Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective and scalable programs to facilitate labor market transitions for women in technology

We describe the design, implementation, and evaluation of a low-cost (approximately $15 per person) and scalable program, called Challenges, aimed at aiding women in Poland transition to technology-sector jobs. This program helps participants develop portfolios demonstrating job-relevant competencies. We conduct two independent evaluations, one of the Challenges program and the other of a traditional mentoring program -- Mentoring -- where experienced tech professionals work individually with mentees to support them in their job search. Exploiting the fact that both programs were oversubscribed, we randomized admissions and measured their impact on the probability of finding a job in the technology sector. We estimate that Mentoring increases the probability of finding a technology job within four months from 29% to 42% and Challenges from 20% to 29%, and the treatment effects do not attenuate over 12 months. Since both programs are capacity constrained in practice (only 28% of applicants can be accommodated), we evaluate the effectiveness of several alternative prioritization rules based on applicant characteristics. We find that a policy that selects applicants based on their predicted treatment effects increases the average treatment effect across the two programs to 22 percentage points. We further analyze how alternative prioritization rules compare to the selection that mentors used. We find that mentors selected applicants who were more likely to get a tech job even without participating in the program, and the treatment effect for applicants with similar characteristics to those selected by mentors is about half of the effect attainable when participants are prioritized optimally.
Interstellar Gas-phase Element Depletions in the Small Magellanic Cloud: A Guide to Correcting for Dust in QSO Absorption Line Systems

We present data on the gas-phase abundances for 9 different elements in the interstellar medium of the Small Magellanic Cloud (SMC), based on the strengths of ultraviolet absorption features over relevant velocities in the spectra of 18 stars within the SMC. From this information and the total abundances defined by the element fractions in young stars in the SMC, we construct a general interpretation on how these elements condense into solid form onto dust grains. As a group, the elements Si, S, Cr, Fe, Ni, and Zn exhibit depletion sequences similar to those in the local part of our Galaxy defined by Jenkins. The elements Mg and Ti deplete less rapidly in the SMC than in the Milky Way, and Mn depletes more rapidly. We speculate that these differences might be explained by the different chemical affinities to different existing grain substrates. For instance, there is evidence that the mass fractions of polycyclic aromatic hydrocarbons in the SMC are significantly lower than those in the Milky Way. We propose that the depletion sequences that we observed for the SMC may provide a better model for interpreting the element abundances in low-metallicity Damped Lyman Alpha (DLA) and sub-DLA absorption systems that are recorded in the spectra of distant quasars and gamma-ray burst afterglows.
A Dataset for Hyper-Relational Extraction and a Cube-Filling Approach

Relation extraction has the potential for large-scale knowledge graph construction, but current methods do not consider the qualifier attributes for each relation triplet, such as time, quantity or location. The qualifiers form hyper-relational facts which better capture the rich and complex knowledge graph structure. For example, the relation triplet (Leonard Parker, Educated At, Harvard University) can be factually enriched by including the qualifier (End Time, 1967). Hence, we propose the task of hyper-relational extraction to extract more specific and complete facts from text. To support the task, we construct HyperRED, a large-scale and general-purpose dataset. Existing models cannot perform hyper-relational extraction as it requires a model to consider the interaction between three entities. Hence, we propose CubeRE, a cube-filling model inspired by table-filling approaches and explicitly considers the interaction between relation triplets and qualifiers. To improve model scalability and reduce negative class imbalance, we further propose a cube-pruning method. Our experiments show that CubeRE outperforms strong baselines and reveal possible directions for future research. Our code and data are available at github.com/declare-lab/HyperRED.
An Investigation of Open Clusters Berkeley 68 and Stock 20 Using CCD UBV and Gaia DR3 Data

We performed detailed photometric and astrometric analyses of the open star clusters Berkeley 68 and Stock 20. This was based on ground-based CCD UBV photometric data complemented by space-based Gaia Data Release 3 (DR3) photometry and astrometry. A total of 198 and 51 stars were identified as likely cluster members for Berkeley 68 and Stock 20, respectively. Two-color diagrams were used to derive the reddening and photometric metallicity for each cluster. The reddening for Berkeley 68 and Stock 20 is E(B − V) = 0.520 ± 0.032 mag and 0.400 ± 0.048 mag, respectively. Photometric metallicity [Fe/H] is −0.13 ± 0.08 dex for Berkeley 68 and −0.01 ± 0.06 dex for Stock 20. Keeping as constant reddening and metallicity, we determined the distance moduli and ages of the clusters through fitting isochrones to the UBV and Gaia-based color–magnitude diagrams. Photometric distances are d = 3003 ± 165 pc for Berkeley 68 and 2911 ± 216 pc for Stock 20. The cluster ages are 2.4 ± 0.2 Gyr and 50 ± 10 Myr for Berkeley 68 and Stock 20, respectively. Present-day mass function slopes were found to be Γ = 1.38 ± 0.71 and Γ = 1.53 ± 0.39 for Berkeley 68 and Stock 20, respectively. These values are compatible with the value of Salpeter. The relaxation times were estimated as 32.55 and 23.17 Myr for Berkeley 68 and Stock 20, respectively. These times are less than the estimated cluster ages, indicating that both clusters are dynamically relaxed. Orbit integration was carried out only for Berkeley 68 since radial velocity data were not available for Stock 20. Analysis indicated that Berkeley 68 was born outside the solar circle and belongs to the thin-disk component of the Milky Way.
1000 au exterior arcs connected to the protoplanetary disk around HL Tauri

Aims. The protoplanetary disk around HL Tau is the youngest candidate of planet formation known to date, and it is still embedded in a protostellar envelope with a size of thousands of au. In this work, we study the gas kinematics in the envelope and its possible influence on the embedded disk. Methods. We present our new ALMA cycle 3 observational results of HL Tau in the 13 CO (2–1) and C 18 O (2–1) emission at resolutions of 0.8 (110 au), and we compare the observed velocity pattern with models of different types of gas motion. Results. The 13 CO and C 18 O emission lines both show a central compact component with a size of 2″ (280 au) that traces the protoplanetary disk. The disk is clearly resolved and shows Keplerian motion, based on which, the protostellar mass of HL Tau is estimated to be 1.8 ± 0.3 M ⊙ , assuming the inclination angle of the disk to be 47° from the plane of the sky. The 13 CO emission shows two arc structures with sizes of 1000–2000 au and masses of 3 × 10 -3 M ⊙ connected to the central disk. One is blueshifted and stretches from the northeast to the northwest, and the other is redshifted and stretches from the southwest to the southeast. We find that simple kinematical models of infalling and (counter-)rotating flattened envelopes cannot fully explain the observed velocity patterns in the arc structures. The gas kinematics of the arc structures can be better explained with three-dimensional infalling or outflowing motions. Nevertheless, the observed velocity in the northwestern part of the blueshifted arc structure is ~60–70% higher than the expected free-fall velocity. We discuss two possible origins of the arc structures: (1) infalling flows externally compressed by an expanding shell driven by XZ Tau; and (2) outflowing gas clumps caused by gravitational instabilities in the protoplanetary disk around HL Tau.
Magic3D: High-Resolution Text-to-3D Content Creation

DreamFusion [31] has recently demonstrated the utility of a pretrained text-to-image diffusion model to optimize Neural Radiance Fields (NeRF) [23], achieving remarkable text-to-3D synthesis results. However, the method has two inherent limitations: (a) extremely slow optimization of NeRF and (b) low-resolution image space supervision on NeRF, leading to low-quality 3D models with a long processing time. In this paper, we address these limitations by utilizing a two-stage optimization framework. First, we obtain a coarse model using a low-resolution diffusion prior and accelerate with a sparse 3D hash grid structure. Using the coarse representation as the initialization, we further optimize a textured 3D mesh model with an efficient differentiable renderer interacting with a high-resolution latent diffusion model. Our method, dubbed Magic3D, can create high quality 3D mesh models in 40 minutes, which is 2× faster than DreamFusion (reportedly taking 1.5 hours on average), while also achieving higher resolution. User studies show 61.7% raters to prefer our approach over DreamFusion. Together with the image-conditioned generation capabilities, we provide users with new ways to control 3D synthesis, opening up new avenues to various creative applications.
DGD-cGAN: A Dual Generator for Image Dewatering and Restoration

Underwater images are usually covered with a blue-greenish colour cast, making them distorted, blurry or low in contrast. This phenomenon occurs due to the light attenuation given by the scattering and absorption in the water column. In this paper, we present an image enhancement approach for dewatering which employs a conditional generative adversarial network (cGAN) with two generators. Our Dual Generator Dewatering cGAN (DGD-cGAN) removes the haze and colour cast induced by the water column and restores the true colours of underwater scenes whereby the effects of various attenuation and scattering phenomena that occur in underwater images are tackled by the two generators. The first generator takes at input the underwater image and predicts the dewatered scene, while the second generator learns the underwater image formation process by implementing a custom loss function based upon the transmission and the veiling light components of the image formation model. Our experiments show that DGD-cGAN consistently delivers a margin of improvement as compared with the state-of-the-art methods on several widely available datasets.
TensAIR: Online Learning from Data Streams via Asynchronous Iterative Routing

Online learning (OL) from data streams is an emerging area of research that encompasses numerous challenges from stream processing, machine learning, and networking. Recent extensions of stream-processing platforms, such as Apache Kafka and Flink, already provide basic extensions for the training of neural networks in a stream-processing pipeline. However, these extensions are not scalable and flexible enough for many real-world use-cases, since they do not integrate the neural-network libraries as a first-class citizen into their architectures. In this paper, we present TensAIR, which provides an end-to-end dataflow engine for OL from data streams via a protocol to which we refer as asynchronous iterative routing. TensAIR supports the common dataflow operators, such as Map, Reduce, Join, and has been augmented by the data-parallel OL functions train and predict. These belong to the new Model operator, in which an initial TensorFlow model (either freshly initialized or pre-trained) is replicated among multiple decentralized worker nodes. Our decentralized architecture allows TensAIR to efficiently shard incoming data batches across the distributed model replicas, which in turn trigger the model updates via asynchronous stochastic gradient descent. We empirically demonstrate that TensAIR achieves a nearly linear scale-out in terms of (1) the number of worker nodes deployed in the network, and (2) the throughput at which the data batches arrive at the dataflow operators. We exemplify the versatility of TensAIR by investigating both sparse (Word2Vec) and dense (CIFAR-10) use-cases, for which we are able to demonstrate very significant performance improvements in comparison to Kafka, Flink, and Horovod. We also demonstrate the magnitude of these improvements by depicting the possibility of real-time concept drift adaptation of a sentiment analysis model trained over a Twitter stream.
Narrowband HST Imagery of the Young Planetary Nebula Henize 1357

We present Hubble Space Telescope (HST) Planetary Camera observations showing the first optically resolved images of the planetary nebula Henize 1357 . The small size of this object has heretofore precluded optical investigations of its morphology. In H-beta, He 3-1357 appears to have an equatorial ring of enhanced density tilted approximately 56 deg from our line of sight. In addition, there are bubbles of gas above and below the ring with areas of decreased brightness near the poles where a fast stellar wind has broken through the red giant envelope. He 3-1357 has been evolving very rapidly, becoming a planetary nebula only within the past few decades. Assuming a distance of 5.6 kpc, the radius of the nebula is 0.02 pc, implying an expansion age of 2650 yr, although it has been ionized for no more than 40 yr. The H-beta flux is now 5.8 x 10(exp -11) ergs/sq cm/s which implies an ionized mass of 0.2 solar mass, log L*/solar luminosity = 3.7, and a core mass of 0.59 solar mass.
GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation

We introduce GENIUS: a conditional text generation model using sketches as input, which can fill in the missing contexts for a given sketch (key information consisting of textual spans, phrases, or words, concatenated by mask tokens). GENIUS is pre-trained on a large-scale textual corpus with a novel reconstruction from sketch objective using an extreme and selective masking strategy, enabling it to generate diverse and high-quality texts given sketches. Comparison with other competitive conditional language models (CLMs) reveals the superiority of GENIUS's text generation quality. We further show that GENIUS can be used as a strong and ready-to-use data augmentation tool for various natural language processing (NLP) tasks. Most existing textual data augmentation methods are either too conservative, by making small changes to the original text, or too aggressive, by creating entirely new samples. With GENIUS, we propose GeniusAug, which first extracts the target-aware sketches from the original training set and then generates new samples based on the sketches. Empirical experiments on 6 text classification datasets show that GeniusAug significantly improves the models' performance in both in-distribution (ID) and out-of-distribution (OOD) settings. We also demonstrate the effectiveness of GeniusAug on named entity recognition (NER) and machine reading comprehension (MRC) tasks. (Code and models are publicly available at https://github.com/microsoft/SCGLab and https://github.com/beyondguo/genius)
Dynamics of reverse saturable absorption and all-optical switching in C 60

The dynamic processes of reverse saturable absorption (RSA) in a solution of C60 are studied in detail by use of rate equations and the light-propagation equation for a five-energy-level model. The RSA experimental results for C60 in toluene solution with a nanosecond–picosecond Nd:YAG laser at 532 nm are consistent with the theoretical simulations. Effects of sample concentration and thickness on RSA and contributions of the singlet and triplet excited states to RSA for various laser-pulse widths and wavelengths are also analyzed. Finally, an all-optical switch based on excited-state nonlinear absorption and the pump-probe technique is implemented.
A Structure-Guided Diffusion Model for Large-Hole Diverse Image Completion

Diverse image completion, a problem of generating various ways of ﬁlling incomplete regions ( i.e . holes) of an image, has made remarkable success. However, managing input images with large holes is still a challenging problem due to the corruption of semantically important structures. In this paper, we tackle this problem by incorporating explicit structural guidance. We propose a structure-guided diffusion model (SGDM) for the large-hole diverse completion problem. Our proposed SGDM consists of a structure generator and a texture generator, which are both diffusion probabilistic models (DMs). The structure generator generates an edge image representing a plausible structure within the holes, which is later used to guide the texture generation process. To jointly train these two generators, we design a strategy that combines optimal Bayesian denoising and a momentum framework. In addition to the quality improvement, auxiliary edge images generated by the structure generator can be manually edited to allow user-guided image editing. Our experiments using datasets of faces (CelebA-HQ) and natural scenes (Places) show that our method achieves a comparable or superior trade-off between visual quality and diversity compared to other state-of-the-art methods.
CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval

Multi-vector retrieval methods combine the merits of sparse (e.g. BM25) and dense (e.g. DPR) retrievers and have achieved state-of-the-art performance on various retrieval tasks.These methods, however, are orders of magnitude slower and need much more space to store their indices compared to their single-vector counterparts.In this paper, we unify different multi-vector retrieval models from a token routing viewpoint and propose conditional token interaction via dynamic lexical routing, namely CITADEL, for efficient and effective multi-vector retrieval.CITADEL learns to route different token vectors to the predicted lexical keys such that a query token vector only interacts with document token vectors routed to the same key.This design significantly reduces the computation cost while maintaining high accuracy.Notably, CITADEL achieves the same or slightly better performance than the previous state of the art, ColBERT-v2, on both in-domain (MS MARCO) and out-of-domain (BEIR) evaluations, while being nearly 40 times faster. Source code and data are available at https://github.com/facebookresearch/dpr-scale/tree/citadel.
Rotational spectroscopy of the HCCO and DCCO radicals in the millimeter and submillimeter range

The ketenyl radical, HCCO, has recently been detected in the ISM for the first time. Further astronomical detections of HCCO will help us understand its gas-grain chemistry, and subsequently revise the oxygen-bearing chemistry towards dark clouds. Moreover, its deuterated counterpart, DCCO, has never been observed in the ISM. HCCO and DCCO still lack a broad spectroscopic investigation, although they exhibit a significant astrophysical relevance. In this work we aim to measure the pure rotational spectra of the ground state of HCCO and DCCO in the millimeter and submillimeter region, considerably extending the frequency range covered by previous studies. The spectral acquisition was performed using a frequency-modulation absorption spectrometer between 170 and 650 GHz. The radicals were produced in a low-density plasma generated from a select mixture of gaseous precursors. For each isotopologue we were able to detect and assign more than 100 rotational lines. The new lines have significantly enhanced the previous data set allowing the determination of highly precise rotational and centrifugal distortion parameters. In our analysis we have taken into account the interaction between the ground electronic state and a low-lying excited state (Renner-Teller pair) which enables the prediction and assignment of rotational transitions with $K_a$ up to 4. The present set of spectroscopic parameters provides highly accurate, millimeter and submillimeter rest-frequencies of HCCO and DCCO for future astronomical observations. We also show that towards the pre-stellar core L1544, ketenyl peaks in the region where $c$-$\mathrm{C_3H_2}$ peaks, suggesting that HCCO follows a predominant hydrocarbon chemistry, as already proposed by recent gas-grain chemical models.
Professional Presentation and Projected Power: A Case Study of Implicit Gender Information in English CVs

Gender discrimination in hiring is a pertinent and persistent bias in society, and a common motivating example for exploring bias in NLP. However, the manifestation of gendered language in application materials has received limited attention. This paper investigates the framing of skills and background in CVs of self-identified men and women. We introduce a data set of 1.8K authentic, English-language, CVs from the US, covering 16 occupations, allowing us to partially control for the confound occupation-specific gender base rates. We find that (1) women use more verbs evoking impressions of low power; and (2) classifiers capture gender signal even after data balancing and removal of pronouns and named entities, and this holds for both transformer-based and linear classifiers.
PAL: Program-aided Language Models

Large language models (LLMs) have recently demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("few-shot prompting"). Much of this success can be attributed to prompting methods such as"chain-of-thought'', which employ LLMs for both understanding the problem description by decomposing it into steps, as well as solving each step of the problem. While LLMs seem to be adept at this sort of step-by-step decomposition, LLMs often make logical and arithmetic mistakes in the solution part, even when the problem is decomposed correctly. In this paper, we present Program-Aided Language models (PAL): a novel approach that uses the LLM to read natural language problems and generate programs as the intermediate reasoning steps, but offloads the solution step to a runtime such as a Python interpreter. With PAL, decomposing the natural language problem into runnable steps remains the only learning task for the LLM, while solving is delegated to the interpreter. We demonstrate this synergy between a neural LLM and a symbolic interpreter across 13 mathematical, symbolic, and algorithmic reasoning tasks from BIG-Bench Hard and other benchmarks. In all these natural language reasoning tasks, generating code using an LLM and reasoning using a Python interpreter leads to more accurate results than much larger models. For example, PAL using Codex achieves state-of-the-art few-shot accuracy on the GSM8K benchmark of math word problems, surpassing PaLM-540B which uses chain-of-thought by absolute 15% top-1. Our code and data are publicly available at http://reasonwithpal.com/ .