. The origin of this peaks has not been verified, but is believed to come from the third harmonic of the shear wave in the steel plate.
Algorithmic Diversity and Tiny Models: Comparing Binary Networks and the Fruit Fly Algorithm on Document Representation Tasks

Neural language models have seen a dramatic increase in size in the last years. While many still advocate that ‘bigger is better’, work in model distillation has shown that the number of parameters used by very large networks is actually more than what is required for state-of-the-art performance. This prompts an obvious question: can we build smaller models from scratch, rather than going through the inefficient process of training at scale and subsequently reducing model size. In this paper, we investigate the behaviour of a biologically inspired algorithm, based on the fruit fly’s olfactory system. This algorithm has shown good performance in the past on the task of learning word embeddings. We now put it to the test on the task of semantic hashing. Specifically, we compare the fruit fly to a standard binary network on the task of generating locality-sensitive hashes for text documents, measuring both task performance and energy consumption. Our results indicate that the two algorithms have complementary strengths while showing similar electricity usage.
Controlling Pretrained Language Generation Models by Learning to Focus

Transformer-based language models, which are 001 pretrained on large-scale unsupervised data and 002 then finetuned on task-specific datasets, have 003 become the dominant paradigm for various nat-004 ural language generation tasks. The finetuning 005 and usages of such models are typically con-006 ducted in an end-to-end manner. This work 007 attempts to develop a control mechanism by 008 which a user can select spans of context as 009 “highlights” for the model to focus on, while 010 generating output text. To achieve this goal, we 011 augment a pretrained model with trainable “at-012 tention vectors” that are directly applied to the 013 model’s embeddings, while the model itself is 014 kept fixed. These vectors, trained on automatic 015 annotations derived from attribution methods, 016 act as indicators for context importance. We 017 test our approach on two core generation tasks: 018 dialogue response generation and abstractive 019 summarization. We also collect evaluation data 020 where the highlight-generation pairs are anno-021 tated by humans. Our experiments show that 022 the trained attention vectors are effective in 023 steering the model to generate outputs that are 024 relevant to user-selected highlights. 025
Improving Paraphrase Generation models with machine translation generated pre-training

Paraphrase generation is a fundamental and 001 longstanding problem in the Natural Language 002 Processing field. With the huge success of pre-003 trained transformers, the pre-train–fine-tune ap-004 proach has become a standard choice. At the 005 same time, popular task-agnostic pre-trainings 006 usually require terabyte datasets and hundreds 007 of GPUs, while available pre-trained models 008 are limited to architecture and size. We propose 009 a simple and efficient pre-training approach 010 specifically for paraphrase generation, which 011 noticeably boosts model quality and doesn’t 012 require significant computing power. We also 013 investigate how this procedure influences the 014 scores across different architectures and show 015 that it helps them all. 016
Unsupervised Common Sense Relation Extraction

Vast and diverse knowledge about the rela-001 tions in the world help humans comprehend 002 and argue about their environment. Equipping 003 machines with this knowledge is challenging 004 yet essential for general reasoning capabilities. 005 Here, we propose to apply unsupervised rela-006 tion extraction (URE), aiming to induce gen-007 eral relations between concepts from natural 008 language. Previous work in URE has predom-009 inantly focused on relations between named 010 entities in the encyclopedic domain. The more 011 general, and more challenging, domain of com-012 mon sense relation learning has not yet been 013 addressed, partially due to a lack of datasets. 014 We present a framework for common sense re-015 lation extraction from free-text, associated with 016 two benchmark datasets. We present initial ex-017 periments using three state-of-the-art models 018 developed for encyclopedic relation induction. 019 Our results verify the utility of our benchmarks 020 for common sense relation extraction, and sug-021 gest ample scope for future work on this impor-022 tant, yet challenging, task. 1 023
Application of network pharmacology in the study of mechanism of Chinese medicine in the treatment of ulcerative colitis: A review

Network pharmacology is a research method based on a multidisciplinary holistic analysis of biological systems, which coincides with the idea of the holistic view of traditional Chinese medicine. In this review, we summarized the use of network pharmacology technology through studying Chinese medicine single medicine or Chinese medicine compound research ideas and methods for the treatment of ulcerative colitis, based on the application of the current network pharmacology in Chinese medicine research, including the important role in the mechanism of the prediction and verification, to search for new ideas for disease diagnosis and treatment, this study summarizes the application of network pharmacology in the treatment of ulcerative colitis in traditional Chinese medicine, including monotherapy and compound therapy, and considers that relevant research studies have fully demonstrated the function characteristics of the multi-component, multi-target, and multi-pathway of traditional Chinese medicine, and can also explain the connotation of “selecting appropriate treatment methods according to the differences and similarities of pathogenesis” of traditional Chinese medicine. Finally, we raised important questions about the prospects and limitations of network pharmacology, such as differences caused by different data collection methods, a considerable lag, and so on.
Characteristics associated with treatment seeking for smoking cessation among heavy-drinking research participants

Objective Treatment seeking for smoking cessation has tremendous clinical implications with the potential to reduce tobacco-related morbidity and mortality. The present study seeks to elucidate clinical variables that distinguish treatment seeking versus non-treatment seeking status for smoking cessation in a large sample of heavy drinking smokers using data-driven methods. Materials and methods This secondary data analysis examines n = 911 (n = 267 female) individuals who were daily smokers and heavy drinkers (≥ 7 drinks per week for women, ≥ 14 for men) that were enrolled in either a treatment-seeking study (N = 450) or a non-treatment seeking study (N = 461) using identical pharmacotherapies. Participants completed measures of demographics, alcohol and cigarette use, alcohol craving, the Barratt Impulsiveness Scale (BIS-11), and the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68). These measures were used in a random forest model to identify predictors of treatment seeking status. Results The top variables of importance in identifying treatment seeking status were: age, drinks per drinking day, cigarettes per smoking day, BIS-11 cognitive impulsivity, WISDM social environmental goads, WISDM loss of control, WISDM craving, and WISDM tolerance. Age and drinks per drinking day were two of the most robust predictors, followed by measures of nicotine craving and tolerance. Conclusion Individuals who are daily smokers and consume more drinks per drinking day are less likely to belong to the smoking cessationtreatment-seeking group. Targeting heavy drinking smokers, particularly younger individuals, may be necessary to engage this group in smoking cessation efforts and to reduce the burden of disease of nicotine dependence earlier in the lifespan.
SMRT and NCoR1 fine-tune inflammatory versus tolerogenic balance in dendritic cells by differentially regulating STAT3 signaling

Dendritic cell (DC) fine-tunes inflammatory versus tolerogenic responses to protect from immune-pathology. However, the role of co-regulators in maintaining this balance is unexplored. NCoR1-mediated repression of DC immune-tolerance has been recently reported. Here we found that depletion of NCoR1 paralog SMRT (NCoR2) enhanced cDC1 activation and expression of IL-6, IL-12 and IL-23 while concomitantly decreasing IL-10 expression/secretion. Consequently, co-cultured CD4+ and CD8+ T-cells depicted enhanced Th1/Th17 frequency and cytotoxicity, respectively. Comparative genomic and transcriptomic analysis demonstrated differential regulation of IL-10 by SMRT and NCoR1. SMRT depletion represses mTOR-STAT3-IL10 signaling in cDC1 by down-regulating NR4A1. Besides, Nfkbia and Socs3 were down-regulated in Ncor2 (Smrt) depleted cDC1, supporting increased production of inflammatory cytokines. Moreover, studies in mice showed, adoptive transfer of SMRT depleted cDC1 in OVA-DTH induced footpad inflammation led to increased Th1/Th17 and reduced tumor burden after B16 melanoma injection by enhancing oncolytic CD8+ T-cell frequency, respectively. We also depicted decreased Ncor2 expression in Rheumatoid Arthritis, a Th1/Th17 disease.
Amount of bird suitable areas under climate change is modulated by morphological, ecological and geographical traits

Biodiversity is already experiencing the effects of climate change through range expansion, retraction, or relocation, potentializing negative effects of other threats. Future projections already indicate richness reduction and composition modifications of bird communities due to global warming, which may disrupt the provision of key ecological services to ecosystem maintenance. Here, we systematically review the effect of morphological, ecological, and geographical traits on the amount of future suitable area for birds worldwide. Specifically, we tested whether body mass, diet, habitat type, movement pattern, range size, and biogeographic realm affect birds' suitable area. Our search returned 75 studies that modeled the effects of climate change on 1,991 bird species. Our analyses included 1,661 species belonging to 128 families, representing 83% of the total, for which we were able to acquire all the six traits. The proportion of birds' suitable area was affected by range size, body mass, habitat type, and biogeographic realm, while diet and movement pattern showed lower relative importance and were not included in our final model. Contrary to expectations, the proportion of birds' suitable area was negatively related to range size, which may be explained by higher climatic stability predicted in certain areas that harbor species with restricted distribution. In contrast, we observed that birds presenting higher body mass will show an increase of the proportion of suitable area in the future. This is expected due to the high exposure of smaller birds to environmental changes and their difficulty to keep thermoregulation. Our results also indicated a low proportion of suitable area to forest-dependent birds, which is in accordance with their higher vulnerability due to specific requirements for reproduction and feeding. Finally, the proportion of suitable area was low for birds from Oceania, which is expected since the region encompasses small islands isolated from continents, preventing their species from reaching new suitable areas. Our study highlights that different traits should be considered when assessing extinction risk of species based on future projections, helping to improve bird conservation, especially the most vulnerable to climate change.
Resistance training and Down Syndrome: A narrative review on considerations for exercise prescription and safety

The current manuscript reviews the literature on the health effects of resistance training (RT) for individuals with Down syndrome (DS), focusing on this training modality’s methodology, application, and safety. The literature has mentioned that early aging in this population is associated with loss of muscle strength, lower lean and bone mass, and increased obesity. It is necessary to propose non-pharmacological measures for prevention and health promotion. Thus, this review suggests a current research-based RT guide for individuals with DS. This review is divided into three sections: Section 2 briefly reviews DS and the effects on structural and functional decline and how exercise and physical activity can influence health aspects in this population; Section 3 summarizes the evidence for RT prescription; Section 4 briefly reviews the health and potential benefits of RT in individuals with DS. The findings from this review suggest that most individuals with DS should engage in moderate-intensity RT at least 2 days a week and perform RT on the major muscle groups and include balance training. The RT program should be modified and adapted according to individuals’ characteristics and limitations. RT promotes positive, health-related benefits such as increasing strength, improving body composition, improving functional capacity and balance, reducing inflammatory status and oxidative stress, and improving the immune system. The RT protocols summarized in this current review provide guidance, critical conclusions, and novel research settings, which could be useful to coaches, clinicians, and researchers to effectively design RT program for individuals with DS.