Disappearing Discounts: Hedge Fund Activism in Conglomerates

Hedge fund activism removes the diversification discount in targeted conglomerate firms. Targeted conglomerates increase investment in segments with better growth opportunities, while reducing each division's over-reliance on their own cash flow relative to their reliance on cash flows from other segments. These improvements are stronger when firms are ex-ante financially constrained, when CEOs are subsequently replaced by outsiders, and when payout is subsequently increased. Refocusing is no more valuable than increasing internal efficiency. The results are not driven by mean reversion. The results are consistent with hedge funds' skill in unlocking the value of internal capital markets in diversified firms

ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments

Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autoregressive fashion, unlike most offline trajectory optimization algorithms for a fixed trajectory length. To this end, we first construct the motion library by solving a dense set of trajectory optimization problems for diverse scenarios and parameter settings. Then we learn the motion manifold from the dataset in a supervised learning fashion. We show that the proposed ARMP can generate physically plausible motions for various tasks and situations. We also showcase that our method can be successfully integrated with the recent robot navigation frameworks as a low-level controller and unleash the full capability of legged robots for complex indoor navigation.Comment: Submitted to IRO

Spontaneous Osteoarthritis in Dogs - Clinical Effects of Single and Multiple Intra-articular Injections of Hyaluronic Acid

Background: The treatments of osteoarthritis (OA) are commonly conservative and multimodal to relieve pain and improve movement. Intra-articular injection of hyaluronic acid (IAHA) has been studied as a treatment option for OA in dogs. IAHA helps restore the viscoelasticity of the synovial fluid and relieves the clinical symptoms of OA. However, the efficacy of IAHA in dogs is still a controversial subject. This study aims to confirm the IAHA effect in dogs with spontaneous OA and to compare the effectiveness depending on the number of injections. Materials, Methods & Results: Thirty dogs with spontaneous OA were assigned to a single injection group (n=17) and a 3-weekly injections group (n=13). Dogs weighing less than 10 kg were injected 1 mL of HA to the OA joint, and more than 10 kg dogs were injected 2 mL of HA. In the case of the 3-weekly injections group, the same amount was administered 3 times at 1-week intervals. After the injection, physical and orthopedic examinations were performed to check for complications. Radiographic OA score was evaluated before and 3 months after the injection to confirm and to evaluate the progression of OA. Clinical symptom evaluations were performed on pre-injection, 1-, 2-, and 3-months post-injection. They consisted of the clinical lameness score by veterinarians and Canine Brief Pain Inventory (CBPI) by owners. Results were compared with unpaired t-test, repeated-measures ANOVA with Tukey’s or Sidak’s multiple comparison test, or Wilcoxon test, with P < 0.05. Patients had a median age of 9 years (range 3 to 16 years) and a bodyweight of 4.8 kg (range 2 to 48 kg). No systemic side effects or major complications were detected during the trial period. IAHA produced temporary pain and discomfort in 6 cases. There was no change in the radiographic OA score before and 3 months after injections in both groups, and the difference between groups was not confirmed. In both groups, the clinical lameness score significantly decreased at 1, 2, 3 months after injection compared with pre-injection. The score was lower at 3 months after the injection than at 1 month. The clinical lameness score had no significant difference between the groups. Similarly, CBPI was all decreased in the single injection group and 3-weekly injections group compared to pre-injection, and the score at 3 months post-injection was lower than at 1 month. No significant differences between the groups were found in CBPI. Discussion: Most studies on the efficacy of IAHA in canine OA have been conducted using an experimental model, so studies on spontaneous canine OA are insufficient. This study confirmed that IAHA improves clinical symptoms such as pain relief and movement improvement in spontaneous OA dogs using CBPI and clinical lameness score. In order to confirm the optimal IAHA protocol, a single IAHA and 3-weekly IAHA were compared. The result shows that clinical symptoms improved in both single and 3-weekly injections groups, but no significant difference was confirmed during the 3-month study period. These findings  may suggest that a single IAHA may have a similar effect to multiple IAHA, and repeated injections are unnecessary. In humans and canine OA models, it is reported that the effect of IAHA was maintained for 6 months. This study showed that the effect of IAHA was maintained for 3 months study period and that clinical symptoms improved at 3 months than at 1 month. In conclusion, these findings suggested that IAHA improves clinical symptoms in dogs with spontaneous OA, and a single IAHA showed a similar effect to 3 weekly IAHA. Keywords: canine, treatment, hyaluronic acid, intra-articular injection, osteoarthritis

Learned Token Pruning for Transformers

A major challenge in deploying transformer models is their prohibitive inference cost, which quadratically scales with the input sequence length. This makes it especially difficult to use transformers for processing long sequences. To address this, we present a novel Learned Token Pruning (LTP) method that reduces redundant tokens as the data passes through the different layers of the transformer. In particular, LTP prunes tokens with an attention score below a threshold value, which is learned during training. Importantly, our threshold based method avoids algorithmically expensive operations such as top-k token selection which are used in prior token pruning methods, and also leads to structured pruning. We extensively test the performance of our approach on multiple GLUE tasks and show that our learned threshold based method consistently outperforms the prior state-of-the-art top-k token based method by up to ~2% higher accuracy with the same amount of FLOPs. Furthermore, our preliminary results show up to 1.4x and 1.9x throughput improvement on Tesla T4 GPU and Intel Haswell CPU, respectively, with less than 1% of accuracy drop (and up to 2.1x FLOPs reduction). Our code has been developed in PyTorch and has been open-sourced